HK1194166A - Dynamic data collection - Google Patents

Description

Dynamic data collection

Technical Field

Embodiments of the present invention relate to a method of performing a structured collection protocol on a collection device.

Background

Diseases that last long or are frequently heavily attacked are generally defined as chronic diseases. Known chronic diseases include, inter alia, depression, obsessive-compulsive delusions, alcoholism, asthma, autoimmune diseases (e.g. ulcerative colitis, lupus erythematosus), osteoporosis, cancer and diabetes. Such chronic diseases require long-term care management in order to have effective long-term treatment. One of the functions of long-term care management after initial diagnosis is then to optimize the therapy of the patient's chronic disease.

In the case of diabetes, which is characterized by hyperglycemia due to inadequate insulin secretion, insulin function, or both, it is known that diabetes behaves differently in every human body due to the unique physiology of each individual interacting with different health and lifestyle factors, such as eating habits, weight, stress, illness, sleep, exercise, and medication. Biomarkers are biologically derived indicators of a patient that indicate a biological or pathogenic process, pharmacological response, event, or condition (e.g., aging, disease or illness risk, presence or progression, etc.). For example, a biomarker may be an objective measure of a variable associated with a disease, which may be used as an indicator or predictor of the disease. In the case of diabetes, such biomarkers include values measured for glucose, lipids, triglycerides, and the like. A biomarker may also be a set of parameters from which the presence or risk of a disease can be inferred, rather than a measured value of the disease itself. When properly collected and assessed, biomarkers can provide useful information about a patient's medical problem, and can be used as part of a medical assessment, as medical control, and/or for medical optimization.

For Diabetes, clinicians typically treat diabetic patients according to published treatment guidelines, such as the Joslin Diabetes Center&Of Joslin ClinicClinical Guideline for Pharmacological Management of Type 2 Diabetes(2007) And Joslin Diabetes Center&Of Joslin ClinicClinical Guideline for Adults with Diabetes(2008). The guidelines may specify desired biomarker values, such as fasting blood glucose values of less than 100mg/dl, or a clinician may specify desired biomarker values based on the clinician's training and experience in treating a diabetic patient. Such guidelines do not specify biomarker collection procedures that are adjusted for parameters in order to support a particular therapy for optimizing therapy for a diabetic patient. Subsequently, diabetics often must measure their glucose levels with little collection structure and little consideration of lifestyle factors. Such unstructured collection of glucose levels may result in a lack of interpretative context for some biomarker measurements, thereby reducing the value of such measurements to clinicians and other such healthcare providers that help patients manage their disease.

Different clinicians may require a certain number of collections of patients with chronic disease at various times in order to diagnose the chronic disease or optimize therapy. But these requirements for performing such collections according to the schedule may overlap, repeat, run in reverse of each other, and/or burden the patient such that the patient may avoid any further attempts to diagnose their chronic disease or optimize therapy.

Furthermore, if the requesting clinician does not properly evaluate the patient in order to know whether the requested collection schedule is possible and/or whether the parameters corresponding to the collection are appropriate and/or acceptable for the patient, it is not possible to obtain useful results through such collection. In addition, such a requirement may waste clinician and patient time and effort and consumables used to perform the collection if sufficient appropriate data is not collected to complete the collection required so that the collected data helps address the clinician's medical problems and/or interests. Likewise, such failures can be discouraging to the patient in seeking further therapy advice.

Furthermore, prior art collection devices used to facilitate collection schedules provide limited guidance (if provided) and simple reminders regarding collection events. Such prior art devices typically require manual programming by a clinician or patient in order to manage the collection schedule. Such limited guidance and functionality provided by the prior art may also further discourage patients from seeking any further optimization for their therapy, as the administration of another collection procedure in this manner may be viewed by the patient as cumbersome, thereby simply turning such optimization into guesswork.

Disclosure of Invention

In view of the foregoing background, embodiments of the present invention present a system and method for managing the implementation, execution, data collection, and data analysis of a desired structured collection procedure running on a portable, handheld collection device. Embodiments of the invention may be implemented on a variety of collection devices, such as blood glucose measurement devices (meters) capable of accepting and running thereon one or more collection procedures and associated meter executable scripts in accordance with the invention. In one embodiment, these collection procedures may be generated on a computer or on any device capable of generating collection procedures.

According to one embodiment, a method of executing a structured collection protocol on a collection device comprising a processor and a memory component is provided. The method comprises the following steps: providing a plurality of previous biomarker sample data stored in a memory of a collection device, wherein the previous biomarker samples comprise at least one numerical value based on a measurement of a bodily fluid and a plurality of contextualized data components associated to the previous biomarker samples; setting a first criterion, wherein the first criterion classifies previous biomarker samples as similar if they share at least one same contextualized data component; determining whether previous biomarker samples are similar based on the first criterion; grouping biomarker samples determined to be similar based on a first criterion; calculating an expected value for future biomarker samples that meet a first criterion, wherein the calculation is based on at least a subset of the set of similar previous biomarker samples; setting a second criterion, wherein the second criterion is an acceptable difference from the calculated expected value; collecting one or more biomarker samples that satisfy the first criterion; and assessing, via the processor, compliance of the collected biomarker samples with the second criterion.

According to another embodiment, another method of performing a structured collection protocol on a collection device that includes a processor and a memory component is provided. The method comprises the following steps: providing a plurality of previous biomarker sample data stored in a memory of a collection device, wherein the previous biomarker samples comprise at least one numerical value based on a measurement of a bodily fluid and a plurality of contextualized data components associated to the previous biomarker samples; setting a first criterion, wherein the first criterion classifies previous biomarker samples as similar if they share at least one same contextualized data component; determining whether previous biomarker samples are similar based on the first criterion; grouping biomarker samples determined to be similar based on a first criterion; calculating an expected value for future biomarker samples that meet a first criterion, wherein the calculation is based on at least a subset of the set of similar previous biomarker samples; setting a second criterion, wherein the second criterion is an acceptable difference from the calculated expected value; collecting one or more biomarker samples of a set of samples, the biomarker samples being compliant with the first criterion, wherein the set of samples comprises a predicted number of biomarker samples to be recorded over a collection time period; assessing, via a processor, compliance of the collected biomarker samples with a second criterion; and determining whether an adjustment to the set of samples is required based on the compliance or lack of compliance of the collected biomarker samples with the second criterion, wherein the adjustment comprises recalculating the number of biomarker samples in the set of samples, adjusting the collection frequency of the samples, adjusting the duration of the collection time period, or a combination thereof.

In accordance with another embodiment, a method of executing a structured collection protocol on a collection device including a processor is provided. The method comprises the following steps: providing a plurality of previous biomarker sample data stored in a memory, wherein the previous biomarker samples comprise at least one numerical value based on a measurement of a bodily fluid, whereby the previous biomarker samples are associated to contextualized data; defining the biomarker samples as similar based on a predefined first criterion, whereby the first criterion consists of a comparison of one or more contextualized data of the biomarker samples; tagging, by the processor, the similar biomarker samples; calculating, via a processor, an expected value for a future similar biomarker sample based on the measured values, whereby the calculation is based on at least a subset of similar biomarker samples including more than one previous biomarker sample; setting a second criterion based on the calculated expected value; and configuring a structured collection protocol with the second criteria.

According to another embodiment, a collection device configured to guide a diabetic patient through a structured collection protocol is provided. The collecting device comprises: a meter configured to measure one or more selected biomarkers; a processor disposed inside the meter and coupled to a memory, wherein the memory comprises a collection procedure; and software having instructions that, when executed by a processor, cause the processor to: accessing a plurality of previous biomarker sample data stored in a memory, wherein the previous biomarker samples comprise at least one numerical value based on a measurement of a bodily fluid, whereby the previous biomarker samples are associated to contextualized data; defining the biomarker samples as similar based on a pre-defined or user-defined first criterion, whereby the first criterion consists of a comparison of one or more contextualized data of the biomarker samples; labeling similar biomarker samples; calculating, via a processor, an expected value for a future similar biomarker sample based on the measured values, whereby the calculation is based on at least a subset of similar biomarker samples including more than one previous biomarker sample; setting a second criterion based on the calculated expected value; and configuring a structured collection protocol with the second criteria.

The first criterion may require that the biomarker sample have multiple shared identical contextualized data components. The method may include recalculating the expected values for future biomarker samples. The range may be the predicted variance or standard deviation from the collected biomarker samples. The processor may label similar biomarker samples. The method can comprise the following steps: if the one or more collected biomarker samples do not meet the second criterion, an alarm system in the collection device is triggered. The method can comprise the following steps: if the one or more collected biomarker samples do not meet the second criteria, displaying, via the collection device, the educational course. The method can comprise the following steps: if the one or more collected biomarker samples do not meet the second criterion, the diabetic patient is instructed to collect a new set of samples. The method can comprise the following steps: if the one or more collected biomarker samples do not meet the second criteria, the patient is prompted via the collection device. The calculation of the second criterion may be based on the measured values of previous biomarker samples and pattern recognition of contextualized data. The threshold may define a lower limit, an upper limit, or both. A biomarker sample value below a lower limit may indicate a hypoglycemic event. Alternatively, a biomarker sample value above the upper limit may indicate a hyperglycemic event.

The method may comprise recalculating the predicted number of biomarker samples for a set of samples collected in the future. The method can comprise the following steps: if one or more biomarker samples fail to comply with the second criterion, the number of biomarker samples is increased. The method can comprise the following steps: if one or more biomarker samples fail to comply with the second criterion, the collection frequency of the biomarker samples is increased. The method can comprise the following steps: if the one or more biomarker samples fail to comply with the second criterion, the duration of the collection period is increased. The method can comprise the following steps: the number of biomarker samples is reduced if the biomarker samples comply with the second criterion. The last biomarker sample in the set of samples may be eliminated. At least one or more biomarker samples in the set of samples immediately preceding the last biomarker sample may be eliminated. The method can comprise the following steps: the collection frequency of the biomarker samples is reduced if the biomarker samples comply with the second criterion. The method can comprise the following steps: the duration of the collection period is reduced if the biomarker sampling complies with the second criterion. The predicted number of biomarker samples may include at least one start biomarker sample, at least one end biomarker sample, and at least one intermediate biomarker sample between the start biomarker sample and the end biomarker sample. The start biomarker sampling may be recorded after an event, wherein the event is an action of the diabetic patient to adjust the blood glucose or insulin level of the diabetic patient. The event may be a meal, exercise, insulin administration, or a combination thereof. At least one biomarker sample may be recorded prior to the event. The at least one intermediate biomarker sample may be eliminated if the at least one assessed starting biomarker sample complies with the second criterion. The first start sample may be collected at a time period offset from a set interval between biomarker sample collections within the sample set. The method may include skipping a first start biomarker sampling. The method can comprise the following steps: assessing the second start biomarker sample to determine whether it complies with a second criterion; and instructing the user to collect all intermediate biomarker samples if the second starting biomarker sample fails to comply with the second criterion. The method may comprise reducing the collection frequency of biomarker samples collected after collecting the start biomarker sample. The collection frequency may be reduced for intermediate samples and further reduced for end samples. The collection frequency may be decreased for the intermediate samples and increased for the end samples. The method can comprise the following steps: if there are a sufficient number of collected samples that comply with the first and second criterion, a calculation is performed on the set of samples.

The second criterion may include a threshold, a range, or both. An alarm system may be triggered if one or more future biomarker samples do not meet the second criterion. The user may be prompted if one or more future biomarker samples do not meet the second criterion. The calculation of the second criterion may be based on pattern recognition taking into account the contextualized data of the measured values and/or the previous biomarker samples. The structured collection protocol may comprise a sampling set comprising a predicted number of biomarker samples to be recorded, whereby the processor may recalculate the number of biomarker samples in the sampling set based on the compliance of future biomarker samples with the second criterion.

These and other advantages and features of the invention disclosed herein will become more fully apparent from the following description, the accompanying drawings and the claims.

Drawings

The following detailed description of embodiments of the present invention can be best understood when read in conjunction with the following drawings, where like structure is indicated with like reference numerals.

FIG. 1 is a schematic diagram illustrating a long term care management system for diabetics and clinicians and others interested in long term care management of patients according to one embodiment of the present invention.

Fig. 2, 2A are schematic diagrams illustrating an embodiment of a system suitable for implementing structured collection according to one embodiment of the present invention.

Fig. 3 shows a block diagram of an embodiment of the collecting device according to the invention.

FIG. 4 illustrates one embodiment of a data record in tabular form generated using structured collection on the collection device of FIG. 3 in accordance with the present invention.

FIG. 5A depicts a method of generating a structured collection procedure for medical use cases and/or questions according to one embodiment of the present invention.

Fig. 5B, 5C illustrate defining parameters of a structured collection procedure and factors that may be considered to optimize therapy for a patient using the structured collection procedure, respectively, in accordance with one or more embodiments of the present invention.

Fig. 6A, 6B, 6C, 6D, 6E illustrate various structured collection procedure embodiments defined in accordance with the present invention.

Fig. 7A depicts a structured collection of diagnostic or therapy support for a patient with a chronic disease, according to one embodiment of the invention.

Figure 7B conceptually illustrates one example of a predefined structured collection procedure and a method for customizing the predefined structured collection procedure, in accordance with one embodiment of the present invention.

FIG. 8A illustrates a method for performing a structured collection procedure according to one embodiment of the invention.

Fig. 8B, 8C illustrate a method of implementing a structured collection procedure through a graphical user interface provided on a collection device according to one embodiment of the present invention.

FIG. 9 illustrates a method for performing a structured collection procedure to obtain contextualized biomarker data from a patient, according to another embodiment of the present invention.

FIG. 10A depicts non-contextualized and contextualized data.

FIG. 10B depicts an exemplary collection procedure according to one embodiment of the invention.

FIG. 11 depicts a schematic diagram of acceptable contextualized data being blended with unacceptable contextualized data, according to one embodiment of the invention.

FIG. 12 depicts elements of software according to one embodiment of the invention.

Fig. 13, 14 depict a collection procedure execution method according to one embodiment of the invention.

FIG. 15 illustrates a method of providing diabetes diagnosis and therapy support according to one use example embodiment of the invention.

16, 17, 18 depict different screen shots of a graphical user interface according to one embodiment of the invention.

19A-19D show a flow diagram depicting a structured collection protocol for optimizing insulin titration, in accordance with an embodiment of the present invention.

20A-C are flow diagrams depicting a dynamic structured collection protocol in which sample sets may be dynamically adjusted in accordance with one or more embodiments of the invention.

Detailed Description

The invention will be described with respect to various illustrative embodiments. Those skilled in the art will recognize that the present invention may be implemented in many different applications and embodiments and is not particularly limited in its application to the specific embodiments depicted herein. In particular, the invention will be discussed below in connection with diabetes management by sampling blood, but one of ordinary skill in the art will recognize that the invention can be modified for use with other types of fluids or analytes besides glucose and/or can be used to manage other chronic diseases besides diabetes.

As used herein with respect to the various illustrative embodiments described below, the following terms include (but are not limited to) the following meanings.

The term "biomarker" may mean a physiological variable measured to provide data related to a patient, such as a blood glucose value, a interstitial glucose value, an HbA1c value, a heart rate measurement, a blood pressure measurement, lipids, triglycerides, cholesterol, and the like.

The term "contextualizing" may mean documenting and interrelating conditions that already exist or are about to occur around the collection of a particular biomarker measurement. Preferably, data about documentation and cross-correlation conditions that already exist or are about to occur around the collection of a particular biomarker may be stored with and linked to the collected biomarker data. In particular, further evaluation of the collected biomarker data takes into account data about documentation and cross-correlation conditions, so that not only such data is assessed, but also the links between the data it is contextualized. The data about documentation and cross-correlation conditions may include, for example, information about the time, food, and/or exercise that occurred around and/or concurrently with the collection of specific biomarker measurements. For example, during a test procedure focused on titration optimization, the context of a structured collection procedure according to one embodiment of the invention may be documented by utilizing entry criteria that verify a fasting state of the user prior to accepting the biomarker values.

The term "contextualized biomarker data" may mean information about a cross-correlation condition in which specific biomarker measurements are collected in combination with measured values for the specific biomarker. In particular, the biomarker data is stored together with and linked to information about the cross-correlation condition in which the particular biomarker measurement was collected.

The term "criteria" may mean one or more criteria, and may be at least one or more of guideline(s), rule(s), characteristic(s), and dimension(s) used to determine whether one or more conditions are met or met in order to begin, accept, and/or end one or more procedure steps, actions, and/or values.

The term "adherence" can mean that a person follows the structured collection procedure to properly perform the required procedure steps. For example, biomarker data should be measured under the prescribed conditions of the structured collection procedure. The adherence is then defined as appropriate if the prescribed conditions are given for the biomarker measurements. For example, the specified condition is a time-related condition and/or may illustratively include eating a meal, taking a fasting sample, eating a meal of a certain type within a desired time window, taking a fasting sample at a desired time, taking a minimum amount of sleep, and/or the like. The adherence can be defined as appropriate or inappropriate for a structured collection procedure or a single data point, in particular contextualized biomarker data. Preferably, the adherence can be defined as appropriate or inappropriate by a range of prescribed condition(s) or by selectively determined prescribed condition(s). Furthermore, the adherence can be calculated as an adherence ratio describing how much the adherence is given for a structured collection procedure or a single data point, in particular of contextualized biomarker data.

The term "adherence event" may mean that the person performing the structured collection procedure is not able to perform the procedure steps. For example, if a person does not collect data when required by the collection device, the adherence is determined to be inappropriate, resulting in an adherence event. In another example, the adherence criterion can be a first criterion for a patient fasting of 6 hours and a second criterion for collecting fasting bG values at a desired time. In this example, if the patient provided the bG sample at the requested time but fasting only 3 hours before the provision, the first adherence criterion is not satisfied, although the second adherence criterion is satisfied, and thus an adherence event to the first criterion will occur.

The term "violation event" is a form of an adherence event in which the person performing the structured collection (test) procedure (protocol) does not take the therapeutic at the recommended time, does not take the recommended amount, or both.

The term "adherence criterion" may include adherence and may also mean the basis for comparing (e.g., evaluating) a measured value, a value related to a measured value, and/or a calculated value to a defined value or defined range of values, wherein data is accepted with permission or positive reception based on the comparison. The adherence criterion may in one embodiment take into account time-dependent values and/or adherence, but may also take into account noise, etc. in other embodiments. Furthermore, adherence criteria may be applied to contextualized biomarker data, accepting the biomarker data according to a comparison of contextualized data about documentations and interrelated conditions that exist or occur around the collection of a particular biomarker. The adherence criteria may be similar to a sanity check for a given piece or group of information. In one embodiment, the single data point/information or group of data or information is rejected if the accepted criteria are not met. In particular, such rejected data will not then be used for further calculations that are used to provide therapy recommendations. The rejected data is primarily only used to assess compliance and/or automatically trigger at least one other action. For example, such a triggered action prompts the user to follow a structured collection procedure or a single requested action, such that the adherence criteria can be satisfied based on the action.

The term "data event request" may mean a query for data collection at a single point in space-time defined by a special set of cases, e.g., defined by events that are time-dependent or not time-dependent.

The term "decentralized disease state assessment" may mean a determination of the degree or extent of progression of a disease performed by utilizing biomarker measurements of interest, so as to deliver a numerical value without the need to send a sample to a laboratory for assessment.

The term "medical use case or problem" may mean at least one or more of a procedure, situation, condition, and/or problem that provides uncertainty as to the existence of certainty with respect to some medical fact, and is combined with a concept that has not yet been verified, but that if true, will explain a particular fact or phenomenon. The medical use cases or questions may already be arranged and stored in the system so that the user can choose between different medical use cases or questions. Alternatively, the medical use case or question may be defined by the user himself.

The terms "centralized," "structured," and "sporadic" are used interchangeably herein with the term "test," and may mean a predefined sequence in which a test is performed.

The terms "software" and "program" are used interchangeably herein.

FIG. 1 illustrates a long-term care management system 10 for a diabetic patient(s) 12 and a clinician(s) 14 and others 16 who are interested in the long-term care management of the patient 12. Patients 12 with writing difficulties may include those with metabolic syndrome, pre-diabetes, type 1 diabetes, type 2 diabetes, and gestational diabetes. Others 16 interested in the care of the patient may include family members, friends, support groups, and religious organizations, all of which may affect patient compliance with the therapy. The patient 12 may have access to a patient computer 12, such as a home computer, which may be connected to a public network 50 (wired or wireless), such as the internet, cellular network, etc., and coupled to the dongle, docking station, or device reader 22 for communication with an external portable device, such as the portable collection device 24. An example of a Device Reader is shown in the handbook "Accu-Chek. Smart Pix Device Reader users's Manual" (2008) available from Roche Diagnostics.

The collection device 24 may be essentially any portable electronic device that can be used as an acquisition mechanism to digitally determine and store biomarker value(s) according to a structured collection procedure, and which can be used to run the structured collection procedure and method of the present invention. More details regarding various illustrative embodiments of the structured collection procedure are provided in later sections below. In one embodiment, the collection device 24 may be a self-monitoring blood glucose meter 26 or a continuous glucose monitor 28. One example of a Blood Glucose Meter is an Accu-Chek Active Meter and an Accu-Chek Aviva Meter described in the brochure "Accu-Chek Aviva Blood Glucose Meter Owner's Booklet (2007)", some portions of which are disclosed in U.S. Pat. No. 6,645,368B 1 entitled "method and method of using the Meter for determining the concentration of a component of a fluid", assigned to Roche Diagnostics operations, Inc., which is incorporated herein by reference. An example of a continuous glucose monitor is shown in U.S. Pat. No. 7,389,133 entitled "Method and device for connecting the monitoring of the concentration of an analyte" (2008, 6/17), assigned to Roche Diagnostics Operation, Inc., which is incorporated herein by reference.

In addition to the collection device 24, the patient 12 may use a variety of products to manage his or her diabetes, including: a test strip 30 carried in a vial 32 for use in the collection device 24; software 34 operable on the patient computer 18, the collection device 24, a handheld computing device 36 (such as a laptop computer, personal digital assistant, and/or mobile phone); and a paper tool 38. The software 34 may be preloaded or provided via computer-readable media 40 or provided via public network 50 and loaded for operation in the patient computer 18, collection device 24, clinician computer/office workstation 25, and handheld computing device 36 as desired. In other embodiments, the software 34 may also be integrated into a device reader 22 coupled to a computer (e.g., computer 18 or 25) for operation thereon, or may be accessed remotely, e.g., from a server 52, over a public network 50.

Additional therapy devices 42 and other devices 44 may also be used by the patient 12 for a particular diabetes therapy. In addition, therapy device 42 may include devices such as an ambulatory infusion pump 46, an insulin pen 48, and a lancing device 51. An example of ambulatory injection pumps 46 includes, but is not limited to, Accu-Chek spiral pumps described in the handbook "Accu-Chek spiral Instrument Pump System User Guide" (2007) available from Roche Diabetes Care. Other devices 44 may be medical devices that provide patient data such as blood pressure, health devices that provide patient data such as exercise information, and geriatric care devices that provide notifications to caregivers. The other devices 44 may be configured to communicate with each other according to the standards promulgated by Continua Health Alliance.

Clinicians 14 for diabetes are of a wide variety and may include, for example, nurses, nurse practitioners, physicians, endocrinologists, and other such health care providers. The clinician 14 typically has access to a clinician computer 25, such as a clinician office computer, which may also be provided with software 34. Also available on the computers 18, 25 by the patient 12 and clinician 14 are, for example, Microsoft ® health valult^TMAnd Google^TMHealth care record system 27, such as Health, to exchange information over public network 50 or through other network means (LAN, WAN, VPN, etc.) and store information, such as collected data from collection device 24, into the patient's electronic medical record, such as EMR 53 (fig. 2A) that may be provided to and from computers 18, 25 and/or server 52.

Most patients 12 and clinicians 14 can interact with each other and with others having computers/servers 52 through a public network 50. Such other persons may include the patient's employer 54, third party payers 56 (such as insurance companies that pay some or all of the patient's healthcare costs), pharmacies 58 that dispense certain diabetes consumables, hospitals 60, government agencies 62 (which may also be payers), and companies 64 that provide healthcare products and services for detecting, preventing, diagnosing, and treating diseases. The patient 12 may also permit others (such as an employer 54, a payer 54, a pharmacy 58, a hospital 60, and a government agency 62) to access the patient's electronic health record through a health care recording system 27, which health care recording system 27 may reside on the clinician computer 25 and/or one or more servers 52. Reference will also be made to fig. 2 hereinafter.

Fig. 2 illustrates one embodiment of a system suitable for implementing structured collection according to one embodiment of the present invention, which in another embodiment may be part of the chronic care management system 10, and communicates with such components via conventional wired or wireless communication means. The system 41 may include a clinician computer 25 in communication with the server 52 and the collection device 24. Communication between the clinician computer 25 and the server 52 may be facilitated by a communication link to the public network 50, to the private network 66, or a combination of both. The private network 66 may be a local or wide area network (wired or wireless) connected to the public network 50 through network devices 68 such as (web) servers, routers, modems, hubs, and the like.

In one embodiment, the server 52 may be a central repository for a plurality of structured collection procedures (or protocols) 70a, 70b, 70c, 70d, with several details of exemplary structured collection procedures being provided in later sections. The server 52 and the network device 68 may also act as a data aggregator for several of the completed structured collection procedures 70a, 70b, 70c, 70 d. Accordingly, in such an embodiment, data from the collection device(s) of the patient 12 that has completed the collection procedure may then be provided from the server 52 and/or the network device 68 to the clinician computer 25 when required in response to the acquisition of patient data.

In one embodiment, one or more of the plurality of structured collection procedures 70a, 70b, 70c, 70d on the server 52 may be provided over the public network 50, such as through a secure web interface 55 implemented on the patient computer 18, the clinician computer 25, and/or the collection device 24 (FIG. 2A, showing another embodiment of the system 41). In another embodiment, the clinician computer 25 may act as an interface (wired or wireless) 72 between the server 52 and the collection device 24. In another embodiment, the structured collection procedures 70a, 70b, 70c, 70d and software 34 may be provided on the computer readable medium 40 and loaded directly onto the patient computer 18, the clinician computer 25 and/or the collection device 24. In another embodiment, the structured collection procedure 70a, 70b, 70c, 70d may be provided pre-loaded (embedded) in the memory of the collection device 24. In other embodiments, the new/updated/modified structured collection procedures 70a, 70b, 70c, 70d may be sent between the patient computer 18, the clinician computer 25, the server 52, and/or the collection device 24 over the public network 50, the private network 66, over a direct device connection (wired or wireless) 74, or a combination thereof. Accordingly, in one embodiment, external devices, such as computers 18 and 25, may be used to establish communication links 72, 74 between the collection device 24 and other electronic devices, such as other remote Personal Computers (PCs) and/or servers, such as over the public network 50, such as the internet, and/or other communication networks, such as the private network 66 (e.g., LAN, WAN, VPN, etc.).

As a conventional personal computer/workstation, the clinician computer 25 may include a processor 76 that executes programs, such as the software 34, as well as programs, such as from memory 78 and/or the computer readable medium 40. The memory 78 may include system memory (RAM, ROM, EEPROM, etc.) and storage memory, such as a hard drive and/or flash memory (internal or external). The clinician computer 25 may also include a display driver 80 to interface a display 82 with the processor 76, an input/output connection 84 for connecting user interface devices 86, such as a keyboard and mouse (wired or wireless), and a computer readable drive 88 for portable memory and disks, such as the computer readable medium 40. The clinician computer 25 may also include a communication interface 90 for connecting to the public network 50 and other devices, such as the collection device 24 (wired or wireless), and a bus interface 92 for connecting the aforementioned electronic components to the processor 76. Reference will now be made to fig. 3 in the following.

Fig. 3 is a block diagram conceptually illustrating the portable collection device 24 depicted in fig. 2. In the illustrated embodiment, the collection device 24 may include one or more microprocessors, such as processor 102, which may be a central processing unit including at least one more single or multiple cores and a cache memory, which may be connected to a bus 104, which may include data, memory, control, and/or address buses. The collection device 24 may include software 34 that provides instruction code that causes the processor 102 of the device to implement the methods of the present invention, which will be discussed in later sections. The collection device 24 may include a display interface 106 that provides graphics, text, and other data from the bus 104 (or from a frame buffer not shown) for display on a display 108. The display interface 106 may be a display driver of an integrated graphics solution that utilizes a portion of the main memory 110 of the harvesting device 24, such as Random Access Memory (RAM), and processes from the processor 102, or may be a dedicated graphics processing unit. In another embodiment, the display interface 106 and the display 108 may additionally provide a touch screen interface to provide data to the collection device 24 in a well known manner.

The main memory 110 may be Random Access Memory (RAM) in one embodiment, and may include other memory such as ROM, PROM, EPROM or EEPROM, and combinations thereof, in other embodiments. In one embodiment, collection device 24 may include secondary memory 112, which may include, for example, a hard disk drive 114 and/or a computer-readable media drive 116 for computer-readable media 40, representing, for example, at least one of a floppy disk drive, a magnetic tape drive, an optical disk drive, a flash memory connector (e.g., a USB connector, a Firewire connector PC card slot), and so forth. The drive 116 reads from and/or writes to the computer-readable media 40 in a well-known manner. Computer-readable medium 40 represents a floppy disk, magnetic tape, compact disk (CD or DVD), flash drive, PC card, or the like, which is read by and written to by drive 116. It should be appreciated that the computer-readable medium 40 may have stored therein the software 34 and/or the structured collection procedures 70a, 70b, 70c, and 70d and data resulting from completed collections performed according to one or more of the collection procedures 70a, 70b, 70c, and 70 d.

In alternative embodiments, the secondary memory 112 may include other means for allowing the software 34, collection procedures 70a, 70b, 70c, 70d, other computer programs, or other instructions to be loaded into the collection device 24. Such means may include, for example, a removable storage unit 120 and an interface connector 122. Examples of such removable storage units/interfaces may include a program cartridge and cartridge interface, a removable memory chip (e.g., ROM, PROM, EPROM, EEPROM, etc.) and associated socket, and other removable storage units 120 (e.g., hard disk drive) and interface connector 122 that allow software and data to be transferred from the removable storage unit 120 to collection device 24.

In one embodiment, the collection device 24 may include a communication module 124. The communication module 124 allows software (e.g., the software 34, the collection procedures 70a, 70b, 70c, and 70 d) and data (e.g., data resulting from completed collections performed according to one or more of the collection procedures 70a, 70b, 70c, and 70 d) to be transferred between the collection device 24 and the external device(s) 126. Examples of communication module 124 may include one or more of the following: a modem, a network interface (such as an ethernet card), a communications port (e.g., USB, Firewire, serial, parallel, etc.), a PC or PCMCIA slot and card, a wireless transceiver, and combinations thereof. The external device(s) 126 may be a patient computer 18, a clinician computer 25, a handheld computing device 36 such as a laptop computer, a Personal Digital Assistant (PDA), a mobile (cellular) phone, and/or a dongle, a docking station, or a device reader 22. In such an embodiment, the external device 126 may provide and/or connect to one or more of a modem, a network interface (such as an Ethernet card), a communications port (e.g., USB, Firewire, serial, parallel, etc.), a PCMCIA slot and card, a wireless transceiver, and combinations thereof, to provide communications, such as with the clinician computer 25 or server 52, over the public network 50 or private network 66. Software and data transferred through the communication module 124 may be in the form of wired or wireless signals 128, which may be electronic, electromagnetic, optical, or other signals capable of being sent and received by the communication module 124. For example, it is known that signals 128 can be transmitted between communication module 124 and external device(s) 126 using wire or cable, fiber optics, a phone line, a cellular phone link, an RF link, an infrared link, other communication channels, and combinations thereof. Specific techniques for connecting electronic devices via wired and/or wireless connections (e.g., via USB and Bluetooth, respectively) are known in the art.

In another embodiment, the collection device 24 may be used with an external device 132, such as provided as a handheld computer or mobile phone, to perform actions such as prompting the patient to take an action, acquiring a data event, and performing calculations regarding information. One example of a collection device provided as a handheld computer in combination with such an external device 126 is disclosed in U.S. patent application No. 11/424,757 entitled "System and method for collecting information from multiple floors therapy by means of a specified" filed on 16.6.2006, assigned to Roche Diagnostics Operations limited and incorporated herein by reference. Another example of a handheld computer is shown in the User Guide entitled "Accu-Chek Pocket Assembly Software with Board calls User Guide" (2007), available from Roche Diagnostics.

In the illustrated embodiment, the collection device 24 may provide a measurement engine 138 for reading a biosensor 140. The biosensor 140, which in one embodiment is a disposable test strip 30 (fig. 1), is used with the collection device 24 to receive a sample, e.g., capillary blood, which is exposed to an enzymatic reaction and measured by the measurement engine 138 by electrochemical techniques, optical techniques, or both, in order to measure and provide a biomarker value, e.g., blood glucose level. One example of a disposable test strip and measurement engine is disclosed in U.S. patent publication No. 2005/0016844 a1 "Reagent strips for test strips" (1/27/2005), which is assigned to Roche Diagnostics Operations limited and incorporated herein by reference. In other embodiments, the measurement engine 138 and biosensor 140 may be of a type used to provide biomarker values for other types of sampled fluids or analytes besides glucose, heart rate, blood pressure measurements, and combinations thereof. Such an alternative embodiment may be used for embodiments in which values from more than one biomarker type are required by the structured collection procedure according to the present invention. In another embodiment, biosensor 140 may be a sensor with indwelling catheter(s) or a subcutaneous tissue fluid sampling device(s), such as when collection device 24 is implemented as a Continuous Glucose Monitor (CGM) in communication with an infusion device, such as pump 46 (fig. 1). In other embodiments, collection device 24 may be a controller that implements software 34 and communicates between the injection device (e.g., ambulatory insulin pump 46 and electronic insulin pen 48) and biosensor 140.

Data, including at least information collected by the biosensor 140, is provided to the processor 102 by the measurement engine 138, and the processor 102 may execute computer programs stored in the memory 110 to perform various calculations and processes using the data. By way Of example, such a Computer Program is described in U.S. patent application No. 12/492,667 entitled "Method, System, and Computer Program Product for Providing book Of Estimated True Mean Blood Glucose Value and Estimated Glucose (HbA1C) Value for Structured distances Of Blood Glucose", filed on 26.6.2009, assigned to Roche Diagnostics Operations, Inc. and incorporated herein by reference. The data from measurement engine 138 and the results of the calculations and processing performed by processor 102 using the data are referred to herein as self-monitored data. The self-monitoring data may include, but is not limited to, glucose values, insulin dosage values, insulin types of the patient 12 and parameter values used by the processor 102 to calculate future glucose values, supplemental insulin dosages, and carbohydrate supplements, as well as such values, dosages, and amounts. Such data is stored in the data file 145 of the memory 110 and/or 112 along with a date time stamp 169 for each measured glucose value and administered insulin dosage value. The internal clock 144 of the collection device 24 may provide the current date and time to the processor 102 for such use.

The collection device 24 may also provide a user interface 146, such as buttons, keys, trackballs, trackpads, touch screens, and the like, for data entry, program control and navigation of options, selections and data, making information requests, and the like. In one embodiment, the user interface 146 may include one or more buttons 147, 149 for providing input and navigation of data in the memory 110 and/or 112. In one embodiment, the user may use one or more buttons 147, 149 to enter (document) contextualized information, such as data related to the daily lifestyle of the patient 12, and confirm completion of prescribed tasks. Such lifestyle data can relate to food intake, drug use, energy levels, exercise, sleep, general health, and overall well-being of the patient 12 (e.g., happy, sad, calm, stressed, tired, etc.). Such lifestyle data may be recorded into the memory 110 and/or 112 of the collection device 24 as part of the self-monitored data by navigating through a menu of options displayed on the display 108 using the buttons 147, 149 and/or through a touch screen user interface provided by the display 108. It should be appreciated that the user interface 146 may also be used to display self-monitored data or a portion thereof on the display 108, such as used by the processor 102 to display measured glucose levels and any entered data.

In one embodiment, the collection device 24 may be turned on by pressing any one or any combination of the buttons 147, 149. In another embodiment, the biosensor 140 is a test strip, and the collection device 24 may be automatically turned on when the test strip is inserted into the collection device 24 for measurement of the glucose level of a blood sample placed on the test strip by the measurement engine 138. In one embodiment, the collection device 24 may be turned off by holding down one of the buttons 147, 149 for a predefined period of time, or in another embodiment, the collection device 24 may be automatically turned off after not using the user interface 146 for a predefined period of time.

An indicator 148 may also be connected to the processor 102 and may operate under the control of the processor 102 to issue audible, tactile (vibration), and/or visual alerts/reminders to the patient regarding bG measurements and the daily times of the event (such as meals), possible future hypoglycemia, and so forth. The collection device 24 is also provided with a suitable power source 150, as is known, to make the device portable.

As previously described, the collection device 24 may be pre-loaded with the software 34 or provided via the computer-readable medium 40 and receive the signal 128 directly via the communication module 124 or indirectly via the external device 132 and/or the network 50. When provided in the latter manner, the software 34 is stored in the main memory 110 (as shown) and/or the secondary memory 112 as it is received by the processor 102 of the collection device 24. The software 34 contains instructions that, when executed by the processor 102, enable the processor to perform the features/functions of the present invention, as discussed in later sections herein. In another embodiment, the software 34 may be stored in the computer-readable medium 40 and loaded into cache memory by the processor 102, thereby causing the processor 102 to perform the features/functions of the present invention as described herein. In another embodiment, the software 34 is implemented primarily in hardware logic, e.g., using hardware components such as Application Specific Integrated Circuits (ASICs). Implementing a hardware state machine to perform the various features/functions described herein will be apparent to those skilled in the relevant art(s). In another embodiment, the invention is implemented using a combination of both hardware and software.

In an exemplary software embodiment of the invention, the methods described hereinafter may be implemented using the C + + programming language, but may be implemented with other programs, such as (but not limited to) Visual Basic, C, C #, Java, or other programs available to those skilled in the art. In other embodiments, program 34 may be implemented using a scripting language or other proprietary interpretable language used in conjunction with an interpreter. Reference will also be made to fig. 4 below.

Fig. 4 depicts, in tabular form, a data file 145 containing data records 152 of self-monitoring data 154 resulting from a structured collection procedure in accordance with one embodiment of the present invention. The data records 152 (e.g., rows) along with the self-monitored data 154 (e.g., columns in some columns) may also provide context information 156 (e.g., other columns in some columns and by row and column header information) associated therewith. Such contextual information 156 may be collected automatically during the structured collection procedure, such as by input received automatically from any of the measurement engine, the biosensor, and/or any other device, or may be collected by manual input received from the user interface made by the patient in response to collection requirements (e.g., requirements displayed by the processor 102 on the display 108). Accordingly, since such contextual information 156 may be provided with each data record 152 in one embodiment, such information may be readily available to the physician, and no further collection of such information is necessary after completion of the structured collection procedure, and thus need not be provided again by the patient, either manually or orally. In another embodiment, if such contextual information 156 and/or additional contextual information is collected after completion of a structured collection procedure according to the present invention, such information may be provided at a later time, for example, by one of the computers 18, 25 in the associated data file 145 and/or record 152. Such information may then be associated with the self-monitoring data in the data file 145 and thus will not need to be provided again, either orally or manually. The processing in the latter embodiment may be required in the following cases: the structured collection procedure is implemented, or partially implemented, as a paper tool 38 for use with a collection device that is not capable of running the software 34 that implements the structured collection procedure.

It should be appreciated that the data file 145 (or a portion thereof, such as the self-monitored data 154 alone) may be transmitted/downloaded (wired or wireless) from the collection device 24 to another electronic device, such as the external device 132 (PC, PDA, or cell phone), or transmitted/downloaded to the clinician computer 25 via the network 50 via the communication module 124. The clinician may use the diabetes software provided on the clinician computer 25 to assess the received self-monitored data 154 of the patient 12 as well as the contextual information 156 to obtain a therapy result. One example of some of the functions that may be incorporated into Diabetes software and configured FOR a personal computer is Accu-Chek 360 Diabetes Management System available from Roche Diagnostics, which is disclosed in U.S. patent application No. 11/999,968 entitled "METHOD AND SYSTEM FOR SETTING TIME BLOCK", filed 12, 7, 2007 and assigned to Roche Diagnostics Operations, Inc. and incorporated herein by reference.

In one embodiment, the collection device 24 may be provided as a portable blood glucose meter that is used by the patient 12 to record self-monitoring data including insulin dose readings and field measured glucose levels. Examples of such bG meters mentioned previously include, but are not limited to, Accu-Chek Active meters and Accu-Chek Aviva systems, made by Roche Diagnostics Inc. all of them ^。The diabetes management Software is compatible for downloading test results to a personal computer, or is compatible with Accu-Chek pod Compass Software for downloading and communicating with the PDA. Accordingly, it should be appreciated that the collection device 24 may include the software and hardware necessary to process, analyze and interpret the self-monitored data and generate an appropriate data interpretation output according to a predefined flow sequence (as described in detail below). In one embodiment, the results of the data analysis and interpretation performed by the collection device 24 on the stored patient data may be displayed in the form of reports, trend monitoring graphs, and charts to assist the patient in managing their physiological condition and to support patient-physician communication. In other embodiments, the bG data from the collection device 24 can be used to generate reports (in hard copy or electronic form) by the external device 132 and/or the patient computer 18 and/or the clinician computer 25.

The collection device 24 may also provide the user and/or his or her clinician with information including at least one or more of: a) editing data descriptions, such as titles and descriptions of records; b) storing the record at a specified location, in particular in a user-definable directory as described above; c) retrieving the record for display; d) searching for records according to different criteria (date, time, title, description, etc.); e) categorizing the records according to different criteria (values of bG levels, dates, times, durations, titles, descriptions, etc.); f) deleting the record; g) exporting the record; and/or h) performing data comparisons, modifying records, and excluding records as is known.

Lifestyle, as used herein, may be generally described as a pattern of personal habits, such as meals, exercise, and work schedules. The individual may additionally be taking medication, such as insulin therapy or oral medication that they are required to take in a periodic manner. The present invention implicitly takes into account the effect of this action on glucose.

It should be appreciated that the processor 102 of the collection device 24 may implement one or more structured collection procedures 70 provided in memory 110 and/or 112. In one embodiment, each structured collection procedure 70 may be stand-alone software, providing the necessary program instructions that, when executed by the processor 102, cause the processor to perform the structured collection procedure 70 as well as other prescribed functions. In other embodiments, each structured collection procedure 70 may be part of the software 34, and may then be selectively executed by the processor 102, in one embodiment by receiving a selection from the user interface 146 from a menu list provided in the display 108, or in another embodiment by activation of a particular user interface, such as a structured collection procedure run mode button (not shown) provided to the collection device 24. It should be appreciated that the software 34 likewise provides the necessary program instructions that, when executed by the processor 102, cause the processor to perform the structured collection procedure 70 as well as the other prescribed functions of the software 34 discussed herein. A suitable example of An alternative structured collection procedure With An alternative schema provided as a collection instrument is disclosed in U.S. patent application No. 12/491,523 entitled "independent Blood Glucose Monitoring System With An alternative Graphical User Interface And Methods Thereof," filed on 25.6.2009, assigned to Roche Diagnostics Operations, Inc. And incorporated herein by reference.

In another embodiment, command instructions may be sent from the clinician computer 25 and received by the processor 102 through the communication module 124 that place the collection device 24 in a collection mode that automatically runs the structured collection procedure 70. Such command instructions may specify which of the one or more structured collection procedures is to be run and/or provide the structured collection procedure to be run. In another embodiment, a list of defined medical use cases or medical issues may be presented by the processor 102 on the display 108, and a particular structured collection procedure 70 may be automatically selected by the processor 102 from among a plurality of structured collection procedures (e.g., procedures 70a, 70b, 70c, and 70 d) depending on the selection of a defined medical use case or medical issue received by the processor 102 through the user interface 146.

In another embodiment, after selection, the structured collection procedure(s) 70 may be provided via a computer readable medium (e.g., 40) and loaded by collection device 24, downloaded from computer 18 or 25, other device(s) 132, or server 52. The server 52 may be, for example, a healthcare provider or company that provides such a predefined structured collection procedure 70 for download in accordance with selected defined medical use cases or issues. It should be appreciated that the structured collection procedure(s) 70 may be developed by a healthcare company (e.g., company 64) and implemented via a web page over the public network 50 and/or made available for download on the server 52, such as shown in FIG. 2. In other embodiments, the notification that a new structured collection procedure 70 is available for the collection device 24 to help resolve a particular use case/medical issue that a user (e.g., healthcare provider and patient) may have may be provided in any standard manner, such as by postal letters/cards, email, text message, guest, etc.

In some embodiments, as previously mentioned, the paper tool 38 may perform some of the functions provided by the diabetes software 34. One example of some of the functionality that may be incorporated into the diabetes software 34 and configured as paper tools 38 is Accu-Chek 360 View Blood Glucose Analysis System (Accu-Chek 360 View Blood Glucose Analysis System) paper available from Roche Diagnostics, also disclosed in U.S. patent application No. 12/040,458 entitled "Device and method for assembling Blood Glucose control", filed on 29.2.2007, assigned to Roche Diagnostics Operations, Inc. and incorporated herein by reference.

In another embodiment, software 34 may be implemented on continuous glucose monitor 28 (FIG. 1). In this manner, continuous glucose monitor 28 may be used to obtain time resolved data. Such time resolved data can be used to identify fluctuations and trends that might not otherwise be noticed for on-site monitoring of blood glucose levels and standard HbA1c testing. For example, nighttime low glucose levels, high blood glucose levels between meals, early morning spikes in blood glucose levels, and how eating habits and physical activity can affect blood glucose and the effect of therapy changes.

In addition to the collection device 24 and software 34, the clinician 14 may prescribe other diabetes therapy devices for the patient 12, such as an ambulatory insulin pump 46 and an electronically-based insulin pen 48 (FIG. 1). The Insulin Pump 46 typically comprises Configuration Software such as disclosed in the handbook "Accu-Chek in Lamp Configuration Software" also available from Disetronic Medical Systems AG. The insulin pump 46, as well as the electronically-based insulin pen 48, can record and provide insulin dosage and other information to the computer, and thus can be used as another means of providing the biomarker data required by the structured collection procedure 70 (fig. 2) according to the present invention.

It should be recognized and as previously mentioned that one or more of the method steps discussed below may be configured as paper tool 38 (fig. 1), but preferably all of the method steps are carried out electronically on system 41 (fig. 2) or any electronic device/computer, such as collection device 24, having a processor and memory, and having program(s) resident in the memory. It is known that when a computer executes a program, the instruction codes of said program cause the processor of the computer to carry out the method steps associated therewith. In other embodiments, some or all of the method steps discussed below may be configured on a computer readable medium 40 storing instruction code for a program that, when executed by a computer, may cause the processor of the computer to perform the method steps associated therewith. These method steps will be discussed in more detail below with reference to fig. 5A and 5B.

Creating a structured collection procedure。

Fig. 5A depicts a method 200 of creating the structured collection procedure 70 shown in fig. 5B for a medical use case or question, which may be implemented in any of the previously described devices 18, 24, 25, 26, 28, 36, 52 as standalone software, as part of the diabetes software 34, or some portion thereof as part of the paper tool 38. In step 202, a medical use case or issue, generally referred to hereinafter as use case(s), is selected and/or may be defined. It should be appreciated that the use case may be, for example, one selected from among the following medical use cases or questions: it is desirable to know the effect of eating a particular food; it is desirable to know the optimal time to take the drug before and/or after a meal; and hopefully the effect of exercise on bG levels. Other use cases may be issues involving: find a diagnosis, how best to initialize therapy for a patient, find a determination of the state of disease progression for a patient, find the best way to optimize patient therapy, etc. Other examples may provide a structured collection procedure 70 that may be used to help resolve information regarding fasting glucose, pre-meal glucose values, post-meal glucose values, and the like. Other medical issues may be to control the biomarkers within predefined contexts to optimize the biomarkers within predefined contexts, with therapy initiation, therapy type, oral monotherapy, oral combination therapy, insulin therapy, lifestyle therapy, adherence to therapy, therapy efficacy, insulin infusion or inhalation, insulin type, split insulin into basal and bolus (bolus), and the like. For example, medical problems with oral monotherapy and oral combination therapy may include problems involving: sulfonylureas, biguanides, thiazolidinediones, alpha-glucosidase inhibitors, meglitinide, dipeptidyl peptidase IV inhibitors, GLP-1 analogs, taslutamide, PPAR bis alpha/gamma agonists, and aleglitazar. The selected use case may be assigned to the medical use case parameter 220 depicted in fig. 5B.

In step 204, a situation or problem surrounding the selected use case may be defined. This can be done by looking at all factors that may affect the change in the use case. For example, in a use case where it is desirable to know how to best optimize patient therapy, some factors to be viewed may include stress, menstrual cycle, pre-dawn effects, background insulin, exercise, bolus timing with meals, basal rate, insulin sensitivity, postprandial behavior, and so forth, as shown in fig. 5C.

In step 206, a determination may be made as to what kind of analysis can be used to solve or clarify the situation or problem. Such an analysis may for example be selected from among: assessing changes in fasting glucose (FPG) values during the course of the collection procedure 70, monitoring one or more specific values for the duration of the collection procedure 70, determining the ratio of insulin to carbohydrate (I: C), determining insulin sensitivity, determining an optimal time for taking another variable such as meal(s), and the like. In step 208, a sample set determination may be made as to which information must be collected, such as what biomarker(s) and context(s) in which the biomarkers should be collected, and when this information needs to be collected to conduct the analysis. For example, the sample group may be defined as a string of data objects, wherein each data object comprises: target types, such as time-based types, which may use a target time (e.g., used for alert features), time window lower bounds, time window upper bounds, etc., or data-based types, which define data types (individual, aggregate, or formula), conditions under which data is accepted (e.g., unconditional, below a certain value, above a certain value, a certain formula, etc.), collection types (e.g., user input, sensors, data, etc.), and any reminder screen text collected for each item (e.g., static and/or dynamic in terms of both formatting and value insertion). The result of this process is a schedule of collection events 222 (FIG. 5B). Next in step 210, the manner in which each or a set of collection schedule events 222 are to be implemented is then determined so as to be available to address the situation or problem of the selected use case. This may result in one or more adherence criteria 224. Additionally and/or alternatively to the manner in which collection is performed, the adherence criterion(s) 224 can also be based on one or more biomarker values falling within a predefined range or being equal to a particular predefined value. In other embodiments, the adherence criterion(s) can be formula(s) that use biomarker data or such data sets to determine whether the resulting value falls within a predefined range or is equal to a particular predefined value.

For example, the adherence criteria 224 can describe parameters surrounding an event 237 that the patient 12 needs to perform, such as a test within a particular window, fasting for a given amount of time, sleep for a given amount of time, exercise, low pressure, not in menstruation, and so forth. Thus, the adherence criteria 224 may establish the context of the information to be provided. The adherence criteria 224 may also be used in another context as previously mentioned to provide an assessment as to whether the data is acceptable, and when used in such a context may be referred to as "acceptance" criteria. For example, prior to taking a sample, the adherence criteria 224 can establish whether various steps leading to taking the sample are achieved. For example, processor 102 displays a question in response to request 240, "do you empty in the last 8 hours? "where a" yes "response received by the processor through the user interface 146 satisfies the adherence criteria 224 for that step. In another example, after taking the sample, the processor 102 may utilize other adherence (or acceptance) criterion(s) to assess the reasonableness of the received data. For example, based on previous data, a fasting bG sample should be between 120-180mg/dl, but the received value is 340mg/dl, and thus such adherence (acceptance) criteria are not met because they fall outside of the predefined range for acceptable values. In such an example, an adherence event 242 occurs, wherein the processor 102 may give a prompt for additional samples. In this case, if the resampling also fails (i.e., not between 120-180 mg/dl), then the assessment provided by the processor 102 is that the patient 12 is not fasting, and thus after the resampling fails, the processor 102 automatically extends the events 237 in the event schedule 222 accordingly as indicated by the adherence criteria.

Next in step 212, the condition(s) and context(s) under which the schedule of events 222 should begin and end may be determined. This results in one or more entry criteria 226 and exit criteria 228 being provided for the schedule of events 222, and possibly for a set of other schedules of events to which the schedule of events 222 belongs, in the case of providing a blanket structured collection procedure (e.g., procedures 70a, 70b, 70c, and 70 d) that may be run simultaneously and/or sequentially one after another.

For example, the one or more entry criteria 226 may be used to determine whether the patient satisfies the conditions for use of the collection procedure by the processor 102, where the patient 12 is checked for satisfaction of the entry criteria 226 based on, for example, the current age being within a range, the HbA1c being within a range, the patient is checked for the presence of a particular disease, the patient has been checked for the presence of the disease, has been suffering from the disease for a minimum period of time, has a body mass index within a range, has Fasting Plasma Glucose (FPG) within a range, has a particular drug sensitivity, is taking a particular drug dose, satisfies one or more prerequisites of another structured collection procedure, has completed one or more other structured collection procedures, does not have one or more particular preconditions (e.g., pregnancy, non-fasting, or contraindications, such as paresthesia, Fever, vomiting, etc.), and combinations thereof. The entry criteria 226 may also initiate the schedule of events 222 by initiating events such as time of day, time of week, meal at time offset, exercise, and exercise at time offset, use of therapy medication at time offset, physiological condition, biomarker range, and biomarkers within a predetermined range of offset calculated from previous biomarker values. An example of a physiological condition may be when a predetermined number of physiological events (e.g., hyperglycemia, hypoglycemia, a particular temperature at a particular time of day, etc.) occur within a predefined amount of time (e.g., hours, days, weeks, etc.), then the entry criteria are met to begin the structured collection procedure. Accordingly, the entry criteria may be used to support the need for usage to meet a prerequisite, an indication of usage, and/or contraindications for usage. For example, the entry criteria 226 may define a prerequisite where, in order for the structured collection procedure 70 to run an insulin sensitivity optimization, the processor 102 must first verify that the structured collection procedure for the basal titration is completed and/or has the desired result, and/or that another structured collection procedure for the ratio of insulin to carbohydrate is also completed and/or has the desired result. In another example, the entry criteria 226 may define a condition that requires satisfaction of a particular use indication, where a particular structured collection procedure may provide for separate uses for type 1 versus type 2 diabetics as well as various types of structured collection procedures that may be used to titrate a particular drug. In another example, the entry criteria 226 may define a condition that requires satisfaction of a particular use contraindication, wherein a particular structured collection procedure 70 will not be run, for example, if the patient 12 is pregnant, ill, etc.

An example of one or more exit criteria 228 may be based on a determination by the processor 102 that a particular value has been reached, the average of the primary sample values being within a certain range, (one or more of which are based on the average value of the primary sample values)A plurality) of specific events and/or condition(s) have or have not occurred, as well as combinations thereof. Other conditions under which the protocol may stop may include adverse events, such as hypoglycemic events, the patient suffering from a disease, the patient undergoing a change in therapy, and so forth. Additional details may also be provided by the processor 102 to the patient 12 on the display 108 based on what specific exit criteria are met. For example, in one example, if patient 12 measures a glucose value indicating hypoglycemia, after exiting the protocol, processor 102 automatically executes another alternative protocol that instructs patient 12 to ingest carbohydrates and measure his blood glucose value every half hour until blood glucose exceeds 120 mg/dL. For this alternative procedure, the processor 102 may also ask the patient 12 to document his meals, activities, stress, and other relevant details in order to ensure that conditions leading to hypoglycemia are recorded. The patient 12 may also be instructed by the processor 102 to contact the clinician 14 in that situation and in other such special situations as deemed appropriate. Exit criteria may also include, for example, criteria for an end, such as exiting after successful completion, or exiting after an indeterminate completion, such as expiration of a predetermined timeout time (logistics end), for example, at nAfter a day there were no results, whereinnOr 1 to 365 days, or by termination, e.g., due to unsuccessful termination by a fail-safe device. It should be appreciated that the structured collection procedure 70 may also be defined to automatically end not only based on satisfaction of the exit criteria 228, but also when the patient 12 fails to make a request for an acceptable level of compliance and/or when the physiological state of the patient has changed such that the patient should not implement the schedule of events 222 (and thus the adherence criteria 224 is not satisfied), wherein the adherence event 242 is about to end the structured collection procedure.

In step 214, guidance 230 for the user during collection and any options 232 for customizing the collection may be determined. For example, for guidance 230, the clinician 14 may use a default list of messages or adjust messages to guide the patient 12 during execution of the collection procedure 70. As an example, a message may be provided upon a successful data acquisition (i.e., adherence criteria 224 is met) that will read "thank you. Your next scheduling measurement is at 1230 pm. "an alert, such as provided by the indicator 148, may also be associated with the collection procedure 70, which alerts the patient 12 to take a measurement, and may include a time delay (snooze) function if the patient 12 requires additional time to perform the measurement. The delay function and other device features will be discussed further in later sections.

The result of step 208-. In one embodiment, in generating the collection procedure 70, the clinician 14 also generates printed material that explains to the patient (at least) the following: the purpose of the collection procedure 70 and the desired result to be expected, i.e., setting a target for the collection procedure 70; collection protocol 70 design and number of measurements required; entry criteria 226 that the patient 12 must meet before initiating the collection procedure 70 and before taking each reading; and exit criteria 228 such that the patient 12 should stop continuing the collection procedure 70. Such printed material, along with guidance 230 that may be provided during execution of the collection procedure 70, ensures that the patient is fully aware of what the data collection procedure is being performed for.

Examples of structured collection procedures 70 may be, for example, structured collection procedures for determining insulin to carbohydrate ratios, for determining bolus timing for meal initiation, and for determining exercise equivalent to ingested carbohydrate. In step 218, the structured collection procedure 70 is then made available for implementation and use in the system 41, for example, in the manner discussed above with respect to any of fig. 1, 2, and 3. The structured collection procedure 70 may accordingly be provided through a previous process, such as by a medical facility or healthcare company 64, to assist the clinician 14 in resolving and/or studying defined medical use cases or issues.

FIG. 5B illustrates the interaction of the parameters 222, 224, 226 and 228 of the structured collection procedure 70 for obtaining contextualized biomarker data from a diabetic patient in order to address an example of medical use that underlies the structured collection procedure. As previously described, a use case parameter 220 may be provided to identify the medical use case or problem that parameters 222, 224, 226, and 228 address. For example, the processor 76 of the clinician computer 25, the processor 102 of the collection device 24, and/or the server 52 may read medical use case parameters 220 (fig. 2) from a plurality of structured collection procedures 70a, 70b, 70c, 70d, such as provided on these devices and/or within the system 41, and provide a list of available structured collection procedures, such as on the display 82 of the clinician computer 25 or the display 108 of the collection device 24. Further, the clinician computer 25, patient computer 18, and/or server 52 may use the medical use case parameters 220 to locate/categorize/filter such structured collection procedures according to the medical use case(s).

As previously described, the entry criterion(s) 226 establish requirements for initiating the structured collection procedure 70 in order to obtain patient data including biomarker data collected particularly in a predefined context. In one example, the processor 102 of the collection device 24 may use the entry criterion(s) 226 to determine when the associated structured collection procedure 70 is appropriate for the physiological context of the patient and to ensure that all necessary inputs for the associated structured collection procedure have been established. It should therefore be appreciated that the start date and/or time of the structured collection procedure may be dynamically changed automatically by the processor 102 of the collection device 24 if the predefined condition(s) of the entry criterion(s) 226 are not satisfied. Accordingly, until the entry criteria 226 are satisfied, the start date and/or time of the associated structured collection procedure 70 may be some unknown time in the future.

For example, in one embodiment, the structured collection procedure 70 may be automatically selected by the processor 102 from among multiple structured collection procedures 70a, 70b, 70c, 70d, e.g., provided in the memory 110 of the collection device 24, provided in the memory of the computers 18, 25, and/or from the server 52, based on the condition(s) satisfying the defined entry criteria 226 for the associated structured collection procedure. For example, in one embodiment, a first structured collection procedure, such as procedure 70d, may be used to display a trend in blood glucose levels ("bG level trend"). Thus, the entry criteria 226 for the first structured collection procedure 70d may be that the patient's bG level mean rises above a certain predefined rate over a defined period of time (e.g., the past days, weeks, and months from the current date). For a second structured collection procedure, such as procedure 70a, its entry criteria 226 may require that a certain number of bG measurements measured before the breakfast be below a predefined bG value within a defined time period (e.g., the past days, weeks, and months from the current date). In one such example, at startup in one embodiment, upon being commanded (such as by input received via a user interface in another embodiment), or at a scheduled time programmed by the software 34 in yet another embodiment, the processor 102 may run a traversal of the various entry criteria 226 provided by the various structured collection procedures 70a and 70d (which are provided, for example, in the memory 110 of the collection device 24) and determine whether the declared condition(s) for the entry criteria 226 of a particular structured collection procedure 70 are satisfied. In this example, the processor 102 determines that historical data from past measurements in the memory 110 indicates that the bG level mean of the patient has risen and that the entry criteria 226 for the first collection procedure 70d have been met, but that the entry criteria for the second collection procedure 70a have not been met. In this example, the processor 102 then automatically selects and initiates the first structured collection procedure 70d based on the foregoing analysis.

It should also be appreciated that the use of the entry criteria 226 may help reduce the improper allocation of medical overhead by ensuring that the usage instructions for the structured collection procedure 70 are satisfied before beginning the collection event schedule 222. The entry criteria 226 may also help ensure that any requests to perform multiple structured collection procedures do not overlap if incompatible, do not unnecessarily repeat with each other, and do not place a significant burden on the patient. In this way, by using the entry criteria 226, the processor 102 of the collection device 24 can simultaneously solve and automatically avoid many of the noted problems, wherein the patient can avoid any further attempts to diagnose their chronic disease or optimize therapy.

As shown in fig. 5B, the entry criteria 226 may include context-specific entry criteria 234, procedure-specific entry criteria 236, and combinations thereof. Examples of context-specific entry criteria 234 may include one or more variables to identify meals, hypoglycemic events, insulin types and dosages, stress, and so forth. In another example, context-specific entry criteria 234 may be defined, for example, in the form of specific question(s) for which processor 102 requires a specific answer to be received from the patient via input from user interface 146. For example, the processor 102, when executing the entry criteria 226, may display on the display 108 questions regarding whether the patient is willing and able to perform the structured collection procedure 70 within a required period of time. If the patient gives a positive answer through the user interface 146, the entry criteria 226 are met and the processor 102 continues to automatically administer the collection event 237 according to its associated timing as defined in the structured collection procedure 70. If the patient answers the displayed question in the negative, the processor 102 will not continue the structured collection procedure 70 and may reschedule the question to a future time, for example, if the option parameter indicates.

Examples of procedure-specific entry criteria 236 may include one or more variables to identify a disease state, a disease condition, a selected therapy, parameter preconditions, a ratio of insulin to carbohydrate prior to testing for insulin sensitivity, incompatible collection procedures, and the like. The procedure-specific entry criteria 236 may be defined such that the processor 102 will automatically continue the structured collection procedure 70 for one of three initiators, for example, the patient 12, the clinician 14, or the data if the condition(s) of the entry criteria 226 are satisfied. For example, if the clinician 14 specifies a structured collection procedure 70, such as by an authorized user entering a valid password via the user interface 146 in one embodiment in order to unlock a particular structured collection procedure for use, the procedure-specific entry criteria 236 may be satisfied. In another embodiment, the clinician 14 may send a password or authorization code from the clinician computer 25 and/or server 52 to the collection device 24, the collection device 24 specifying (authorizing) the use of the collection procedure 70 by the patient 12 on the collection device 24. It should be appreciated that one or more structured collection procedures 70 may be provided in the memory 110 of the collection device 24 that is not usable by the patient 12, or may be hidden from view on the display 108 (e.g., in a selection list) by the patient until authorized by the clinician 14.

The procedure-specific entry criteria 236 may be satisfied, for example, by the user by selecting a particular structured collection procedure 70 from among the list of structured collection procedures 70a, 70b, 70c, 70d provided on the display 108. One example of a data-initiated procedure for criteria 236 would be that the biomarker measurement(s) provided to the processor 102 indicate a positive occurrence or that a condition exists such that the entry criteria 226 for a particular structured collection procedure is satisfied. Such a condition may be, for example, the occurrence of a single event, such as a severe hypoglycemic event, or the occurrence of a series of events, such as hypoglycemic events within a given predetermined time range, such as within 24 hours from a start time, within a week from a start time, etc., a calendar date time, etc.

Accordingly, the entry criteria 226 may be a single criterion or multiple criteria that establish the context and/or condition of the patient's physiology in relation to the medical use case addressed by the structured collection procedure 70. In another embodiment, the entry criteria 226 may be evaluated after the patient data is collected, such as with respect to historical patient data.

The schedule of events 222 specifies one or more events 237, each of which includes at least one or more variables defining an administration time 238, instructions 230 for administering the event, requirements 240 for patient actions, which may include requirements for information from the patient and/or requirements for collecting at least one type of biomarker data from the patient, and combinations thereof. For the delivery times 238, the schedule of events 222 may specify the timing of each event 237, such as biomarker sampling at a particular time of three consecutive weekdays, or one sample taken at wake-up, one sample taken thirty minutes later and another sample taken one hour later.

The guidance 230 for each event 237 and for any criteria 224, 226, 228 may include, for example, providing an electronic reminder (audible, visual) for: start, end, and/or wake up at a particular time, perform bG collection at a particular time, ingest a particular meal or food(s) at a particular time, perform a particular exercise(s) at a particular time, take medicine at a particular time, and so forth. The guidance 230 may also include information, questions, and requirements for recording specific information about physiology, health, well-being, etc. at a particular time, recommendations for improving compliance with the collection procedure, encouragement, and positive/negative feedback.

It should be appreciated that the event 237 defines all steps that need to be performed before and after biomarker sampling according to the requirements 240, thereby creating a reproducible set of conditions, i.e., context before and/or after sampling, in the biomarker data for the biomarker sampling. In the context of diabetes, examples of such biomarker data include fasting blood glucose values, preprandial glucose values, postprandial glucose values, and the like. An example of a set of circumstances can include data associated with biomarker values that identify collected information in patient data about meals, exercise, therapy administration, sleep, water intake, and the like.

Each event 237 in the schedule of events 222 can be time-based, event-based, or both. Event 237 may also be a meal start, a wake time, an exercise start, a therapy administration time, a relative offset used with previous glucose values, or a time indicating movement above or below a predetermined biomarker value threshold. The events 237 may also include any desired patient actions that must be performed before and during biomarker sampling, thereby creating a reproducible condition at the time of biomarker sampling. This aspect may include one or more of meals, exercise, therapy administration, sleep, water intake, and the like. In addition, the events 237 in the schedule of events 222 may be adjusted (number, type, timing, etc.) to accommodate the working schedule of the patient 12, stress factors, and so forth.

As previously described, the adherence criteria 224 are used to qualitatively assess whether an event 237 administered according to the schedule of events 222 provides data that is acceptable in addressing the medical use case underlying the structured collection procedure 70. In particular, the adherence criteria 224 can provide variables and/or values that are used to validate data from the performed events 237. For example, the adherence criteria 224 may be a check performed by the processor 102 of the collection device 24 of whether the value collected in response to the event 237 is within a desired range or is above, below, or at a desired value, where the value may be time, quantity, type, and so forth. In one embodiment, the same or different adherence criteria 224 may be associated with each event 237 within the schedule of events 222 and with the entry criterion(s) 226, and in another embodiment as the exit criterion 228, such as shown in FIG. 6D (i.e., "stop exercise when bG comes back within target range," which defines both adherence and exit criteria). In one embodiment, one or more events 237 in the schedule of events 222 can be modified (e.g., added, deleted, delayed, etc.) if one or more particular events do not satisfy the adherence criteria 224 for the one or more particular events. In one embodiment, failure of the adherence criterion(s) 224 can trigger an adherence event 242. In one embodiment, upon occurrence of an adherence event 242 because the associated adherence criteria 224 for the event 237 is not met or satisfied, the processor 102 may request that one or more additional actions be performed as a result. For example, processor 102 may prompt the patient for additional information on display 108 and/or a question to determine whether patient 12 is ill, stressed, or unable to perform a request, such as a meal or exercise. If the patient answers "yes," e.g., via the user interface 146, the processor 102 may provide a delay (i.e., pause) to the schedule of events as part of the adherence event 242. In one embodiment, the delay may continue until the patient indicates that his or her condition is better in response to another question prompted by the processor 102, such as on the next day or after a predefined amount of time and also as part of the adherence event. For example, as part of event 237, the processor 102 prompts the patient 12 to take a medication, but the patient is not at home, and his/her insulin, for example, is at home. The patient 12 may select a delay via the user interface 146, wherein the processor 102 reminds the patient again after a predetermined amount of time as part of the adherence event 242. The delay may also have an upper limit where the structure test procedure 70 may end as it is if the schedule of events 222 has not restarted within a certain amount of time. In another embodiment, another form of adherence event 242 is a violation event that results when the person performing the structural test procedure 70 is not able to make the recommended changes in response to the requirements. For example, a portion of the request or event 237 may be to have the patient adjust the medication dose from 10U to 20U, where the patient answers a question displayed on the display 108 asking whether the patient is about to or has complied with the change in negative. In response to such violation events, the processor 102 may also send messages and/or provide delays, as discussed above with respect to the adherence event.

In another example and in one embodiment, the bG measurements must be collected once before each meal in order for the structured collection procedure 70 to provide data that can be used to solve the medical use case or problem for which it is designed (such as identified by the use case parameters 220). In this example, if the patient is not able to take bG measurements for lunch in response to the requirements for the collection according to the schedule of events 222, and thus the adherence criteria 224 for that event 237 fail to be met, the processor 102, in response to the associated adherence event 242, may be programmed according to the instructions in the collection procedure 70 to cancel all remaining events 237 for that day in the schedule of events 222, mark the morning bG measurements stored in a data file (such as the data file 145 in fig. 4) as invalid, and reschedule the schedule of events 222 for the next day. Other examples of further actions that may be taken by the processor 102 in response to the adherence event 242 may be: dynamically changing the structural test protocol by switching to a schedule of secondary events that may make it easier for the patient to perform, providing additional events to the measurements to make up for missing data, changing the exit criteria from primary exit criteria to secondary exit criteria to provide modified criteria(s), changing the adherence criteria from primary adherence criteria to secondary adherence criteria, populating the missing data for a failed event with historical data or an estimate based on historical data, performing a specific calculation to see if the structured collection procedure 70 can still be successfully performed, sending a message to a specific person (such as a clinician) regarding the failed event, providing a specific indication in an associated data record 152 to ignore or estimate missing data points, and so forth. In other embodiments, the adherence criteria 224 may be dynamically evaluated, for example, based on one or more biomarker values and/or input received from a user interface in response to one or more questions, by an algorithm that determines whether the collected data provides a value that may be used to address the medical use case or example. In this example, if the calculated adherence value is not useful, e.g., does not fall within the desired range or does not meet a particular predefined value, then further processing as defined by the resulting adherence event will occur later, such as any one or more of the processes discussed above.

As previously described, the exit criteria 228 establish requirements for exiting or completing the structured collection procedure 70 such that the structured collection procedure 70 has sufficient contextual data to answer the medical question addressed by the structured collection procedure 70. The exit criteria 228 may help increase the efficiency of the structured collection procedure 70 by minimizing the required number of samples needed to address a medical use case. "resolve" means that sufficient patient data must be collected so that the clinician 14 can give an assessment of the medical use case. In other embodiments, the evaluation may be represented by a given confidence interval. Confidence intervals are a set of discrete or continuous values that are statistically assigned to a parameter. The confidence interval typically includes the true value of the parameter over a predetermined portion of time.

As with the entry criteria 226, the exit criteria 228 may include one or more of context-specific exit criteria 244, procedure-specific entry criteria 246, and combinations thereof. Examples of context-specific exit criteria 244 may include one or more variables to identify mood, desired glycemic event (i.e., blood glucose level), to indicate stress, illness, contraindications (such as hyperglycemia, hypoglycemia, vomiting, fever, etc.). Examples of the protocol-specific entry criteria 246 can include one or more variables to identify a number of events that satisfy the adherence criteria, a biomarker value within a desired predetermined range and/or at a desired predetermined value, a desired disease state, a desired disease condition, no change in the biomarker after a predetermined period of time, no significant progression to the desired biomarker value within a predetermined period of time, and so forth. It should be appreciated that in one embodiment, the exit criteria 228 may establish the condition(s) that need to be met for the entry criteria 226 of the second structured collection procedure 70. For example, after determining the appropriate insulin to carbohydrate (I: C) for a first collection procedure (e.g., the structured collection procedure 70B in fig. 6B), a structured test for determining an optimal time for meal start administration of perfusion is run, such as the structured test procedure 70C (fig. 6C) requiring a current I: C ratio, which may be adjusted such that the processor 102 may automatically implement the schedule of events for a second structured collection procedure 70C after the exit criteria for the first structured collection procedure 70B are met at some unknown time. In other embodiments, the exit criteria 228 of the first structured collection procedure 70 and the entry criteria 226 of the second structured collection procedure 70, e.g., being run by the processor 102 according to the schedule of events 222, may both be based on, e.g., the same one or more of the contraindications mentioned above. In such an embodiment, the processor 102 will automatically start the schedule of events for the second structured collection procedure 70 after occurrence of a contraindication (which in this example satisfies the exit criteria 228 of the first structured collection procedure 70) provided to the processor 102 and/or detected by the processor 102, for example via the user interface 146 and/or the biosensor 140, respectively, because the entry criteria 226 of the second structured collection procedure 70 have also been satisfied. An example of the second structured collection procedure 70 that may be initiated by exiting the first structured collection procedure may be one having a schedule of events 222 that requires biomarker samples to be taken at routine intervals, such as every 30 minutes, hour, specific times of day, etc., until the contraindication(s) are cleared (e.g., the biomarker value(s) reach a desired range or value, the patient 12 indicates to the processor 102 via the user interface 146 that the contraindication(s) are no longer present, a predefined period of time expires, etc.). Such an embodiment is useful if it is desired to record the context and value of the event following the occurrence of the contraindication(s) and the first collection procedure should be exited upon the occurrence of the contraindication(s).

The exit criteria 228 may be a single criterion or multiple criteria that establish conditions for exiting the structured collection procedure 70. The conditions are provided in one embodiment to ensure that sufficient contextualized biomarker data has been obtained to answer the medical question addressed by the collection method. For example, such that a predetermined number of valid samples have been acquired, or such that the variability in the samples is below a predetermined threshold. It should therefore be appreciated that the end date and/or time of the collection procedure 70 may be dynamic and may be automatically changed by the processor 102 if the predefined condition(s) of the exit criterion(s) 228 are not met. Likewise, the conditions of the exit criteria 228 may be dynamic and may be automatically changed by the processor 102, for example, with or without the particular adherence criteria 224 being met. For example, in one embodiment, if the adherence criteria 224 for a particular collection event 237 is satisfied, the processor 102 is instructed to use a first exit criterion, and if not, the processor 102 is instructed to use a second exit criterion different from the first exit criterion. Accordingly, until the exit criteria 228 are satisfied, the end date and/or time of the structured collection procedure 70 may be some unknown time in the future. In another embodiment, the exit criteria 228 may be evaluated after the patient data is collected, for example, with respect to historical patient data.

It will be appreciated that the entry and exit criteria 226, 228, along with the adherence criteria 224, may help shorten the time to administer the structured collection procedure 70 and reduce the overhead associated with the collection by defining one or more of acceptable conditions, values, structures, and contexts needed to administer the schedule of events 222 so as to count each collection event 237 and/or reduce test strips 30 consumed in terms of useless collections that do not help address medical use instances or issues. Reference will be made to fig. 6A-6E hereinafter.

Structured collection procedure example.

6A-6E illustrate examples of some structured collection procedures 70a, 70b, 70c, and 70d depicting functions that may be readily converted by one of ordinary skill in the relevant art into instruction code that may be implemented on any of the devices 18, 24, 25, 26, 28, 36, 52 described above. Accordingly, a discussion of the pseudo-code or actual code for the functions shown is not provided for the sake of brevity.

FIG. 6A illustrates by way of illustration one embodiment of a structured collection procedure 70a that is used to obtain contextual biomarker data from a diabetic patient. The horizontal axis shows the performance time 238 of various events 237, and the vertical axis shows the adherence criteria 224 without a numerical value. In the illustrated embodiment, the events 237 may include recording information about meal 248 and sleep 250 to provide context 252 for five biomarker samples 254, and the events 237 are also part of the schedule of events 222. In this example, the adherence criterion 224 for the meal 248 may be a value that must be higher than a minimum value, for example, for a certain carbohydrate amount. The entry criteria 226 may include, for example, that the biomarker value is above a particular value, such as the particular value needed to satisfy the contextualization requirements for starting the structured collection procedure 70 a. The exit criteria 228 may also include that the biomarker value is below a particular value, such as the particular value needed to satisfy the contextualized requirement for ending the structured collection procedure 70 a. Such a structured collection procedure 70 may be used to help address several instances of medical use.

GLP 1 The test protocol is structured.

For example, several epidemiological studies have demonstrated that elevated postprandial glucose (PPG) levels are an important predictor of cardiovascular mortality and morbidity with respect to type 2 diabetes (T2D). To this end, there is a series of human glucagon-like peptide-1 (GLP 1) drugs that act over an extended period of time once a week and that can be prescribed to T2D patients who exhibit elevated postprandial bG values. These GLP 1 drugs resemble the natural hormone GLP-1, which plays an important role in blood glucose regulation by stimulating insulin secretion and inhibiting glucagon secretion. Thus, in one embodiment, a structured collection procedure 70 can be provided that proposes making a large number of measurements of bG values over time during a time after one or more meals, thereby allowing for the efficacy of therapy to be demonstrated through the observed reduced postprandial bG values. Based on such observed values, it may be determined whether a dose recommendation for the GLP 1 drug and/or a particular GLP 1 drug is the completely correct drug for the patient.

For example, when a patient is prescribed a particular medication (e.g., a GLP 1 medication), the structured collection procedure 70 may be provided on the collection device 24. In the case of a GLP 1 drug, where it is desired to determine drug efficacy, the entry criteria 226 for such a structured collection procedure may then be that the patient must confirm that the processor 102 performed the structured collection procedure 70 within a certain period of time (e.g., within the next 4 to 24 weeks) in response to a question displayed on the display 108, and/or that the processor 102 determined that the patient's average PPG level was high (e.g., above 141 mg/dl) from previous postprandial bG values over a period of time (e.g., weeks, months, etc.). Other factors may also be used as the entry criteria(s) 226, such as fasting glucose being above a particular value (e.g., 126 mg/dl) or below a particular value (e.g., 240 mg/dl).

The schedule of events 222 is then automatically executed by the processor 102 after the conditions of the entry criterion(s) 226 have been satisfied and verified by the processor 102. The schedule of events 222 will indicate the desired collection events 237, where the processor 102 will automatically prompt the patient to enter a post-meal bG value after breakfast, lunch, and dinner (i.e., perform a bG measurement on a sample provided to a test strip that is read by the measurement engine and provided to the processor for storage in the data record and display). The schedule of events 222, by being customized by the prescribing physician, can also define a collection event 237 having an administration time 238 where the patient must take the medication and provide a dose reminder when the medication has been taken and a requirement 240 for confirmation from the patient. For example, the processor 102, when executing the schedule of events 222, will automatically prompt the patient to take a dose, for example, 10mg of Taspoglutide (Taspoglutide) on a particular day of the week, at a time specified by the collection event 237 in the schedule of events 222, and then after a period of time (e.g., after 4 weeks) to take a second dose according to a second interval, followed by 20mg also on a particular day of the week. A collection event 237 may also be defined in the schedule of events 222, where the processor 102 presents a request for information on the display 108, such as whether the patient feels good, to provide an indication of energy level, to provide an indication of the size of a meal taken, and so forth.

The condition(s) of adherence for each entered post-meal bG value can be provided by using adherence criteria 224, where before or after a certain amount of time of the prompt (e.g., before or after the prompt)A 30 minute test window) of any post-meal bG values entered (i.e., measured), such measured values will not be accepted by the processor 102 as valid measurements for the schedule of events 222. In one embodiment, the processor 102 may automatically take further action based on the adherence criteria 224 evaluation automatically performed by the processor 102. For example, if the bG measurement is taken before the measurement specified by the collection event in the event schedule 222 and falls outside of a defined test window, e.g., -30 minutes before the collection event time, the processor 102 in this case will automatically notify the patient that the measurement still needs to be taken at the specified time because the previous measurement was not accepted because it falls outside of the test window. Likewise, if after the test window, for example, collect event time +30 minutes, the processor 102 may automatically notifyThe patient's previous measurements were not accepted as falling outside the test window and encouragement is provided on the display 108 to cause the patient to struggle to make measurements within the test window.

The exit criteria 228 for such a GLP 1 structured collection procedure 70 can be an indication that the bG mean has reached a desired value using a minimum amount of time (e.g., days, weeks, months, etc.), a minimum acceptable number of measurements, or both. Likewise, the exit criteria 228 can be an indication that the bG mean has not reached a desired value after a maximum amount of time (e.g., days, weeks, months, etc.), a maximum number of acceptable measurements, or both. Further, the exit criteria 228 may be other factors that indicate that the medication or dose is completely inappropriate for the patient, such as nausea and/or vomiting for each of the minimum number of days in response to a collection event prompted by the processor 102 on the display 108 for such information. Other factors may also be used as exit criteria 228, such as fasting glucose being below a particular value (e.g., 126 mg/dl) or above a particular value (e.g., 240 mg/dl). The data collected from such a drug-based structured collection procedure 70 can then be used by a physician to make dose recommendations for GLP 1 drugs and/or to determine whether a particular GLP 1 drug is the correct drug for a patient.

Fig. 6B illustrates another example showing a structured collection procedure 70B with defined medical use case parameters 220 that indicate that the procedure may be helpful in determining the appropriateness of the ratio of insulin to carbohydrate (I: C). As shown, the entry criteria 226 are defined as the patient simply confirming the guidance 230 on selecting a fast-acting meal, which should note that the insulin dose is calculated for the current I: C ratio, and agrees not to exercise and not to ingest additional food or insulin during the test period. For example, the processor 102 may present such guidance 230 on the display 108 that the user may confirm after reading by entering "yes" or "no" for the desired entry selection using the user interface 146. If the user enters "yes," the entry criteria 226 are satisfied and the processor 102 automatically begins the schedule of events 222 defined in the structured collection procedure 70 b. In another embodiment, the entry criteria 226 may be or include meeting a requirement for selecting a fast-acting meal. For example, the request 240 for a selection may be that the processor 102 displays a menu of selections on the display 108 that provides a list of quick-acting meals that require entry of such selections through the user interface 146. For example, the selection of a quick-acting meal may be made by pressing one of the buttons 147, 149 or through a touch screen interface (if provided by the display 108). Such selections may then be stored in the memory 110 of the collection device 24 as settings data 163 (FIG. 4), which may be part of the data file 145 (FIG. 4) for the structured collection procedure 70 b. In an alternative embodiment, a specific fast-acting meal may be recommended by the structured collection procedure 70 b.

As shown, the schedule of events 222 may include one or more events, such as the plurality of events 237a-k shown, and each of which has an associated delivery time 238a-k and an action requirement 240 a-k. As shown, the action requirements 240a-c and 240f-k are requirements for the user to take bG level measurements, the requirement 240d is for taking a dose of insulin, and the requirement 240e is for eating the fast-acting meal. It is further shown that the events 238f-k each have adherence criteria 224, which adherence criteria 224 must be met if the data for the events 238f-k is to be recorded in the data file 145. In this example, the adherence criteria 224 require that at their respective delivery times 238f-kActions 240f-k are completed within 20 minutes to count the data records 152 that record the value(s) received for the corresponding event 237f-k for completion of the collection procedure 70 b. In one embodiment, the processor 102 will issue each of the requests 240a-k at its associated execution time 238a-kk to obtain the resulting data values, e.g., 256a-k (fig. 4), e.g., at the time the request is fulfilled.

For example, the processor 102 may utilize the requirement 240a to prompt the patient 12 to take a bG level (biomarker) measurement at the delivery time 238 a. Upon receipt of the resulting measurements by the processor 102, such as automatically from the measurement engine 138 after reading the test strip (biosensor) 140 for the desired biomarker, the measurements are automatically recorded by the processor 102 in the data file 145 as corresponding data values 256a for the associated event 237 a. With respect to actions 240d and 240e, the processor 102 may automatically prompt the patient 12 at a desired time to take a prescribed action at the desired time, and then again automatically prompt the patient to confirm that the desired action has been taken, or that a predefined condition has been reached. Date timestamp 169 may also be automatically provided in date record 152 by processor 102 in the following cases: upon triggering the claim 240a-k, upon acknowledging the claim 240a-k, upon completing the event 237a-k, upon receiving the data value 256a-k for the event 237a-k, and combinations thereof. Moreover, in another embodiment, the patient 12 can record the data values 256a-k for one or more events 237a-k by inputting the data directly into the device 24 via the user interface 146, wherein the processor 102 stores the input data values/information in the associated data record 152 for the event 237a-k, or in other embodiments can record a voice message with the information for later transcription into digital data. In other embodiments, the patient 12 may be instructed by the collection device 24 to record data for the events 237a-k using the paper tool 38.

As previously described, each event 237 may be a record of a biomarker value, or a requirement for a required patient action (such as eating, exercise, therapy administration, etc.) necessary to generate a context for the biomarker value. In the illustrated embodiment, the scenario 252 for completion events 237a-c is to establish a pre-meal baseline and trending-free condition, and for events 237f-k is to establish post-meal fluctuations and tails. The context 252 for these events may also be associated with the respective data record 152 for each event as context information 156 (FIG. 4). Such information is useful later when reconstructing the data and/or when it is desired to know the context for generating the data records.

It should be appreciated that any patient actions taken in addition to the required requirements for the patient actions 240a-k may also be recorded by the processor 102, but will not be considered by the processor 102 as part of the collection procedure 70 b. The expected data 256a-k for the events 237a-k may be identified based on event type, event time, event trigger, and combinations thereof. Each delivery time 238a-k may be fixed or variable based on previous data. In other embodiments, some of the events 237a-k may also be past, current, or future events, such as meals, workouts, etc., or data values for hypoglycemic events, hyperglycemic events, for example, or data of particular values of interest. In some embodiments, the events 237a-k may be identified by a protocol-based paper tool 38.

Further, as shown, if the conditions of the exit criteria 228 are met, the structured collection procedure 70b will end. In this example, the exit criteria 228 are satisfied if at least three of the actions 240f-k satisfy the adherence criteria 224. For example, if desired, the processor 102 may provide a unique identifier (e.g., an incremental count) 167 (FIG. 4) in the data file 145 for each event 237a-k that is performed and satisfies the adherence criteria 224. In the illustrated embodiment of FIG. 4, each of the events 237a-c and 237e-k receives a unique identifier, but event 237d does not (e.g., < NULL >) because it does not satisfy the associated adherence criteria (not shown). Further, in one embodiment, the analysis logic 258 and resulting recommendations 260 may also be provided in the structured collection procedure 70b, which the processor 102 may automatically apply to the collected data when the exit criteria 228 are met.

Similar features are also provided in the examples shown in fig. 6C and 6D, where fig. 6C depicts a structured collection procedure 70C with defined medical use case parameters 220 that indicate that the procedure is useful for determining the appropriateness of a bolus for meal initiation. Likewise, fig. 6D depicts a structured collection procedure 70D with defined medical use case parameters 220 that indicate that the procedure can be used to determine the appropriateness of exercise equivalent to carbohydrate intake. In addition to the foregoing examples, other such structured collection procedures may be designed to address other various medical use cases, such as the following: determining the effect of eating a particular food on the patient's biomarker levels; determining an optimal time to take the drug before and/or after a meal; and determining the effect of a particular drug on the patient's biomarker levels. Other structured collection procedures may also be provided that may be used to address issues related to: how to optimally initialize a therapy for a patient, find a determination of the state of disease progression for a patient, find the best way to optimize a patient's therapy, etc. For example, the clinician 14 may define and/or use a pre-defined structured collection procedure 70 that looks at various factors that may have an effect on the patient's therapy. Such factors may include, for example, stress, menstrual cycle, premenstrual effects, background insulin, exercise, bolus timing with meals, basal rate, insulin sensitivity, postprandial behavior, and the like.

Fig. 6E shows a diagram of a structured collection procedure 70 including one or more sample groups 262, where each group includes an event schedule 222 that provides repetitions between entry criteria 226 and exit criteria 228. In this example, the schedule of events 222 includes one or more events 237 that occur each day during a consistent time of day. Since the structured collection procedure 70 may span many days, even weeks, and/or months in obtaining the contextualized biomarker data from the diabetic patient 12 before the exit criteria 228 are met, one or more checks 264 (such as for parameter adjustments) may also be provided between the entry and exit criteria 226, 228 and/or to assess whether to rerun the sample set 262 in one embodiment. The duration between such checks 264 can be used for physiological system equalization, assessing treatment efficacy, or convenience. For example, analysis of the check 264 may be performed by the processor 102, either between each sample group 262 or after a predetermined number of such sample groups 262 (as shown), in order to determine whether any parameters in the collection procedure 70 need to be adjusted.

Such analysis may be for parameter optimization or efficacy assessment, for example. For parameter optimization, the processor 102 may use information from previous optimizations, parameters set by a clinician, and collection or therapy strategies to run calculations on samples provided in the previous schedule of events 222 or sample groupings 262, recommending new parameter values. For efficacy assessment, the processor 102 may assess data not utilized by the optimization analysis. It should further be appreciated that after taking a set of samples (i.e., sample set 262), the processor 102 may also assess data from the sample set 262, such as whether such data is needed to alter/optimize the individual's therapy. Adherence criteria may be applied to perform this assessment on the sampled set 262 or aggregated data. For example, the processor 102 may use the first adherence criteria 224 to evaluate whether the sample set 262 provides the least amount of data and, if not, for example, no modification/optimization of the patient's therapy will occur. Another adherence criterion 224 can allow the processor 102 to evaluate whether the data is acceptable to allow for the adjustments required by the check 264, such as to see if the spread of the data, variability (noise) is too high, and using other data attributes of the data. In this example, if such adherence criteria are met, the processor 102 makes the following evaluations: adjusting the parameters of the protocol may easily result in a minimal risk of a serious event, such as a hyperglycemic or hypoglycemic event. Finally, the processor may use the adherence criteria to evaluate the exit criteria based on the data of the sample group, e.g., when the data from the sample group 262 satisfies the adherence criteria, e.g., as discussed above for the sample group.

It should be recognized that collection or therapy strategies can be categorized as either scale-based (sliding or fixed) evaluations or formula-based evaluations. As input to the collection or therapy strategy, in one embodiment, the processor 102 may utilize data collected from a predetermined number of previous sample groupings 262 (one or more). This data can be used as individual points (formula-based collection or therapy strategies only), or combined with filtering for scale-based evaluation. In another embodiment, the results of the check 264, e.g., performed by the processor 102, may also result in a status or recommendation being automatically provided by the processor 102. Such a state or recommendation may be, for example, a state to continue with a current parameter value, a recommendation to change a particular parameter, a recommendation to change adherence and/or exit criteria, a state to switch to secondary adherence and/or exit criteria by the processor 102 based on an analysis performed on data from a previous schedule of events or a previous sample grouping, or a recommendation to terminate a collection procedure, etc. A discussion regarding performing structured collection using a structured collection procedure according to one embodiment of the present invention will be provided below with respect to fig. 7A.

And (5) structured collection.

Fig. 7A depicts a structured collection 300 of diagnostic or therapy support for a patient with a chronic disease. The method 300 may be implemented as instruction code of a program running on a computer having a processor and memory, such as preferably the clinician computer 25 (FIG. 2) as stand-alone software, as part of the software 34, or as software provided as a service by the server 52 through a secure web implementation over the public network 50. When the processor 76 executes the program from the memory 78 of the clinician computer 25, as one of the functions, the processor 76, upon receiving a query for medical use cases and/or questions, searches the memory 78, the computer-readable medium 40, and/or the server 52 for all structured collection procedures 70a-d that match the query submitted in step 302. For example, in one embodiment, the processor 76 may read the medical use case parameters 220 for each available structured collection procedure 70a-d and provide selection options on the display 82 for those structured collection procedures that match the query using conventional search algorithms (e.g., lists, trees, heuristics, etc.) in step 304.

In one embodiment, the displayed list may reflect, for example, structured collection procedures 70a, 70b, 70c, and 70d that may be obtained from the server 52 for use. In another embodiment, the displayed list of selection options may be dynamically created based on the type of medical use case that the clinician 14 wants to study. For example, prior to step 302, a list of selectable medical use instances may be displayed on the display 82 by the processor 76. In such an embodiment, using the user interface device(s) 86, the clinician 14 may select, for example, a "determine the effect of meals on patient therapy" medical use case from among the various medical use cases displayed. After the clinician makes such a selection, the processor 76 receives the selection from the user interface device(s) 86 as input, and after using the decision logic provided by the software 34 (e.g., if …), the processor 76 will then display in step 304, for example, structured collection procedures 70b (e.g., structured collection procedures to determine a more accurate insulin to carbohydrate ratio) and 70c (e.g., structured collection procedures to determine bolus timing for meal initiation), instead of structured collection procedures 70a and 70d, which are structured collection procedures unrelated to the medical use case. Likewise, "displaying all structured collection procedures" may also be an option among the displayed medical use cases, where a complete list of available structured collection procedures will then be displayed in step 304. In another embodiment, step 302 may be skipped and the processor 76 may simply provide a display of the structured collection procedure 70a-d available in the memory 78 of the clinician computer 25 in step 304.

In step 306, a clinician using the user interface device 86 may select a structured collection procedure on the computer 25 for diagnosis or therapy support. For example, the selection process may include selecting from the list displayed in step 304 that provides one or more structured collection procedures. After the clinician makes such a selection in step 306, the processor 78 receives the selection from the user interface device(s) 62 as input, and the processor 76 of the computer 25 automatically retrieves the selected structured collection procedure 70 from an electronic component (e.g., the computer memory 78, the server 52, or the computer readable medium 40) and displays it on the display 82 for viewing.

It should be appreciated that each structured collection procedure 70a, 70b, 70c, and 70d is based on one instance of medical use and has parameters defining entry criteria 226, schedule of events 222, adherence criteria 224, and exit criteria 228. As previously described, the entry criteria 226 establish the conditions that need to be met before biomarker data is obtained from the patient. Each event 237 of the schedule of events 222 includes an administration time, patient instructions for administering the event, a patient action, a requirement for information from the patient, a requirement for collecting at least one type of biomarker data from the patient, and combinations thereof. The adherence criteria 224 are used to qualitatively assess whether an event 237 administered according to the schedule of events 222 provides data that is acceptable to address the medical use case underlying the structured collection procedure 70. Further, as previously described, the exit criteria 228 establish the conditions that need to be met before exiting the structured collection procedure 70.

In step 310, after the processor 76 displays the selected structured collection procedure 70, the clinician 14 may adjust any of the parameters 222, 224, 226, and 228 also displayed on the display 82 in order to meet the needs of the patient 12 and/or the clinician's interests. Security safeguards may be implemented to ensure that only the clinician 14 can modify such parameters and/or run the software 34, such as by password protection. The processor 76 receives as input any of the changes to the parameters 222, 224, 226 and 228 through the user interface device 86 and saves the revised structured collection procedure 70 in the memory 78. Next, in step 312, the selected structured collection procedure 70 is prescribed by the clinician 14 to the patient 12 on the computer 25, wherein the processor 76 of the computer 25 provides the selected structured collection procedure 70 as output to the patient 12 for execution thereof. For example, in step 314, the prescribed structured collection procedure 70 is implemented electronically as part of the software 34 on a processor-based device, such as the collection device 24 or any of the other aforementioned devices 18, 28, and 36 (FIG. 1), or in other embodiments, as part of the paper tool 38.

In one embodiment, the prescribed structured collection procedure 70 may be implemented from the clinician computer 25 (FIG. 2) to the collection device 24 via the communication link 72, via a web page through the public network 50, and/or by making it available for download onto the server 52. In other embodiments, the specified structured collection procedure 70 may be provided by the computer readable medium 40 and loaded by one of the devices 18, 24, 28, and 36, downloaded from another of the devices 18, 24, 25, 26, 28, and 36, or downloaded from the server 52 via a cellular or telephone connection. It should be noted that the new/updated/specified structured collection procedures 70 available on the devices 18, 24, 25, 26, 28 and 36 may be provided in any standard manner, such as by postal letters/cards, e-mail, text messages, pushers, and the like.

The structured collection procedure is customized.

Figure 7B conceptually illustrates one example of a predefined structured collection procedure 70 having defined medical use case parameters 220 that indicate that the procedure facilitates medical use cases or questions that require knowledge of the patient's blood glucose (bG) level and/or relationship between blood glucose values and time of day, meal size, and energy levels. As previously described, the use case parameter 220 may be used as an identity tag, where the processor 102 may locate the associated structured collection procedure 70 in response to, for example, a search query for an entered use case or question. For example, a search query may be input into collection device 24 via user interface 146 and/or may be received from clinician computer 25. Such search queries may be due to a desire to know which use cases can be resolved by the structured collection procedure 70 currently available on the collection device 24, or due to a desire to know which structured collection procedure 70 will be available to resolve a particular use case or problem. Thus, in one embodiment, the use case parameter 220 allows for automatic selection by the processor 102 of a structured collection procedure 70 from a plurality of structured collection procedures 70a-d (provided, for example, in the memory 110, the memory 78, the computer readable medium 40, and/or the server 52) based on a selection from a displayed list provided on the display 108 from the processor 102 or based on a selection received from the processor 102 from a user interface defining a medical question. In other embodiments, the use case parameter 220 may also indicate that the structured collection procedure 70 may also be used to exhibit a relationship between bG level values and time of day, meal size, and/or energy levels.

In one embodiment, various predefined parameters of the structured collection procedure 70 may be displayed for modification/customization by an authorized user on the display 108 via the processor 102 of the collection device 24 and/or on the display 82 via the processor 76 of the clinician computer 25. Such authorized users may be identified on the collection device 24 and/or clinician computer 25, for example, by passwords entered via the user interfaces 146, 86, respectively. In such an embodiment, various pre-defined parameters of the structured collection procedure 70 may be displayed on the displays 108, 82, wherein the customizable parameters may provide editable or selectable variables in the following manner: drop-down boxes with various selection options, radio buttons, checkboxes, formatted fields (month-day-year, numbers, letters, etc.) that require a particular type of information, text boxes for entering messages to be displayed, and so forth. In one embodiment, the structured collection procedure 70 may be displayed for editing in a tabular format (as shown) or, in another embodiment, may be displayed sequentially by listing the parameters one at a time in a scrolling manner. In another embodiment, a structured collection procedure may be provided that cannot be modified.

As shown in fig. 7B, the structured collection procedure 70 may also include parameters defining one or more criteria that set conditions that need to be met by the patient 12 in order for the structured collection procedure to begin (i.e., the entry criterion(s) 226), end (i.e., the exit criterion(s) 228), and combinations thereof. In one embodiment, the processor 102 of the collection device 24 automatically begins, assesses, and ends the structured collection procedure 70 using the one or more criteria if the condition(s) defined by the structured collection procedure 70 are satisfied. In another embodiment, the adherence criterion(s) 224, which are conditions that need to be met in order to accept the collected one or more items of data, may also be provided in the structured collection procedure 70.

As also shown in fig. 7B, the structured collection procedure 70 also includes parameters defining one or more (collection) events 237, which together make up the schedule of events 222. Where each event 237 includes one or more requirements 240, such as measurements from the measurement engine 138 of biomarker values for the sample provided to the biosensor 140 and/or information entered by the patient through the user interface 146, for example in response to questions presented by the processor 102 on the display 108. In the illustrated embodiment, the requirements 240 are for a bG measurement, a meal size indication (S, M or L), and an energy level indication (1, 2, 3, 4, 5), where 1 is lowest and 5 is highest. Other such requirements 240 may include indicating whether the patient has performed exercise, indicating whether a particular food has been eaten, indicating which medication was taken, indicating the dosage of medication taken, etc., and may also be provided in other structured collection procedures 70. In the illustrated embodiment, the collection event may be customized by a yes/no selection box selecting the requirement 240 that the processor 102 should perform.

The structured collection procedure 70 may also include guidelines 230 and timing or administration times 238 associated with each collection event 237 and with each of the entry, exit, and adherence criteria 226, 228, and 224. Such guidance 230 is provided by the processor 102 to the display 108 upon the occurrence of an associated collection event 237 or other parameter. For example, the collection event 237 for a pre-breakfast bG measurement may also have a requirement 240 for an indication of the patient's energy level. Thus, in this example, an associated guide 230 is provided by the processor 102 on the display 108 stating "please indicate the energy level". It should be appreciated that the directions 230 are text boxes, fields, areas that allow information to be provided to the patient to assist the patient in performing the structured collection procedure 70. In this example, one of the numbers from 1 to 5 may be selected as data input for the requirement 237 by pressing one of the buttons 147, 149 or by a touch screen interface (if provided by the display 108), which is then stored by the processor 102 in the memory 110 of the collection device 24 as part of the data file 145 (FIG. 4) for the structured collection procedure 70.

The timing parameters 238 of the structured collection procedure 70 are used to indicate a particular date and/or time (month-day-year, hour: minute) for any of the associated collection event 237, the entry, exit and adherence criterion or criteria 226, 228, 224, or to indicate a period of time after a previous collection event in which the associated collection event was administered(s) ((s))n). Time periods for the various collection events 237 in the illustrated embodiment ₁n、 ₂n、 ₃nHours are indicated, but in other embodiments may be indicated in minutes or seconds. In another embodiment, the timing or performance time parameter 238 for an associated collection event 237 and for one or more entry, exit and adherence criteria 226, 228, 224 may be controlled by anotherCollect events and/or be modified by the one or more criteria.

For example, in the illustrated embodiment, the entry criteria 226 are modified by adherence criteria 224 by incrementing by one day if guidance 230 provided in the form of the question "do you want to test for more than 3 consecutive days" is not confirmed by the patient 12 (such as by providing a "no" selection on the collection device 24). In the example shown, the "confirmation guidance" may be a drop-down selection provided in a combo box for customizing the adherence criteria 224 for the associated collection event 237, which when selected causes the processor 102 to wait for accepted/not accepted input (e.g., via buttons 147, 149) before executing the remaining logic of adherence criteria 224 ("if not increasing timing by one day"). Also in this example, the processor 102 may set the timing or performance time parameter 238 of the exit criteria 228 to a date 3 days (month-day-year) after completion of the entry criteria 226, according to logic provided in the adherence criteria 224 associated with the exit criteria 228. It should be appreciated that in one embodiment, various possible combinations of logical propositions that can be performed by the structured collection procedure 70 can be predefined and selected for customization through drop-down boxes, and/or in another embodiment, logical propositions can be established.

The structured collection procedure 70 may also include option parameters 232 associated with each collection event 237 and with each of the one or more entry, exit, and adherence criteria 226, 228, 224. The option parameter 232 may have a customizable value(s) to govern whether the data and/or results of the associated collection event 237 or any other parameter (e.g., one or more of the entry, exit, and adherence criteria 226, 228, 224) in the structured collection procedure 70 satisfies a particular condition, so that further processing may be performed by the processor 102 if such condition(s) are satisfied. For example, such an option may be for the processor 102 to automatically send a message to the physician indicating that the patient has started the structured collection procedure 70 by meeting the entry criteria 226, or to provide a message to the patient and/or physician in the event that the patient fails the collection event 237 because the adherence criteria were not met, or to provide a message to the physician when the patient completes the structured collection procedure 70 because the exit criteria were met, or a combination thereof. For example, such an option parameter 232 may have a global list of such actions that are selected on the display 108, for example, by a selected value from a range of values associated with each option. For example, the options for each parameter may be customized by selecting from a drop-down box with option selections (e.g., 1, 2, 3, 4, 5 … A, B, C, etc.), and where, for example, option 1 is shown selected for the pre-breakfast collection event 237, which is to have the processor 102 provide a message to the physician if the patient causes the collection event 237 to fail (e.g., due to the adherence criteria not being met). One example in the context of a patient 12 who is diabetic is provided below to further illustrate the features provided on a collection device 24 according to the present invention.

A typical patient with type 2 diabetes may measure his/her blood glucose once a day after waking up in the morning. On routine work, the patient was found to have an elevated HbA1C result. The physician recommends that the person perform a three day rigorous glucose monitoring and selects a structured collection procedure that is useful for this purpose. The structured collection procedure 70 is then customized as previously discussed such that the collection event 237 is defined with a certain number of bG measurement Requirements 240 during the three days, such that it can be before breakfast and after two hours (e.g., before breakfast and after breakfast) ₁n= 2), before lunch and after two hours: ( ₂n= 2), before dinner and after two hours: ( ₃n= 2) and measuring his/her blood glucose while sleeping. Furthermore, the patient 12 may be asked to provide an assessment of the relative size of the meal ingested at the appropriate time, and an indication of how he/she feels about energy levels, by other associated requirements 240 for each collection event 237. In the illustrated embodiment of FIG. 7B, the processor 102 may collect events for each237 an indication of energy level and an assessment of the relative size of the meal ingested for every other collection event 237 (i.e. after a meal). In addition, the physician provides a condition by adherence criteria 224, wherein the adherence criteria 224 is of the collection event 237 that must be associated Period of 30 minutes (n) Meal assessments are performed so that such information is available for assessment. Such information may be used to contextualize the collected data, and may be used in the analysis performed on the collected data.

In addition, the physician will want to be notified when the patient is not able to complete the "pre-breakfast" collection event 237. Thus, to facilitate the notification options, the physician customizes the structured collection procedure 70 by setting the option parameters 232 associated with the "before breakfast" collection event by a drop-down box for "send message to physician in case of failure to comply with the criteria". The associated option parameter 232 of all other collection events 237 by default indicates that the processor 102 will not take any additional action with respect to the option parameter. It should be appreciated that the features and arrangements described above in the illustrated embodiment of fig. 7B provide a simple and convenient interface and method for customizing a structured collection procedure, such as for parameter adjustment as implemented in step 310 of the method 300 discussed above with reference to fig. 7A.

A structured collection procedure is implemented and enforced.

FIG. 8A illustrates a flow diagram of a method for implementing and performing a structured collection procedure 70 for obtaining contextualized biomarker data from the patient 12, according to one embodiment of the present invention. It should be appreciated that the plurality of structured collection procedures 70a-d (FIG. 2) specified in step 312 and implemented in step 314 (FIG. 7A) may be stored in the memory 110 (FIG. 3) of the device 24 and selected for execution at any desired time. For example, after pressing a particular combination of buttons 147, 149, the patient may select the desired structured collection procedure 70a-c and the date on which the structured test collection was started (i.e., set mode function). For example, the range of dates from which selection may be made may be from tomorrow and end 90 days from the present day, which processor 102 may also record in data file 145 (FIG. 4) as part of settings data 163. In such an implementation, the processor 102 reads the setup data 163 for the selected structured collection procedure 70 as instructed by the software 34 and indicates on the display 108 that the device 24 is in the structured test mode, for example, starting the day before the start date of the selected centralized test and until the structured collection procedure ends.

It should be appreciated that multiple structured collection procedures 70a-d may be performed sequentially or simultaneously at any given time. In one embodiment, however, the software 34 only allows the user to schedule another structured collection procedure 70 with a start date that is later than the end date of the structured collection procedure 70 currently being executed. The software 34 also allows the user to override the scheduled date for the structured collection procedure 70. If the structured collection procedure 70 is scheduled and the user enters the set mode function again, the software 34 causes the processor 102 to display the scheduled date as a default date on the display 108; if the user exits the set mode without modifying the date, the previously scheduled date remains valid. If the structured collection procedure 70 has started, the software 34 allows the user to enter the setup mode and causes the processor 102 to cancel the current structured collection procedure 70. In one embodiment, after cancellation, software 34 causes processor 102 to de-tag data records 152 in data file 145 for the data collected for the cancelled structured collection procedure 70 (e.g., invalidating unique identifier 167).

Upon reaching the start of the procedure in step 316 (fig. 8 a), the processor 102 assesses in step 318 whether the entry criterion(s) 226 selected for starting to obtain biomarker data in order to solve a predefined use case or problem (e.g., use case parameter 220) is satisfied. In step 320, the processor 102 specifies the requirements 240 for each event 237 in the schedule of events 222 of the structured collection procedure 70 according to its associated timing 238. It should be appreciated that the schedule of events 222 provides a sampling plan for biomarker data collection that is executed by the processor 102 to obtain the biomarker data in a predefined context. Upon execution of the schedule of events 222 in step 320, the software 34 causes the processor 102 to assign a unique identifier (e.g., an incremental count) 167 in the data record 152 that corresponds to each event 237 in the structured collection procedure 70. Optionally, a date-time stamp 169 may also be provided for each criterion 226, 228, 224, if desired, to indicate when the criterion was satisfied.

The adherence criteria 224 are then applied to the input (e.g., biomarker data or information) received in response to the requirements 240 in order to determine whether the received input satisfies the adherence criteria 224. When the structured collection procedure 70 has started, then in step 324, all data collected in the structured collection procedure 70 according to the requirements 240 and meeting the adherence criteria 224 (if required in step 322) are assigned (tagged) by the processor 102 in the data file 145 with a unique identifier 167. It should be appreciated that the unique identifier also serves to associate the collected data (e.g., data value 256) with its event 237, claim 240 and date timestamp 169 to indicate when the collection in response to the claim 240 was received by the processor 102. While performing the structured collection procedure 70, in one embodiment, the software 34 allows the user to perform measurements on the device 24 at any time without interfering with the onset of the illness.

In one embodiment, for non-critical measurements, the software 34 allows the reminder to "delay" the biomarker measurement for a certain period of time (such as 15 minutes) and up to a number of times as previously described. In another embodiment, biomarker measurements or data entries performed in sufficiently close temporal proximity to the claim 240 in step 320 are designed by the software 34 as valid measurements or data entries for the claim 240. Thus, the processor 102 will tag the associated data record 152 for the event 237 accordingly, using the unique identifier 167 for the biomarker measurement or data entry. In the example of biomarker measurement, if the measurement is accepted as valid for the requirement 240, the software 34 causes the processor 102 to prompt the user to enter additional information if required by the structured collection procedure 70 in order to provide a context 252 for the data resulting from the requirement 240. Such additional inputs may include, for example: an energy level rating from 1 to 5, where 1 is low and 5 is high; a meal size from 1 to 5, with 1 being low and 5 being high; and exercises of either 1 (which means more than 30 minutes) and no or 2 (which means less than 30 minutes). Such additional information or contextual information 156 is stored by the processor 102 in the data file 145 associated with the unique identifier 167 for the data event requirement 240 when entered through the user interface 146, wherein the data event requirement 240 also requires additional information in step 324.

In one embodiment, biomarker measurements determined by the processor 102 to be not close enough in time to the data event requirements 240 defined by the structured collection procedure 70 will not be tagged by the processor 102 with the unique identifier 167 in the data file 145. This is illustrated in the data file 145 shown by the absence of the requirement 240d and data value 256d (which is, for example, < null >) associated with the unique identifier 167. One example of a definition indicated by the structured collection procedure 70 and/or software 34 as "sufficiently close in time to the collection procedure" to enable the processor 102 to make such a determination may be defined with respect to a pre-scheduled time or a delayed time. For example, for pre-meal measurements, up to 15 minutes are expected to be acceptable; for postprandial measurements, up to 10 minutes is expected to be acceptable; and for sleeping measurements up to 15 minutes is expected to be acceptable. Other definitions may be provided in other structured collection procedures 70 and/or software 34.

The processor 102 then assesses whether the exit criteria 228 for the selected structured collection procedure 70 are met in step 326. If not, the processor 102 continues to execute the schedule of events 222 until the exit criteria 228 are met. After the exit criteria 228 are met, the collection procedure 70 ends in step 328. In one embodiment, the structured collection procedure 70 may also end if the entry criteria 226 are also not satisfied in step 318.

In some embodiments, the structured collection procedure 70 may be configured to be performed as: a paper tool 38; diabetes software 34 integrated into the collection device 24, such as the blood glucose meter 26; diabetes software 34 integrated into a computing device 36, such as a personal digital assistant, handheld computer, or mobile phone, 26; diabetes software 34 integrated into the device reader 22 coupled to the computer; diabetes software 34 operating on a computer 18, 25 such as a personal computer; and diabetes software 34 accessed remotely via the internet, such as from a server 52. When the diabetes software 34 is integrated into the collection device 24 or the computing device 36, the diabetes software 34 may prompt the patient to record diary information such as meal characteristics, exercise, and energy levels. The diabetes software 34 may also prompt the patient to obtain biomarker values such as blood glucose values.

Providing alternative structured collection procedures GUI An interface.

Fig. 8B illustrates a method of implementing a structured collection procedure by providing a graphical user interface on the collection device 24, which when executed on the collection device causes the processor 102 to perform the following steps. After pressing a particular combination of buttons 147, 149, the patient 12 can scroll in step 330 to a structured collection procedure 70 available for selection from a list 329 provided by the processor 102 on the display 108 of the collection device 24. If it is desired to begin the structured collection procedure, the patient 12 selects the desired structured collection procedure 70, for example, by pressing the ok button 151 in step 332. In this example, the entry criteria 226 (FIG. 6) of the structured collection procedure 70 provides information for display by the processor 102 to the user on the display 108 in step 334. After reading the displayed information, the user presses any button in step 336, where the next procedure into the criteria 226 is performed by the processor 102. In the example shown, as part of the entry criteria 226, a question is then asked by the processor 102 in step 338. If the patient 12 still wishes to begin the structured collection procedure, the patient 12 selects the confirmation button 151 in step 340; otherwise, any further pressing of the buttons 147, 149 will cause the processor to revert to the list 329, thereby stopping the setup procedure for the structured collection procedure 70.

After the patient 12 presses the confirmation button 151, the processor 102 will provide an alarm clock 343 on the display 108 in step 342 in order to set the time at which the selected structured collection procedure 70 will be started. It should be appreciated that all required events 237 for biomarker sampling, patient information, etc. are automatically scheduled by the processor 102 according to the schedule of events 222 for the structured collection procedure 70, wherein timing, values, questions, etc. may have been adjusted by the clinician 14 as previously discussed with reference to fig. 7A and 7B. Accordingly, no other parameter adjustments in the structured collection procedure 70 are required (or, in one embodiment, are not permitted) by the patient 12 other than the start time allowed by the input entry criteria 226.

In the illustrated embodiment, the patient may adjust the start time of the structured collection procedure for the next day (e.g., day 1) via buttons 147, 149 in step 344. After the start time is verified in step 346 by pressing the ok button 151, the start time is recorded by the processor 102 in the memory 110 as part of the setup data 163 in the data file 145 (fig. 4) for the structured collection procedure 70. The processor 102 then displays the selection list 329 on the display 108 in step 348, completing the setup procedure that satisfies the entry criteria 226 and indicates on the display 108 that the collection device 24 is in the structured test mode 349.

It should be appreciated that in one embodiment, multiple structured collection procedures may be performed sequentially or simultaneously at any given time, and thus in one embodiment, the pattern 349 provided on the display 108 will indicate which structured test is being performed. In one embodiment, however, the software 34 does not allow the user to schedule another structured collection procedure unless the start date is later than the end date of the current structured collection procedure being performed via the user interface 146. It should be appreciated that the processor 102 may automatically reschedule the next structured collection procedure if the current structured procedure is still running because the exit criteria 228 are not met. In another embodiment, the software 34 may also allow the user to override the scheduled date for the structured collection procedure. If the structured collection procedure is scheduled and the user again enters the set mode function, the software 34 causes the processor 102 to display the scheduled date as the default date on the display 108; if the user exits the set mode without modifying the date, the previously scheduled date remains valid. If the structured collection procedure has started, the software 34 allows the user to enter a setup mode and causes the processor 102 to cancel the current structured collection procedure if desired.

In step 350, an alarm condition 351 may be provided by the processor 102 on the previous day (represented by the notation "start") by the next day (represented by the notation "day 1") set in the aforementioned protocol. After the user selects any of the buttons 147, 149, 151 at step 352, the processor 102 provides the first scheduled event 237 as indicated by the schedule of events 222, i.e., displays information 353 on the display 108 at step 354, which the patient 12 confirms by pressing any of the buttons 147, 149, 151 at step 356. Next, in step 358, the processor 102 executes a second scheduled event as indicated by the schedule of events 222, i.e., displays the question 359 for the patient on the display 108, which the patient 12 confirms by pressing any of the buttons 147, 149, 151 in step 360. In the illustrated embodiment, the patient indicates the start time for breakfast in step 362, which is indicated by the number of minutes from the wake up alert 351 previously acknowledged in step 352. After confirming the meal start time to processor 102 by pressing confirmation button 151 in step 364, the meal start time is recorded in memory 110. For example, the meal start time is recorded by the processor 102 in an associated data record 152 in the data file 145 as data for the event 237. Further, in step 366, the processor 102 displays information to the patient 12 regarding the timing of the next scheduled event as a reminder. In step 368, upon reaching the next scheduled event indicated by the schedule of events 222, the processor 102 provides a request 240 on the display 108 to ask the patient to take a measurement (e.g., a blood glucose measurement). Further, in step 370, the processor 102 also puts forward a claim 240 for information about the size of the meal to be ingested on demand of the schedule of events 222 to provide contextual information 156 for the measured values.

As previously described, for each event, the software 34 causes the processor 102 to assign a unique identifier (e.g., an incremental count) 167 (FIG. 4) to the data of each requirement 240 provided in the schedule of events 222 that satisfies the adherence criteria 224 in the associated date record 152 for the event 237. Thus, while performing the structured collection procedure, the software 34 allows the user to perform measurements on the collection device 24 at any time outside of the schedule of events 222. Such measurements, as they are not performed according to the requirements 240, will not be rated against the adherence criteria 224 and will therefore not be provided with the unique identifier 167 in the date file, but only with the date-time stamp and its measurement value. Such data is still recorded in the data file 145 because such data may still be available for another analysis.

In another embodiment, the software 34 also allows for reminders for biomarker measurements, such as provided in step 238. For example, in one embodiment, the processor 102 provides an alarm and/or alert message via the indicator 148 and/or on the display 108, respectively, to remind of providing the measurement. For example, at time 238 for a particular requirement 240 to take a biomarker measurement (or reading), the processor 102 prompts the patient 12 by displaying a message "it is now time for you to take a reading" on at least the display. In another embodiment, an audible alarm and/or a tactile alarm (vibration) may be provided by the processor 102 through the indicator 148. For example, in one embodiment, the collection device 24 will provide such a prompt even when already powered on, such as by the patient 12 implementing an unscheduled event for another reason, for example, when within a time window in which the required measurements/readings will be taken, or will provide a reminder via the prompt by waking up even when powered off (such as in a standby mode). In another embodiment, the provided reminder or prompt may be "delayed" as previously described for a predefined period of time still falling within the time window in which the required (critical) measurements/readings are to be taken, such as 15 minutes or any other such suitable time falling within the time window. It should be appreciated that if the latency feature is for measurements/readings deemed critical to the structured collection procedure 70, such as those needed to help address a medical use case or problem, those needed to meet the adherence criteria 224, and/or those needed for some determination in a subsequent analysis, etc., the latency feature will not extend the requirements 240 beyond the time window provided by the collection procedure 70, such as by the adherence criteria 224 for the requirements 240. For example, in one embodiment, one or more events 237 within the schedule of events 222 can be predefined as critical and primary samples by providing for the use of the option parameters 232 (FIG. 7B) in the structured collection procedure 70. For example, an event 237 designated as critical is an event that cannot be missed, but if missed can be replaced by another sample already present in the data file 145. In another embodiment, the delay may be up to several times for non-critical measurements. For example, a particular event 237 in the structured collection procedure 70 may be specified as having a non-critical requirement 240 that may be delayed, such as by selecting such an option provided as one of the option parameters 232 (fig. 7B). In this embodiment, the option parameter 232 may provide, for example, a delay option and a selectable time interval (e.g., 1-60 minutes, etc.) and a selectable number of times (e.g., 1-5 times, etc.) that the user is allowed to delay the request 240. In another embodiment, the collection device 24 allows the alarm to be turned off, that is the indicator 148 may provide the reminder (audible, vibratory) for the entire time window to be closed via the user interface 146, but wherein the processor 102 still accepts the measurement/reading as long as it is taken within the time window. In another embodiment, the collection device 24 provides a skip reading option, which is also received by the processor 102 through selection entered by the user interface 146, for example, from a list of selectable options provided on the display 108 (such as delay, alarm off, skip reading), wherein again no reminder/reminder will be provided because the patient 12 has indicated to the processor 102 that he/she does not want to take the particular measurement/reading requested. It will be appreciated that selecting the skip reading selection option may result in adherence to event 242, and thus further processing, if adherence criteria 224 has been associated with event 237 prompting requirements 240, as already discussed in the preceding section.

In another embodiment, the adherence criteria 224 may require that the biomarker measurements be performed within a time sufficiently close to the data event requirements 240. Thus, if such biomarker measurements are taken within a time period specified by the adherence criteria 224, the processor 102 may indicate that the measurement or data entry for the event is acceptable and tag (i.e., assign a unique identifier 167) the value of the biomarker measurement or data entry in the data file 145 accordingly. In the example of a biomarker measurement, if the measurement is accepted as valid for the data event requirement 240 (i.e., the adherence criterion(s) 224 is satisfied), the schedule of events 222 may cause the processor 102 to prompt the user to enter additional information (if required by the structured collection procedure 70), such as the provision of contextual information 156 (i.e., context) for the measurement received in response to the requirement 240 as previously mentioned with respect to step 370.

Upon entry of such contextual information 156 through the user interface 146, it may be stored by the processor 102 in the data file 145 associated with the unique identifier 167 for the data event requirement 240 that requires the additional information. Biomarker measurements determined by the processor 102 to be not close enough in time to the data event requirements 240 defined by the adherence criteria 224 will not be tagged by the processor 102 in the data file 145. This is illustrated in the illustrated data file 145 (FIG. 4) by the absence of a data event requirement 240d and a data value 256d associated with the unique identifier 167. One example of a definition indicated by the adherence criteria 224 to be "close enough in time to the collection procedure" for the processor 102 to make such a determination may be defined relative to a pre-scheduled time or a time delayed. For example, for pre-meal measurements, up to 15 minutes are expected to be acceptable; for postprandial measurements, up to 10 minutes is expected to be acceptable; and for sleeping measurements up to 15 minutes is expected to be acceptable. Other definitions may be provided in other adherence criteria 224 to other events in the schedule of events 222 and in other structured collection procedures.

In the illustrated embodiment, the user scrolls to a selection using the buttons 147, 149, which is entered by the processor in the data record 152 for the associated demand 240 by pressing the confirmation button 151 in step 372. In one embodiment, the meal size may be represented by a range of numbers, for example from 1 to 5, with 1 being few and 5 being many. In the illustrated embodiment, additional input is required in step 374 regarding the contextual information 156 of the energy level rating from 1 to 5, where 1 is low and 5 is high, which is input in step 376 in the data file 145 as previously described by receiving input by the processor 102 to the requirement 240 made with the user interface 146. In other embodiments, other contextual information 156 may include information indicating whether the patient has exercised and/or for how long. For example, the user interface 146 may be used, where a yes or 1 means more than 30 minutes, and a no or 2 means less than 30 minutes. In the illustrated embodiment, since the exit criteria 228 are now satisfied by the successful performance of the steps 368-376, the structured collection procedure 70 ends in a step 378 where the processor 102 again displays the list 329 so that the patient 12 can perform other tasks on the collection device 24 as desired. Reference will now be made to fig. 9.

A method of contextualizing biomarker data.

Fig. 9 depicts a method 388 for contextualizing biomarker data for diabetes diagnosis and therapy support in accordance with an embodiment of the present invention. It should be appreciated that in the embodiments discussed above with reference to fig. 8A and 8B, the contextual information 156 is automatically required by the processor during the structured collection procedure 70 and recorded along with the associated biomarker values. In embodiments where such automation is not provided on the collection device 24 and the patient is using the paper tool 38, however, the collected data may be associated with its contextual information 156 at a later time (e.g., after the structured collection procedure 70 is performed to generate at least the data event values 256 in step 390). If not already done by the collection device 24 (such as in the example of a device with limited memory and processing power or when recorded on the paper tool 38), the data may be provided to another device 18, 25, 36 that is running the software 34 and has the capability to associate at least the data event value 256 (FIG. 4) with its corresponding data event requirement 240. This association of at least the data event value 256 with its corresponding data event request 240, date time stamp 169, and context information 156 results in contextualized (self-monitored) data 170 in step 392.

It should be appreciated that the data used in the structured testing according to the present invention should correspond to the expected collection of contextualized data. Considering fig. 10A, in this example the inherent advantages of context become apparent when considering the utility of a subway map with no context on the left hand side and a subway map with context on the right hand side, which makes it possible to easily navigate the system and travel from one location to another. In a similar manner, contextualization can play an important role in diabetes. The context associated with the data may be, for example, due to therapy, events (such as meals, exercises, events 237, requirements 240, etc.), and required times (e.g., timing 238) for the data collection itself. Thus, any data collected by a patient having measured values may be contextualized by being associated with one or more of the aforementioned factors, such as therapy, event, and time, each of which is discussed further below.

Therapy may be defined, for example, as an ongoing treatment intended to alleviate impaired glucose control in a patient. The treatment usually involves antidiabetic agents such as insulin, oral medications, and diet and exercise. Due to the different mechanisms of action, one treatment (or combination of treatments) has a specific pharmacodynamic effect on the blood glucose of a patient. A change in the dose(s) of the therapy(s) or a change in the therapy(s) itself will result in a change in the glucose control of the patient. Thus, the bG data collected has a strong correlation to the underlying therapy and dose, and this information is used to contextualize the data. Variations in dosage or treatment will lead to different situations. It should be appreciated that the therapy context may be set by the clinician 14 by consulting the patient when designing the collection procedure 70, such as discussed above with respect to fig. 5A.

In one embodiment, the events 237 in the collection procedure 70 may include specific conditions surrounding the bG measurement points that play a role in altering the normal glucose levels of the patient. As previously described, the event 237 may be meal or exercise based and relevant to data contextualization. In this context, the underlying assumption is that the patient operates more or less under a well-defined schedule. In creating the collection procedure 70, the patient 12 may discuss lifestyle events with the clinician 14 so that the collection procedure 70 may be adjusted according to the needs of the patient 12. As an example and referring to FIG. 10B, consider a typical collection procedure 70 in which the patient 12 does not exercise regularly, so most events are meal-based events, including breakfast, lunch, and dinner. Such lifestyle of the patient 12 results in six candidate bG measurement points (pre-and post-meal for each meal) for the schedule of events 222 in the collection procedure 70. During the process of creating/customizing (fig. 5A and/or fig. 7B) the collection procedure 70, the clinician 14 may specify that the patient collect one or more of all of these points according to the schedule of events 222 of the collection procedure 70. Any data collected other than these points (i.e., outside the requirements of the collection procedure 70) may be classified by the processor 102 as a non-collection procedure reading. In a similar manner, for type 1 diabetic patients who exercise regularly, the clinician 14 may adjust/customize the collection procedure 70 to include additional measurements around the exercise event. The event information is then used in this example to contextualize the data in an appropriate manner depending on the event 237.

The time represents the actual time at which the measurement was taken and has an absolute term, such as a date-time stamp 169 (fig. 4). Furthermore, time, i.e. an offset from a particular event, may also be represented by an offset. As an example, a post-meal reading is taken at a particular time after a meal, and that time may be different for different days. This occurs because the patient may not be able to take event-based readings at the same time each day. Thus, there are time distributions in which the same measurement is made on different days. Knowledge of this distribution may be used to analyze the timing and parameter timing 238 in the collection procedure 70.

Further, using contextualized data 170, the physiological state of patient 12 at the time of measurement can be described. The patient's physiological state may affect the biomarker values, and thus knowledge of the patient's physiological state is helpful in understanding the biomarker values. The biomarker data may be contextualized because the biomarker data is collected in the context of a predetermined event, such as the duration of a meal, meal type, meal distribution, exercise information, sleep quality, sleep duration, wake-up time, and stressful factors such as illness, and so forth. The time-resolved data allows the biomarker data to be interpreted in context with other information, such as compliance with the structured collection procedure 70 and patient lifestyle events.

Referring again to FIG. 9, the contextualized data 170 is assessed in step 394 using the adherence criterion(s) 224 to generate accepted contextualized data 395 that satisfies the adherence criterion 224. Because the adherence criteria 224 may provide a basis for comparing the data event values 256 to the criteria, which may be accepted and used or rejected and not used, in one embodiment, the adherence criteria 224 may be used to filter the data. In another embodiment, step 394 may precede step 392.

FIG. 11, for example, shows a graphical representation of accepted contextualized data 395 blended with unacceptable contextualized data 397. The vertical axis of the figure shows a biomarker value 256, which includes context 252 in the form of a biomarker set point, a biomarker upper limit, and a biomarker lower limit. The horizontal axis of the graph shows delivery times 238 of measurement requests 240 and sleep period events 237 where actual sleep exceeds the recommended minimum amount of sleep shown in dashed lines. The accepted contextualized data 395 is data that satisfies the adherence criteria 224. The unacceptable contextualized biomarker data 397 either are not within the structured collection procedure 70 or do not meet the adherence criteria 224. Accepted contextualized biomarker data 395 may help improve decision making by excluding unacceptable contextualized biomarker data 397. Statistical techniques can be used to view the accepted contextualized biomarker data 395 in a form that conveys additional information to the clinician 14. Examples of statistical techniques include regression methods, analysis of variance, and the like. Additional details regarding another embodiment of the software 34 will be provided below.

And (3) software.

As mentioned in the preceding section, the software 34 may operate on the patient computer 18, the collection device 24, a handheld computing device 36 (such as a laptop computer, personal digital assistant, and/or mobile phone), and a paper tool 38. The software 34 may be preloaded or provided via computer-readable media 40 or provided via public network 50 and loaded for operation on patient computer 18, collection device 24, clinician computer/office workstation 25, and handheld computing device 36 as desired. In other embodiments, the software 34 may also be integrated into a device reader 22 coupled to a computer (e.g., computer 18 or 25) for operation thereon, or may be accessed remotely over the public network 50, for example, from a server 52. Further, the one or more collection procedures 70 may be provided as part of the software 34, as updates to the software 34, or as individual files that may be operated on and used by the software 34.

In the embodiment discussed below, the software 34 runs on the collection device 24 and provides three basic elements: one or more structured collection procedures 70, a data file 145, and one or more scripts. Since the features of the structured collection procedure 70 and the data file 145 are the same as previously discussed, further details will not be provided. The one or more scripts are small, independent programs that reside on the collection device 24 and each can perform a particular set of tasks, respectively. Such scripts may include, for example, protocol scripts 401, parsing scripts 403, and analysis scripts 405 depicted by FIG. 12, each of which will be discussed in detail in the following paragraphs.

A protocol script.

The protocol script is a script that actually implements the execution of the collection procedure 70 by the processor 102 on the collection device 45. Upon initiation of the collection procedure 70, the protocol script, in one embodiment, causes the processor 102 to create a data structure that summarizes the expected amount of data summarized by the collection procedure 70. In another embodiment, the data structure may be of variable size or of fixed size but with a buffer (e.g., an array in the data structure) if additional data is to be collected during the collection procedure 70. Having a buffer in this way allows for, for example, situations when the collection protocol 70 can be extended to, for example, the maximum memory size that can be allocated for the data structure in the memory 110 of the collection device 24, if such extension is desired or needed due to the desired condition not being met (e.g., the patient biomarker values do not reach the desired values). The data structure, such as data file 145, stores at least the time of initiation of the collection procedure 70, the actual measurement of the biomarker (such as data event value 256) and the time of measurement (such as date time stamp 169), and optionally all other information used for additional contextualization, such as contextual information 156 and requirements 240 such as meals, workouts, etc. As an alternative embodiment, the data structure may also be considered a calendar, which is generated by the clinician 14 and may include details regarding the date and time of day at which measurements need to be taken. The calendar also features a feature that allows the patient to easily see when he or she must take the next measurement. The protocol script also causes the processor 102 to perform all of the functions necessary for the collection procedure 70 to be performed by the processor 102. Once the appropriate data is collected (e.g., the collection procedure 70 is successfully run), the protocol script causes the processor 102 to mark the data structure with the done flag 257 in one embodiment, or in another embodiment provides it as a status condition for the software 34 and passes control of the processor 102 provided in the software 34 to a parse script. In the previous embodiment, the completion flag 257 may also be used to provide information about the reason for the end/termination, to identify the type of completion (end, logistics (timeout), adherence termination, etc.). For the latter embodiment, since one or more structured collection procedures 70 may be loaded onto the collection device 24 at the factory as previously mentioned, by providing status conditions in the software 34 for each collection procedure 70 helps support the requirements that the procedure is only available after authorization by the clinician 14. In one embodiment, the state conditions for each collection procedure 70 may be tracked by software 34 and may include one or more of a "sleep" state, an "authorized" state, a "pending" state, an "active" state, and a "completed" state. The dormant state is useful when shipping a collection device 24 having one or more embedded collection procedures 70, but until authorized for use (such as described above), it cannot be used (or seen) by the patient 12 on the collection device 24. In this case, the collection procedure 70 is said to be dormant. The authorized state is the state when the collection procedure 70 becomes available after the clinician 14 authorizes its use on the collection device 24. During this state, the collection procedure 70 may be configured (e.g., by a clinician) and initiation of start is also initiated as configured, for example, by selection of the clinician, the patient 12, or by a start date. The pending state is a state when the start date is set but before execution, for example in which the collection procedure 70 waits for some unknown time before executing the schedule of events 222 until the entry criteria 226 are met. In one embodiment, once the collection procedure 70 begins at or after the start date by meeting the entry criteria 226, the collection procedure is said to be in an active state with at least the schedule of events 222 being implemented by the processor 102. When the collection procedure 70 ends as previously described, the completed status functions in a similar manner as the done flag 257.

And analyzing the script.

Parsing scripts are scripts that cause the processor 102 to parse contextualized data, such as contextualized data 395 (FIG. 11), once data collection of the collection procedure 70 is complete. The parse script causes the processor 102 to attempt to resolve any anomalies that may occur while the collection procedure 70 is being executed (e.g., in real-time while the procedure 70 is being executed), such as, in one embodiment, only critical data events 237 in the collection procedure 70 (e.g., mandatory data collection for biomarker values). If at the end of execution of the parse script, there is still an exception to at least the mandatory data required by the collection procedure 70, the parse script will cause the processor 102 to indicate that the appropriate data has not been collected. Thus, the collection procedure 70 is marked as incomplete by the processor 102 not providing a complete flag 257 in the data file 145. If there is no exception at the end of parsing the script, e.g., at least for critical events in one embodiment, and/or events marked as primary samples in another embodiment, and/or all events in another embodiment, the collection procedure 70 is marked as complete by providing a complete flag 257 by the processor 102 in the data file 145 containing the collected and contextualized data. The role of the parsing script will be explained later in another embodiment illustrating the execution phase.

The script is analyzed.

The analysis script causes the processor 102 to analyze the completed collection procedure 70 with its own associated data set (e.g., data file 145). The analysis performed by the processor 102 according to the analysis script may be simple (glucose mean, glucose variability, etc.) or may be more complex (insulin sensitivity, noise assessment, etc.). In one embodiment, the collection device 24 may perform the actual analysis by itself, or the analysis may be implemented on a computer such as computers 18, 25. In one embodiment, the results from the analysis script may then be displayed by processor 102 on display 108 of collection device 24 or on a display of a peripheral device. Program instructions and scripts of the software 34 are discussed below with reference to fig. 13 and 14 and fig. 2 and 5B.

The collection procedure is performed.

Fig. 13 and 14 depict a collection procedure execution method 400 performed by the processor during the collection procedure 70 using the aforementioned script in accordance with the program instructions of the software 34. The dotted lines mark the boundaries between the different domains of different scripts and are the boundaries where control exchanges occur. It should be appreciated that the below disclosed embodiments of the present invention may be implemented on a blood glucose measuring device (such as a meter) capable of accepting one or more structured collection procedures 70 and the associated meter executable script discussed above.

Referring first to FIG. 13, once the collection procedure 70 is initiated by the processor 102 using the protocol script 401 on the collection device 24 in step 402 (such as in any of the manners discussed above in the preceding section), after the entry criteria 226 are met (if provided in the collection procedure 70), data event instances (e.g., events 237) occur in accordance with the schedule of events 222 in step 404. For event 237, in this example, the processor 102 prompts by a request 240 to take readings for the patient 12 around a lunch event as forced by the collection procedure 70. For example, the prompt for the claim 240 may be an alert provided by the processor 102 via the sounded indicator 148, wherein the patient 12 is asked by the processor 102 to take a reading on the display 108. In one embodiment, the software 34 provides a delay feature and a skip reading feature, wherein the patient 12 can use the user interface 146 to effect delaying or skipping data collection. For example, by selecting the delay feature previously discussed in the preceding section, the processor 102 may be caused to prompt the patient 12 again for the event 237 after a predefined amount of time to effect a delay for data collection. For example, in one embodiment, such a feature may be used in the event that the patient 12 is unable to take a reading at the prompted time, such as at the beginning of a time window in which the measurement/reading is provided. Likewise, the skip feature will be selected if the patient believes he/she cannot perform the measurements/readings within the time window. FIG. 10B shows an example of a time window or a particular time window surrounding an event ("allowable window").

In one embodiment, the processor 102 then uses the adherence criteria 224 to determine whether data collection for the event 237 was successful due to the conditions satisfying the adherence criteria 224 according to the protocol script 401 in step 406. For example, if the patient 12 successfully collects data within the specified time window, a successful data collection will occur. In another embodiment, the same process may be applied to one or more sample groups 262. The data successfully collected for the events in the schedule of events 222 and/or sample packets 262 is then contextualized by the processor 102 in step 410 according to the protocol script 401, for example, by associating the collected data (e.g., data 256) with the current time (e.g., date-time stamp 169 (fig. 4)), the event 239 and/or the claim 240 and, for example, context information 156 available regarding the patient therapy and the unique identifier 167 (if needed) in the data file 145, as also previously discussed in the preceding section.

In the previous example, if the patient 12 is not able to collect data within the specified time window, the processor 102 scans the contextualized data residing on the collection device 24 according to the protocol script 401 in step 412 to determine if there are available similar data points that meet the requirements of the missed data point. The data point will only be selected by the processor 102 in step 414 according to the protocol script 401 if all requirements of the data point to be collected are met.

As an example, if the collection procedure 70 requires paired measurements, i.e., pre-meal and post-meal measurements, it is important that both measurements be taken around the same event. In this case, it is not allowed to replace any one value from the previous value; if this happens, an exception is flagged for the event under consideration. In this case, the relevant element in the data structure is not completed at that location, where processor 102 would declare an exception in step 416, such as providing a < null > value to unique identifier 167 in the particular data record 152 for the event 237 that caused the exception. If no such constraints exist, data points from the data residing on the collection device 24 may be selected by the processor 102 in step 414 and added to the contextualized collected data in step 410. The replacement data points will have the same context information, event context and were collected within the specified time window of the original collection time period, if so required. In step 418, the processor 102 will check whether data collection is complete for all events 237 in the schedule of events 222 of the collection procedure according to the protocol script 401. The processor 102 also checks if the exit criteria 228 are met if the collection procedure 70 provides the exit criteria 228. If not, the processor 102 continues with the next event in the schedule of events 222 by returning to step 404, wherein data collection is then continued for the remainder of the collection procedure 70 in a similar manner. It should be appreciated that frequent messages may be displayed by the processor 102 to the patient 12 on the display 108 as part of the guidance 230 of the collection procedure 70 to provide guidance to the patient throughout the data collection process. It should be appreciated that as part of the protocol script, the processor 102 may end the collection procedure 70 in one embodiment, or in another embodiment give the patient the option of selecting to end the collection procedure 70 on the display 108, whenever any specified exit criteria are met. Once data collection is complete in step 418, the protocol script 401 then hands over control of the processor 102 to the parsing script 403 in step 420.

Referring to FIG. 14, which highlights the role played by the parsing script 403 in passing control after the collection procedure 70 is completed, the parsing script 403 checks whether the contextualized data 170 in the data file 145 is not completed. To accomplish this, processor 102 reads contextualized data 170 from memory 110 in step 422 and looks for any exceptions (e.g., < null > values for any unique identifier 167) provided in data file 145 as an exception check in step 424 according to parsing script 403. When possible, the processor 102 utilizes the data available on the collection device 24 in step 426 to attempt to resolve any of these exceptions. As one example, applicable data may be obtained from non-collection procedure 70 events or from data collected as part of another collection procedure 70. If the exception cannot be resolved from the existing data, the collection procedure 70 is marked as incomplete in step 428. At this point, the done flag 257 for the collection procedure 70 is set to incomplete (e.g., unset, < empty >, a predefined value, etc.). Otherwise, if there are no exceptions and/or all exceptions are resolved in step 426, the processor 102 sets the completion flag 257 to completed and may then display the results of the collection procedure 70 in step 430. Processor 102 then collects all data associated with collection procedure 70 (i.e., data file 145) according to parsing script 403, and hands control over to analysis script 405 in step 432.

In step 434, if the done flag 257 is marked as completed in the data file 145, the analysis script will cause the processor 102 to perform all necessary analysis on the data collected in step 432, such as the analysis 258 detailed in the collection procedure 70 (FIG. 6B). In one embodiment, simple analysis routine calculations may be performed on the collection device 24, while the analysis may be performed on a computer, such as computer 18 or 25, for more complex collection procedures 70.

When a collection device 24 containing one or more collection procedures 70 is connected to a device reader 22, such as a Smart-Pix device, connected to the computer 18 or clinician computer 25, the software 34 causes the associated processor to automatically display a list of completed collection procedures 70 and their associated data files 145.

In one embodiment, the software 34 may interact with a device reader 22, such as provided as a SmartPix device, for visualizing the results, or any other device (including computers 18, 25, etc.) capable of displaying the results of the analysis of the data from the collection procedure 70. At this point, if on the clinician computer 25, the clinician 14 may decide to view the results of the completed and analyzed collection procedure 70 or to conduct an analysis on the completed collection procedure 70. The clinician 14 may also review any collection procedures 70 that have not been completed and attempt to assess abnormalities present in the collection procedures 70. This interaction gives the clinician 14 the opportunity to give feedback to the patient regarding his data and/or assess why the existing collection procedure(s) 70 could not be completed.

Example of use.

Referring to FIG. 15, an example of use is provided in which the sequence of actions performed by the clinician 14 and the patient 12 are highlighted. The sequence contains an overview of clinician 14-patient 12 interactions from the formulation of medical questions to completion of the collection procedure 70. The dashed line indicates the boundary between the clinician 14 domain and the patient 12 domain, and this is also the boundary at which information exchange occurs. The discussion regarding the completed collection procedure 70 may also be used to encourage the patient and give the clinician 14 the opportunity to provide feedback regarding patient performance and progress.

In step 440, the patient visits the clinician 14, and in step 442, the clinician identifies a problem, resulting in the selection of a medical use case (medical problem) in step 444. After selecting a medical question, for example, on the computer 25, the clinician uses the computer to select and define/customize the structured collection procedure 70 in step 446 using the methods 200 and/or 300 (fig. 5A and 7A). After the structured collection procedure 70 is specified, the computer 25 provides the structured collection procedure 70 to the collection device 24, which is received in step 448. After the entry criteria 226 provided in the procedure 70 are met in step 450, the patient 12 begins data collection according to the structured collection procedure 70 using the collection device 24. During data collection in step 452, individual events 237 are automatically scheduled by the collection device 24 according to the schedule of events 222 contained in the structured collection procedure 70. The adherence criteria 224 are applied at least to all biomarker measurements that are automatically assessed and recorded by the collection device 24 to satisfy the adherence criteria. In step 454, once the exit criteria 228 are met, the structured collection procedure 70 is completed. Next in step 456, the patient 12 may perform any available collection device-based analysis 258, if desired. A report, such as the data report mentioned in step 434 (fig. 14), may also be generated next in step 458. In step 460, data (e.g., completed data file 145) from the collection device 24 or from the patient computer 18 is preferably sent to the clinician computer 25. The collected data is received in step 460 and then analyzed in step 462. Next, a report may be generated in step 464, which may be used to facilitate discussion with the patient 12 regarding any additional outcomes in step 466. The document is next printed in step 468, which may be provided to the patient 12 in step 470, and may be recorded (stored) in the electronic medical record of the patient 12 in step 472.

Generation, modification and transmission of collection procedures.

Embodiments of the present invention also enable the generation, modification, and transmission of collection procedures 70 to/from devices that enable structured testing, such as collection device 24. Since the collection procedure 70 originates from, and is intended to address, a particular medical use case or issue, the transfer of the resulting information (e.g., data file 145) from one device to another is conducted in a secure manner. Further, a method is provided in which all collection procedures relating to information (e.g., data files 145) for a patient or a group of patients may be managed in a safe and efficient manner.

It should be appreciated that the discussion provided below includes various aspects of interaction between the clinician 14 and the patient 12 discussed above with respect to FIG. 15. In particular, the following disclosure provides details regarding the infrastructure required to manage the generation, transmission, and analysis of the collection procedure 70. Reference is also made below to the system 41 of fig. 2 because various aspects are provided relating to the transfer of devices and information (data, reports, etc.) to and from the devices 18, 25 and 52.

In one illustrated embodiment, system 41 may include: a server 52, which is a web server of software 34 residing on the clinician computer 25, acting as a repository for a plurality of collection procedures 70a, 70b, 70c, and 70 d; and a collection device 24 provided, for example, as a blood glucose meter. Accordingly, these components will be referred to as "servers", "software", and "meters", respectively. Further, the computer 25 on which the software 34 resides is referred to as a "client".

In one embodiment, the server 52 may act as a central repository for a plurality of collection procedures 70a, 70b, 70c, and 70d for solving a particular medical problem. Accordingly, one or more collection procedures 70 may be downloaded from the server 52 to the clinician computer 25. In such an embodiment, all communications between the server 52 and the client computer 25 are in a secure and web-based format. Furthermore, in another embodiment, there is not a full two-way data transfer between the computer 25 and the server 52, so that it is not possible to transfer patient data to the server 52. Furthermore, in other embodiments, the requirements for the collection procedure from the server 52 can only be set forth with a valid identifier. Such an embodiment ensures that only authorized clients are allowed to access the server 52 to download the required collection procedure(s) 70.

In one embodiment, each collection procedure 70 downloaded from the server 52 can only be used once (e.g., if the completed flag or status is set, the procedure 70 cannot be run again until re-authorized by the clinician 14). Access is required from an authorized client user with a valid ID 71 (fig. 2) for each successive download of the collection procedure 70. The server 52 also provides updates to the client computer 25 to ensure that the software is up-to-date. There are also limitations on the communication from the client computer 25 to the server 52. The server 52 has access only to information relating to the installed version of the software 34. It is not possible for the server 52 to access any data residing in the client database (e.g., memory 78). In addition, the data on the client computer 25 is access controlled so that it cannot be used and accessed without the necessary permission.

The software 34 residing on the client computer 25 acts as an interface between the server 52 and the collection device 24. The software 34 at the front end includes a user-friendly interface that provides the clinician 14 with prepared information relating to the general clinic. This information may include details about all assigned patients, details about patients that the clinician 14 is scheduled to visit on a given day, and details about patients that require additional attention. The software 34 also interfaces with a database that includes pertinent patient data arranged by individual patient IDs, such as used by and provided in the healthcare record system 27. The software interface also allows the clinician 14 to access details of the patient 12 using the patient identifier. In this manner, the software 34 provides the clinician 14 with information regarding the collection procedure(s) 70 that the patient 12 has completed (i.e., those collection procedures for which the completion flag 257 is set to completed), the associated results, and the collection procedure(s) 70 that the patient 12 is currently performing. All data residing on the client computer 25 is secure and access controlled. The server 52 cannot access the data. The clinician 14 may access data from all patients in the clinic. In addition, an individual patient 12 may access his data (e.g., from a clinician's server) in a secure web-based format using his patient identifier. This data is downloaded from the collection device 24 to a database on the computer 25 and associated with the patient 12 using the patient identifier.

The software 34 also performs an analysis on the data as it is downloaded from the collection device 24 to ensure that data integrity is maintained and that no data corruption occurs upon transfer. The client computer 25, by means of the software 34, may also send emails to the individual patients, and these emails may contain information about the upcoming appointment, reminders as to what the patient should do after the appointment, and reports on the results of the completed collection procedure 70. When the clinician 14 downloads the collection procedure 70 for a particular patient from the server 52, the collection procedure 70 is associated with the patient identifier. This makes it possible to grasp which collection procedures 70 are currently in progress for their patients.

The downloaded collection procedure 70 may be modified by the clinician 14 using the software 34 to tailor the collection procedure 70 to the needs of individual patients as discussed in the previous section (fig. 7B). In modifying the collection procedure 70, the clinician 14 also has the option of altering the analysis that will be conducted on the modified collection procedure 70. Further, even for standard collection procedures 70 that have not been modified, the clinician 14 has the option of adding additional options for analysis.

In addition, the clinician 14 may decide and set guidelines as to when the procedure 70 must be terminated. For example, the clinician 14 may decide and set how many compliance violations are allowed, i.e., how many measurements the patient may miss, e.g., by using the option parameters 232 in the collection procedure 70.

In one embodiment, once the collection procedure 70 is introduced into the collection device 24 by the clinician 14 (details are discussed in the next chapter), the collection procedure 70 cannot be altered by the patient 12. In addition, the collection procedure 70 is associated with both the clinician 14 (prescriber) and the patient identifier in order to ensure that the collection procedure 70 and associated data (e.g., data file 145) are mastered.

The software 34 also allows the clinician 14 to select the type of report that will be generated once the completed collection procedure 70 has been analyzed. The report is adjusted for the device on which it is to be viewed. The report may be for example for a mobile device such as a telephone, palm device or meter, or for a computer, or for a printed format. The software 34 also has the capability to interface with an electronic medical record system to add patient data to the medical record as well as the results of the analysis performed on the data from the collection procedure 70.

The collection device 24 serves as a mechanism by which prospective and contextualized data is collected by the patient 12 as recommended by the collection procedure 70. The collection device 24 may be owned by the patient, or it may be owned by the clinician 14 and lent to the patient 12 during data collection associated with the collection procedure 70. The clinician 14 may introduce the collection procedure 70 into the collection device 24 by several mechanisms. For example, in one embodiment, the collection procedure 70 may be downloaded from the server 52 and added to the collection device 24 via a connection cable linking the client computer 25 to the collection device 24. In another embodiment, the collection procedure 70 may also be obtained on a chip (e.g., the computer-readable medium 40) that may be inserted into the collection device 24. The collection procedure 70 is then loaded into the firmware of the collection device 24, where it may be initiated by the patient 12. In another embodiment, the collection procedure 70 may also be introduced using an RFID tagged chip (e.g., a computer readable medium).

Along with the downloaded collection procedure 70, the collection device 24 also has the ability to display instructions to the patient 12 to provide guidance to the patient at the time of data collection. Further, as discussed previously, the collection procedure 70 may introduce both a patient identifier as well as a clinician identifier into the collection device 24. Similarly, the data collected from the collection device 24 can be associated with a patient identifier and a clinician identifier, such as part of the setup data 163 (FIG. 4) in the data file 145. In addition, the setup data 163 in the data file 145 may also include information about the collection device 24 (i.e., measurement noise, calibration data) as well as the strip lot number and other information about the strip used for any data collection event 237. Such information may be helpful in data analysis.

Upon completion of the collection procedure 70, the collection device 24 may be connected to the software 34. At this point, data, such as data file 145, is securely transferred and stored by processor 76 of client computer 25 in accordance with software 34 running thereon. After the analysis performed on the data from the collection procedure is completed by the software 34 on the client computer 25, the computer 25 also has the ability to store the results of the analysis for reference by the patient. Reference will now be made to fig. 16-18.

Software GUI 。

In one embodiment, a typical workflow highlighting further features of the software 34 that may be used through a graphical user interface (GUI 500) provided on a computer, such as the computer 25 and/or the server 52, is presented. Considered in this example is the typical situation when the clinician 14 opens an instance file for a particular patient. As shown in FIG. 16, the clinician 14 may readily visualize important details about the displayed patient file 502 using the GUI 500 of the software 34 running on the client computer 25. On a top pane 502 of the GUI 500, the clinician 14 can see and use various administrative tasks 504, such as changing a displayed patient file, creating an email containing information from a patient file, creating a fax containing information from a patient file, saving a patient file, bookmarking data in a patient file, selecting an existing bookmark, printing information/charts from a patient file, and so forth.

On the left pane 506 of the GUI 500, the clinician 14 has additional options 508, such as an option to download patient data, such as data files 145, when the collection device 24 is connected to the computer 25 or 18 (either by wired or wireless means). Other options 508 may also include viewing details about patient profiles, logs and additional records, charts based on calculated data, and so forth. As shown in fig. 16, a summary option is selected, which shows its contents in the main pane 510.

Main pane 510 designates all typical steps in the workflow for therapy management of patient 12. These steps may include the following: disease state 512, therapy selection 514, therapy initialization 516, therapy optimization 518, and therapy monitoring 520. Each step provided as an icon on GUI 500 is discussed later.

Disease state 512 is a determination of the disease state, e.g., whether the patient is a type 1 or type 2 diabetic. Generally, the disease state determination is performed when the patient 12 visits the clinician 14 for the first time or when the clinician 14 suspects that a particular patient may be at risk. Once the disease state is determined, a therapy selection 514 follows, and the clinician 14 needs to select an appropriate therapy that takes into account the patient's disease state. Since therapy selection 514 may include the processing of methods 200 and 300 shown in fig. 5A and 7A, respectively, further discussion will not be provided. Therapy initialization 516 is a therapy initialization process that involves establishing initial details by which a therapy is administered to the patient 12. This aspect may include details regarding the initial dosage of the therapy, the time at which the therapy is administered, etc. Further details regarding therapy initialization 516 are provided later with reference to fig. 17. Therapy optimization 518 involves determining the optimal effective dose for the patient so that it does not cause side effects. Finally, therapy monitoring 520 involves routinely monitoring the patient 12 to detect that the therapy is outdated after the selected therapy is optimized. Thus, the GUI 500 provides the clinician 14 with all of the useful information in a user-friendly format.

Fig. 17 represents the situation when the clinician 14 has determined a disease state by disease state 512 and therapy selection 514 and has selected a therapy and is at the step of therapy initialization 516. As shown, the software 34 obscures completed steps in the GUI 500, with only the currently in-progress step (e.g., therapy initialization 516) highlighted. Furthermore, in one embodiment, the software 34 does not allow the clinician 14 to proceed to the next step without completing all required actions in the current step (in other words, all previous steps have been completed). However, the software 34 also provides the clinician 14 with the option of returning to and modifying the previous step by selecting a particular icon for the step in the GUI 500.

In this example, the patient 12 is a diabetic patient, and the clinician 14 needs to initialize long-term-action insulin therapy for type 1 diabetic patients currently for therapy initialization 516. As shown, all available initialization options 522 for initializing the therapy are presented to the clinician 14 for this step on the GUI 500. For example and as shown, the clinician 14 may select a certain type of medication 524 (such as basal insulin, which is shown to be long acting) and select protocol selection icons 526 associated with the medication 524, and each of the protocol selection icons is associated with a collection protocol 70 that may be used to address therapy issue(s) with respect to a particular medication (e.g., insulin glargine (Lantus), insulin detemir) listed (and available) in association with the type of medication 524. The software 34, via the GUI 500, also allows the clinician 14 to decide whether additional therapy related parameters 528, such as insulin sensitivity, insulin to carbohydrate ratio, etc., should be retrieved when needed. Further details for therapy initialization can also be viewed by selecting icon 530 for general information.

When the clinician selects one of the protocol selection icons 526, the software 34 provides a snapshot 532 of the set of conditions in the associated collection protocol 70, as shown in FIG. 18. Typical initial conditions provided in snapshot 532 may include: dose frequency (dose modulation), (default) start dose, target level, schedule of events (e.g., measure fasting glucose for 3 days), recommendations for calculations (e.g., modify drug dose based on 3 day median, measure remaining days to assess effect), and so forth. If multiple details about the selected collection procedure 70 are desired (such as relevant medical literature), example studies and the like that may form the basis for the structured test procedure may be viewed through multiple detail icons 534. The clinician 14 may also choose to accept the provided collection procedure 70 via the accept icon 536 or propose a modification to the collection procedure 70 via the modification protocol 538. A screen representation of all parameters for making a modification in procedure 70 may be opened, for example, on GUI 500 by selecting modification protocol 538, such as depicted in fig. 7B, and no further discussion is provided since it has been previously discussed in the preceding section. Once the collection procedure 70 has been modified, the clinician 14 may review and accept the changes. After accepting the collection procedure 70 by selecting the accept icon 536 on the GUI 500, the software 34 causes the processor (e.g., the processor 76) to send the completed collection procedure 70 to the collection device 24, as previously discussed in the previous section. Specific advantages of the foregoing embodiments of the invention will be mentioned below.

Although not limited thereto, embodiments of the present method provide the advantages mentioned below. Particular embodiments enable contextualization of the collected data by taking into account factors such as meals and existing medications. All data analysis can be performed on the expected data, that is to say contextualized data collection taking into account the medical problem that needs to be solved. Each of the individual collection protocols 70 is directed to collecting bG data in order to address specific medical problems, such as controlling postprandial glucose excursions, adjusting fasting glucose values, characterizing a patient's insulin sensitivity, monitoring a patient's therapy response, and so forth. Using such a collection procedure allows the task of collecting BG values to have a target orientation because the patient knows why he or she is conducting the test. It is believed that knowing the cause for conducting the test results in improved compliance.

Moreover, particular embodiments provide the infrastructure necessary to manage multiple collection procedures 70 run simultaneously by different patients 12 on different collection devices 24, while ensuring secure web-based communications for receiving and transmitting the collection procedures 70 and the results obtained through analysis of these collection procedures 70. For example, certain embodiments provide assistance to the clinician 14 by: making it easier for the clinician 14 to influence all phases of patient therapy from disease state determination to periodic monitoring under on-the-fly periodic therapy; the various stages that make it possible for the clinician 14 to manage the execution of the collection procedure 70 for a set of patients in a secure and web-based format; flexibility is given to the clinician 14 by providing options for selecting a collection procedure 70 from a predetermined list or modifying a collection procedure 70 based on patient needs; the interaction between the clinician 14 and the patient 12 is made more efficient as the communication is completely data-centric and guided by, for example, upcoming medical problems.

Specific examples of therapy optimization are described below, particularly in the context of insulin titration.

Structured collection example for optimized insulin titration.

Fig. 19A provides one exemplary embodiment of a structured collection protocol for optimizing insulin dose titration, which thereby produces insulin doses that maintain biomarker levels within a desired range. In one embodiment, the titrated insulin may be basal insulin. After the structured collection is initiated, the insulin dose is typically an initially prescribed dose, such as the initially prescribed dose listed on the package. But other doses are also contemplated depending on what phase the structured collection protocol is in, since the entry criteria can be considered before each biomarker reading. Thus, the initial dose may be an adjusted dose, a maximum allowable dose, or even an optimized dose, which is higher than the initial prescribed dose. It is envisaged that the structured collection may be used to obtain an optimised insulin value, or may be used after optimisation to verify that the insulin dose is still optimised.

In the embodiment of fig. 19A, the structured collection protocol may optionally require consideration of the entry criteria 710 before starting to collect biomarker data. It is contemplated that the diabetic person, the health care provider, or both may determine whether the entry criteria are met. The entry criteria may be established by a health care provider in some embodiments, and may be related to the age, weight, and medical history of the diabetic person. Thus, the structured collection protocol may require that the diabetic person be subjected to a check-up or physical examination in order to ensure that the diabetic person meets the entry criteria. For example, the entry criteria may specify Fasting Plasma Glucose (FPG) levels or glycated hemoglobin levels as determined by the HbA1c test. The normal range for the HbA1c test is between 4-6% for people without diabetes, so the entry criteria may require a value above about 6%, or in an exemplary embodiment between about 7.5% and about 10%. As an additional example of entry criteria, a fasting plasma glucose level of at least about 140mg/dl is required. The entry criteria may also set requirements regarding body weight or Body Mass Index (BMI). For example, the required BMI may be above about 25kg/m2, or between about 26kg/m2 and about 40kg/m 2. Further, the entry criteria may specify a desired age range (e.g., 30-70) or number of years (e.g., at least 2 years) with diabetes. Furthermore, while it is contemplated that the structured collection protocol is applicable to people with all types of diabetes, the entry criteria may limit the structured collection protocol to type 2 diabetes. In addition, the entry criteria may focus on the current diabetes treatment regimen of the diabetic person. For example, the entry criteria may require that a treatment regimen for a diabetic be limited to oral antidiabetic administration, i.e., no insulin injections. In addition, the entry criteria may require that the diabetic person be not ill or under stress. As previously described, while the embodiment of fig. 19A is directed to consideration of entry criteria, the structured collection protocol does not require that entry criteria be considered prior to collecting biomarker data. For example, referring to the additional embodiments of FIGS. 19B-D, the embodiment of FIG. 19B requires consideration of entry criteria; the embodiment of fig. 19C and 19D does not include such constraints.

Referring again to FIG. 19A, if the entry criteria are not met, then structured collection protocol 715 will not be initiated. The diabetic person or healthcare provider may determine whether the entry criteria are met, or the data processor may determine whether the entry criteria are met. If the entry criteria are met 710, the diabetic person may begin a structured collection protocol. In some embodiments, however, the diabetic person may be required to meet the adherence criteria prior to collecting the biomarkers or administering the insulin.

Adherence criteria are the protocol requirements that a diabetic person must follow when implementing a structured collection protocol. In order to obtain a proper baseline for the biomarker readings, it may be beneficial to ensure that all readings are taken consistently, i.e., approximately at the same time of day for each sample. Thus, the adherence criteria may specify that biomarker collection or insulin administration be performed at the same time each day. To assist the diabetic in meeting the adherence criteria, the data processor displays a prompt for the diabetic with an audible and/or visual reminder to collect a biomarker sample thereof and to enable the diabetic to set future reminders. In particular embodiments, the adherence criteria may also require that the diabetic person fasting for a set period of time before collecting the biomarker readings. The adherence criteria may also be directed to determining whether the diabetic person is ingesting the correct insulin dose. In additional embodiments, the adherence criteria may also require that there is no recent hypoglycemic event or severe hypoglycemic event (hypoglycemic event) within a set period of time (e.g., one week) before collecting the biomarker data. Furthermore, the adherence criteria may specify an exercise regimen or a feeding regimen for the diabetic person. As used herein, "eating regimen" means the typical eating regimen of a diabetic person in terms of calories, carbohydrate intake, and protein intake.

If the diabetic person is not able to meet any or all of the adherence criteria, the diabetic person may be notified, for example, via a display of a blood glucose meter that they are not able to meet the adherence criteria. If the diabetic person is not able to meet the adherence criteria, the data processor device may tag the adherence event, or the diabetic person may record the occurrence of the adherence event. After recording the adherence event, the structured collection protocol will typically continue. However, if too many adherence events are recorded (e.g., more than 4 in one sample period, more than 20 adherence events in the entire execution), the structured collection protocol may be terminated. Further, the structured collection protocol may also rate the adherence events in different ways. For example, a hierarchical adherence event assessment may be performed in which adherence events are weighted. In one or more embodiments, if the adherence event has no effect on the biomarker data, its weight may not weigh as much as the adherence event affecting the biomarker data. For example, when a diabetic person has fasting for the requisite period of time before taking a fasting blood glucose reading but is unable to record that the reading is a fasting blood glucose reading, this will be classified as a less severe and thus less weighted adherence event for diabetes, since recording errors will not affect the fasting blood glucose reading. In contrast, fasting less than the requisite period of time will affect fasting glucose readings and therefore constitute a more serious and thus higher weighted adherence event.

If a violation event occurs (e.g., insulin administration is missed), the structured collection protocol is more likely to be terminated than an adherence event (e.g., fasting less than the required fasting period) because the violation event affects the structured collection protocol more severely. Since the present structured collection protocol is directed to optimizing insulin administration, it is justified that missing insulin administration would be a serious violation.

As with other instructions provided to the diabetic person via the structured collection protocol, the entry criteria or adherence criteria may be provided to the diabetic person via a paper instruction form or via a data processing device as shown in FIG. 3 or a display unit on the processor 102. The data processing device may be any of the electronic devices described above. In one or more embodiments, the data processing device may be a computer or a blood glucose meter having a data processor and a memory unit therein. In addition to listing entry criteria, adherence criteria, or both, the data processing device may prompt the diabetic person to answer a medical question, wherein the answer to the medical question is used by the device to determine compliance with the entry criteria or adherence criteria. The data processing device may notify the diabetic person of their failure to comply with the entry criteria or the adherence criteria. For example, the data processing device may notify the diabetic if the subsequent sample is not taken near the same time as the first sample. The patient may record the sample or answer a medical question by entering the data event directly into a device or computer, where the processor 102 may store the information and provide additional analysis according to the parameters of the structured collection protocol.

Referring again to fig. 19A, the diabetic person may begin collecting one or more sets of biomarker data samples. Each sample set comprises a sufficient number of non-adverse samples recorded over a certain collection period, which means at least two samples that do not indicate an adverse event (e.g. a hypoglycemic or hyperglycemic event). Each sample 740 includes biomarker readings at a single point in time. The collection time period for the set of samples may be defined as a number of samples over a day, a number of samples over a week, a number of samples over consecutive weeks, or a number of samples over consecutive days of a week. The biomarkers may be associated with glucose levels, triglycerides, low density lipids, and high density lipids. In one exemplary embodiment, the biomarker reading is a blood glucose reading. In addition to the biomarker reading, each sample may include the biomarker reading and other contextual data associated with the biomarker reading, wherein the contextual data is selected from a group consisting of: a collection time, a collection date, a time of a last meal, a confirmation that the subject has fasted for a requisite period of time, and combinations thereof. In the exemplary embodiment of fig. 19B, the structured collection protocol occurs within a 7-day method that requires a diabetic patient to administer insulin in the evening, followed by the collection of fasting glucose readings in the next morning. In addition to morning biomarker collection, a diabetic patient is also instructed to take additional biomarker readings when the diabetic patient encounters symptoms of hypoglycemia.

Referring again to fig. 19A, after the biomarker reading is collected, it is determined whether the biomarker reading indicates an adverse event 750. While the present discussion of adverse events focuses on hypoglycemic events and severe hypoglycemic events that may require medical assistance, it is contemplated that adverse events may refer to undesirable levels of other biomarkers or medical indicators, such as lipid levels, blood pressure levels, and the like. In one embodiment, the determination regarding an adverse event may be performed by comparing the biomarker reading to a low threshold, such as the hypoglycemic event or severe hypoglycemic event threshold shown in table 1 below. If the biomarker reading is below one or both of these values, an adverse event may have occurred and should be recorded as an adverse event, or explicitly as a hypoglycemic event or severe hypoglycemic event. As previously mentioned, the determination may be performed by the data processor unit or may be manually entered by the diabetic person.

TABLE 1

Blood sugar range (mg/dl)	Insulin regulating parameter (Unit)
		Below 56 (severe hypoglycemic events)	-2 to-4
56-72 (hypoglycemic events)	0
		73 to 100 (target biomarker range)	0
100-119	+2
		120-139	+4
140-179	+6
		180 and more	+8

If there is an adverse event (e.g., a severe hypoglycemic event), in one embodiment, the indication or data processing device may recommend that the diabetic person contact their health care provider. In another embodiment, the system may automatically contact a Health Care Provider (HCP). In addition, adverse events may optionally lead to dose reduction. Referring to Table 1 above, if it is a hypoglycemic event (between 56-72 mg/dl), the HCP 850 may be contacted, but the dose is not adjusted (see FIG. 19A). However, if it is a severe hypoglycemic event (below 56 mg/dl), the dose may be reduced by an amount (640), such as 2 units, 4 units, or another amount as indicated by a low biomarker reading. In particular embodiments, if the recorded adverse event is a second measured severe hypoglycemic event within the same day, the dose is not reduced. In other embodiments, the data processing device may utilize an algorithm to automatically reduce the insulin dosage and inform the diabetic person of the reduced insulin dosage. In addition, the data processing device that collects the biomarker readings may automatically notify the health care provider of the adverse event, for example, by automated email and text message.

If the biomarker reading is not poor, the next step depends on whether the sample set 760 has a sufficient number of non-poor samples. If only one sample is required for the set of samples, the biomarker sampling parameter may be calculated at that point; but as previously mentioned, the sample sets typically require multiple or at least two samples for each sample set. In an exemplary embodiment, two or more samples taken on consecutive days are required for each sample set. If multiple samples are required, the diabetic person must continue to collect samples.

Once the necessary number of samples have been obtained for the set of samples, biomarker sampling parameters 770 may be obtained. The biomarker sampling parameters may be determined by various algorithms or methods. For example, the biomarker sampling parameter may be determined by: averaging the samples, adding the samples, performing a graphical analysis on the samples, performing a mathematical algorithm on the set of samples, or a combination thereof. In an exemplary embodiment, samples (i.e., biomarker readings) are collected for at least three consecutive days, and the average of the three consecutive days is the biomarker sampling parameter.

After obtaining the biomarker sampling parameter, the value is compared to the target biomarker range. As used herein, a target biomarker range means an acceptable biomarker range in a diabetic person, which thereby indicates that insulin is producing the desired physiological response. If the biomarker sampling parameter falls outside the target biomarker range, an insulin adjustment parameter may be calculated 790. The insulin adjustment parameter is associated with the biomarker sampling parameter and is calculated from the biomarker sampling parameter. Various methods and algorithms are contemplated for calculating the insulin adjustment parameter. For example, the insulin adjustment parameter may be calculated by locating the insulin adjustment parameter associated with the biomarker parameter in an insulin adjustment parameter look-up table (see table 1 above). As previously shown in the exemplary insulin adjustment parameter look-up table of table 1, there may be multiple tiers indicating how many insulin doses should be adjusted. For example, fasting glucose levels below 100mg/dl but above 56mg/dl would not require adjustment of the insulin dose. The greater the deviation from the target range, the higher the regulating unit of insulin.

After the insulin adjustment parameter is determined, the insulin dosage may be adjusted by the amount of the insulin adjustment parameter as long as the insulin adjustment does not increase the insulin dosage above the maximum allowable dosage. The adjusted insulin dose cannot exceed the maximum level set by the health care provider. After determining the adjusted insulin dose value, the diabetic person may then be instructed to collect at least one additional set of samples at the adjusted insulin dose according to the previously described collection procedure. The biomarker sampling parameter, the insulin adjustment parameter, and the adjusted insulin dosage may be calculated manually by the diabetic person or by a data processing device.

If the biomarker sampling parameter is within the target biomarker range, the insulin dosage is not adjusted. Furthermore, the insulin dosage may be considered optimized according to other applicable criteria. In particular, the insulin dose may be considered optimized if one biomarker sampling parameter is within the target biomarker range, or may be considered optimized 820 if at least two consecutive biomarker sampling parameters are within the target biomarker range. If the optimization definition requires at least two consecutive biomarker sampling parameters within the target biomarker range, the diabetic person is instructed to collect at least one additional set of samples at the adjusted insulin dose according to the previously described collection procedure. After the insulin dosage is deemed optimized, the diabetic is instructed to exit the structured collection protocol. After exiting the structured collection protocol 730, the diabetic person may implement additional structured collection protocols in order to determine the future efficacy of the optimized dose.

In an alternative embodiment, the diabetic patient may be instructed to exit the structured collection protocol 730 if the diabetic patient has been performing the test protocol for a long period of time, for example, 6 months or more. Further, as previously described, if there are multiple adherence or violation events, the test may be automatically terminated by the data processing device, or the diabetic may be instructed to exit the structured collection protocol.

And (5) dynamic sampling collection.

As noted above, the primary focus of a diabetic patient on a structured collection protocol is to achieve a desired therapy outcome or goal (e.g., optimized insulin dosage); however, diabetics also desire to achieve this desired result in the most efficient manner possible. To this end, embodiments of the present invention are directed to a more dynamic structured collection protocol. For example, referring to the embodiment of fig. 20A and 20B, if the shortened number of collected samples is sufficient for the collection device to perform the calculations, the collection device used in the structured collection protocol may shorten the number of samples in the sample set and it may determine the therapy outcome for the sample set more quickly than predicted. Alternatively, as shown in fig. 20C, a collection device used in a structured collection protocol may shorten the duration of a collection period in which the number of samples in a set of samples are sampled. While many of the examples focus on dynamic assessment of a set of samples, embodiments of the present invention may also be utilized in assessing individual collected samples.

As shown in fig. 20A, the dynamic structured collection protocol embodiment of the present invention utilizes prior biomarker sample data as a means for assessing future collected biomarker samples or future sets of biomarker data samples. Referring to fig. 20A, the processor of the collection device evaluates previous biomarker sample data 852 stored in the memory of the collection device. The biomarker sample defines a measurement value (e.g., a blood glucose reading) obtained from the body fluid and also includes contextualized data associated with the biomarker sample. For example, the contextualized data may take into account the time the sample was collected, whether the sample was collected on an empty stomach or after a meal, or the amount of insulin injected prior to the collection of the sample. In addition to considering the type of event (e.g., after fasting or after a meal), the importance of the event may also be considered. For example, a biomarker sample collected after a 3 unit insulin dose will not have the same contextualized data as a biomarker sample collected after a 5 unit insulin dose. Similarly, different meal sizes may be understood as different contextualized data components of previous biomarker samples.

To utilize the previous biomarker sample data, an algorithm is provided to the processor of the collection device via the software of the collection device. According to the algorithm, the processor sets a first criterion 854 for the previous biomarker data. The first criterion may be user defined (i.e., set by the patient, health care provider, etc.) or may be predefined based on known rules or guidelines. In one example, if the patient is known to have a high fasting bG level, the patient or healthcare provider may set a first criterion to a fasting requirement for biomarker sampling. As used herein, a first criterion requires that prior biomarker samples share at least one identical contextualized data component. If multiple biomarker samples share at least one same contextualized data component (e.g., fasting), and thus comply with the first criterion 855, the processor would flag or record the compliant previous biomarker samples as similar 858 and would group 860 the similar biomarker samples. As used in this context, similar means having the same contextualized data shared. For example, if the first criterion requires that the previous biomarker samples are fasting glucose values, all fasting previous biomarker samples will be marked as similar and grouped and the non-fasting values 856 will not be utilized in the structured collection protocol. In this example, the first criterion considers only fasting in its analysis of contextualized information of the biomarker sample; however, the first criterion may require that the other contextualized data components are the same. For example, in addition to fasting, the first criterion may also require biomarker samples to be collected in the morning at set intervals after insulin infusion. Without being bound by theory, where multiple identical contextualized data components are required, the processor may minimize the differences in the measured previous biomarker samples.

Referring again to fig. 20A, the processor then calculates expected values for future biomarker samples that satisfy the first criterion. When comparing newly collected biomarker samples, it is beneficial to ensure that the collected samples have the same contextualized data specified by the first criterion as the previous biomarker samples. When calculating the expected values, the entire grouping or at least a subset of the grouping of similar previous biomarker samples may be used. The processor may determine how many previous biomarker samples to use in the calculation. For example, if the previous biomarker samples have a large difference, it may be beneficial to utilize more samples in the computation, or alternatively it may be beneficial to require more shared components of the same contextualized data to minimize the difference. As will be described in more detail below, various calculations are possible to determine the expected values. In one exemplary embodiment, the expected value is obtained from an average of a subset or group of previous biomarker sample data. In another exemplary embodiment, the calculation of the second criterion is a pattern or trend identification performed by the processor on the measured values of the previous biomarker samples and the contextualized data. Alternatively, the calculation may utilize various metrics, such as a mean of the previous biomarker samples, a standard deviation of the previous biomarker samples, a median of the previous biomarker samples, a variance of the previous biomarker samples, a ratio of insulin to carbohydrate, insulin sensitivity, or a combination thereof.

In an exemplary embodiment for titrating long acting insulin, the protocol acquires a sample set of 7 fasting bG samples at a given insulin infusion. Upon completion of the sampling set, the system may use results from all previous sampling sets, including variability (statistics) and heuristics (human-based rules) for predicting a range of likely future biomarker sampling values based on changes in the proposed insulin delivery dose. Assuming that four weeks of sample acquisition have occurred and that a total of 4 sample sets and 28 samples are provided, an average biomarker value is calculated for each sample set. Data points that deviate significantly from this mean are considered outliers and biomarker values are correlated to insulin infusion amounts. For each set of samples, which all have a gradually increasing insulin delivery dose, it is possible to calculate a running insulin sensitivity measure. Furthermore, it is also possible to calculate the overall variability of the values across each sampling set and across all acquired sampling sets. If there are few or no adherence events for the sample and the variability is lowest for the most recent sample set, then the variability measure for the most recent sample set is selected. Based on insulin sensitivity and variability, a range of expected values for the next sample set and the number of samples can be predicted. While this is one suitable method for anticipating the sample value, various other procedures are contemplated herein.

Referring again to FIG. 20A, after tabulating the expected values, the processor then sets a second criterion. As used herein, the second criterion is an acceptable difference or range from the calculated expected value, or alternatively, the second criterion is a threshold, or a combination of a difference and a threshold. For example, if the expected blood glucose value is 120 and the second criterion allows a maximum difference of 20, then the collected blood glucose value of 130 is within the range and thus complies with the second criterion. As used herein, a threshold may define an upper limit and a lower limit. The processor may set a threshold for the second criterion if the previous biomarker sample data shows an adverse event (e.g., a hyperglycemic or hypoglycemic event) or demonstrates a risk of an adverse event. A collected biomarker sample value below a lower threshold limit may indicate a hypoglycemic event, at which point the collection device may alert the user via triggering an alarm. Alternatively, a biomarker sample value above an upper limit may indicate a hyperglycemic event. In other embodiments, it is contemplated that both the threshold and the range of differences may be included. For example, the second criterion may have a calculated expected bG value of 120 and a difference of 20, thereby yielding a second criterion bG range of 100 to 140. Further, the second criterion may have a lower threshold limit of 60. In this case, the collected bG sample of 70 would not be in the range of 100 and 140, but would be above the lower threshold limit of 60. Thus, the biomarker sampling is not compliant with the second criterion and therefore not used in the structured collection protocol, but the collection device does not trigger an alarm because there has not been a hypoglycemic event.

Nevertheless, the collected biomarker samples must satisfy the first criterion. For example, if a first criterion requires fasting, the collected samples must comply with the first fasting criterion before considering compliance with a second criterion. After the first criterion is met, the processor assesses the compliance 872 of the collected biomarker samples to the second criterion.

The processor may perform one or more additional tasks if the biomarker sampling complies with the second criterion. For example, the processor may provide positive reinforcement to the patient, e.g., by a "do good" message displayed via the display unit of the collection device. Alternatively, the processor may instruct the patient to collect additional samples or may perform the calculations if a sufficient number of samples have been collected. Conversely, if the biomarker sampling fails to comply with the second criterion, the processor may also perform one or more additional tasks. For example, the processor may trigger an alarm system, display an educational tutorial to the patient, instruct the diabetic patient to collect a new sample set, prompt the patient for an explanation of non-compliance, or a combination thereof. Based on the compliance or lack thereof, the processor may update or recalculate the expected value of the future biomarker sample with the collected samples.

Although the above discussion focuses on the assessment of individual biomarker sample values, the structured sample set may be assessed and dynamically adjusted. Referring to fig. 20A, one or more biomarker samples that comply with a first criterion are collected. In addition to calculating the expected value, the processor may also predict the number of biomarker samples to be recorded within the collection time period 865. The processor may make predictions based on various sources. For example, the prediction may be an estimate that the healthcare provider deems effective to achieve a treatment outcome or result. In another embodiment, the predicted number may be generated according to a rule. For example, the software may need to initially estimate always 7 samples, so in this scenario the predicted number is 7 samples. In addition, the predicted number may be determined based on other factors, such as the previous performance of the diabetic patient on a similar sampling set, the patient's response to insulin therapy, the severity of the disease, and so forth. For example, diabetic patients with high blood glucose levels and frequent high or low blood glucose spikes may require more samples in the sample set to optimize their insulin dosage compared to gestational diabetic patients whose values are slightly higher than the target level. For example, if the patient previously achieved a blood glucose level within a normal range of 7 samples, the processor may estimate the number of biomarker samples to be 7. The processor may utilize a previous set of samples or previous biomarker samples collected by the patient as a basis for the number of samples to be collected in a future set of samples.

Similarly, the duration of the collection time period and the frequency of sample collection may be set by the health care provider or may be estimated by the processor based on a previously collected set of samples. Further, the number of samples, the duration of the collection period, and the frequency of the collection period may depend on the type of structured collection protocol. The structured testing protocol may include protocols for diabetes assessment, treatment, optimization, or a combination thereof. For example, if the structured collection protocol is a titration protocol, the collection period may be one week with one daily fasting glucose sample collected in the morning. Conversely, if the structured collection protocol is directed to determining the effect of a meal on a patient, the structured collection protocol may only last for several hours, but the protocol requires that biomarker samples be collected every hour over the entire collection period.

Referring to fig. 20A, after collecting at least one biomarker sample 866, the processor assesses whether the collected biomarker sample(s) of the sample set comply with a first criterion 868. If there is no compliance with the first criterion, the data is not utilized in the structured collection protocol. If there is compliance with the first criterion, the processor may then determine whether the set of samples needs to be adjusted based on the collected biomarker samples' compliance with the second criterion or lack thereof. The adjusting may include recalculating the number of biomarker samples in the set of samples, adjusting a collection frequency of the samples, adjusting a duration of the collection time period, or a combination thereof.

For example, if one or more biomarker samples fail to comply with the second criterion 872, the processor may increase the number of biomarker samples in the sample set. Alternatively, the processor may reduce the number of biomarker samples if the biomarker samples comply with the second criterion. Further, the processor may increase the collection frequency of the biomarker samples if one or more of the biomarker samples fail to comply with the second criterion, and may also decrease the collection frequency of the biomarker samples if the biomarker samples comply with the second criterion. In additional embodiments, the processor may increase the duration of the collection period if one or more biomarker samples fail to comply with the second criterion, or the processor may decrease the duration of the collection period if the biomarker samples comply with the second criterion.

Referring again to fig. 20A, after assessing compliance with the first and second criteria, the processor may also determine whether there are a sufficient number of collected samples to perform the calculations on the set of samples. For example, if the patient is expected to be within the range of normal blood glucose in 7 samples, but already at normal levels (e.g., 100 mg/dl) at the third sample, the processor records that the patient has progressed more quickly toward the desired treatment outcome than estimated, and thus the sample set may be shortened.

When reducing the number of samples, the processor may instruct the diabetic patient to stop collecting samples for the sample set or not to collect the last sample in the sample set. Alternatively, the patient may be instructed to begin collecting another set of samples. From the patient's perspective, this reduction is expected to shorten the number of samples and reduce the duration of the collection period. Next, the processor may perform a calculation on the shortened sample set. Alternatively, the processor may automatically calculate the treatment outcome if it has been determined that the necessary number of samples have been collected.

In yet another embodiment, the processor may reduce the number of samples by eliminating at least one or more biomarker samples prior to a last biomarker sample in the set of samples. In this embodiment, the need to collect the last biomarker sample while eliminating one sample before the last sample may be beneficial because it eliminates the number of biomarker samples, but still provides insight into the progress of the patient over the entire collection period of the sample set.

Referring again to fig. 20A, if the processor alternatively determines that the number of samples collected is insufficient to perform the calculation on the set of samples, the diabetic patient is instructed to collect additional samples 878. In some embodiments, the patient may be instructed to continue collecting samples in the sample set until a sufficient number of samples have been collected.

In another embodiment, if the sample set is shortened, the processor may adjust the predicted number of samples for future collections of the sample set. When the sampling set is shortened, this may indicate that the diabetic patient is quickly responding to insulin therapy. As described above, the processor may consider this response to diabetes treatment when determining the predicted number of samples in a sample set for future sample set collection. As set forth in the previous insulin titration embodiment, the structured protocol may require at least two sample sets of biomarker readings within an exemplary target range before the protocol will account for the insulin dose to be optimized. Therefore, shortening one sampling set in the titration protocol may shorten the other sampling sets of the insulin titration protocol, which would be highly desirable for a diabetic patient.

To further illustrate the structured collection protocol, the following example is provided. In one example, the collection device initially predicts that 7 blood glucose samples are necessary for the diabetic patient to optimize his/her insulin dosage; however, the diabetic patient has the first 5 samples that meet the first and second criteria. Thus, the collection device may determine that the desired treatment outcome has been achieved without collecting all of the predicted samples (i.e., the diabetic patient has optimized his/her insulin). Alternatively, it is contemplated that the sample set may be shortened for undesirable results. For example, the first 5 collected biomarker readings in the predicted sampling set of 7 samples may all indicate that the biomarker readings are outside of the range defined by the second criterion. Thus, the collection device may determine that an undesirable treatment outcome has been achieved without collecting all of the predicted samples (i.e., the diabetic patient has not optimized his/her insulin and needs to continue to collect additional sets of samples).

Referring to an alternative embodiment of dynamic sample set adjustment as shown in fig. 20B, the structured collection protocol may focus on determining a patient's response to an event that adjusts insulin or blood glucose levels. An event is a diabetic's action to regulate the diabetic's insulin level or blood glucose, which may be eating, exercising, insulin administration, or a combination thereof. In a particular embodiment, the event is a meal. In another particular embodiment, the event is a combination of a meal and insulin administration. For example, the collection device may prompt the user to enter consumed food as well as carbohydrate, protein, or other nutritional information into the collection device. Based on this information, the collection device may instruct the patient to administer a set amount of insulin to cover the meal. In an exemplary embodiment, the system may have a priori knowledge of "insulin on meal (board)" and an understanding of the size of previous meals. With the existing and known insulin to carbohydrate ratio and insulin on a meal, the system can specify the amount of insulin intended to cover the meal. Thus, the structured protocol can assess the patient's response to a meal, and can also assess how insulin compensates for the meal.

For the embodiment of fig. 20B, the prediction of the number of biomarker samples may include at least one start biomarker sample 922, at least one end biomarker sample 924, and at least one intermediate biomarker sample 926 between the start biomarker sample and the end biomarker sample. In one example of a sampling set, the start sampling may be collected immediately after a meal and 1 hour after a meal, the intermediate sampling may be collected 2 hours, 3 hours, and 4 hours after a meal, and the end sampling may be collected 5 hours and 6 hours after a meal. Various collection frequencies are conceivable for the sampling.

As shown in fig. 20B, at least one sample is collected prior to the event in order to obtain an initial baseline. For example, if the event is a meal, the sample collected before the meal provides a basis for comparison of subsequent samples collected after the meal and provides the patient with insight as to the effect that the meal has on his/her blood glucose or insulin level. When calculating the ratio of insulin to carbohydrate, a sample is required before the user consumes a meal. Like many other components, the collection device may also predict the initial baseline value. If the collected baseline deviates significantly from the initial baseline value, the collection device may adjust the parameters of the structured protocol by needing to collect more samples or by adjusting the frequency of sample collection.

After the event 1010 is implemented, the structured collection protocol requires multiple post-event samples. In general, at least one collected starting biomarker sample is assessed for compliance with a second criterion; however, the starting biomarker sample changes when the first sample is collected. For example, the processor may start scheduling samples for hourly collection immediately after a meal; however, the processor may decide to skip the first sample immediately after the meal or wait/offset the collection of the first sample by at least one or two hours 1024 after the meal. Without being bound by theory, the collection of offset start samples will desirably coincide with the time at which the blood glucose value is predicted to fall within a predetermined range around the initial baseline. As indicated above, this prediction of when the blood glucose value falls within a predetermined range may be based on a previously sampled set.

Alternatively, the protocol may require sample collection 1022 and any other additional samples 1026 to be performed immediately after the event. After the start sample(s) are collected 1026, the processor may determine whether the samples comply with second criteria 1030. For example, the second starting biomarker sample may be assessed to determine whether it is compliant. If the sampling complies with the second criterion 1030, the processor may reduce or eliminate at least one intermediate biomarker sampling 1032. If the start biomarker sampling complies with the second criteria 1030, the processor may determine that the patient is progressing satisfactorily and, therefore, not all intermediate samples are required. Alternatively, if the second starting biomarker sample fails to comply 1034 with the second criteria, the processor may instruct the user to collect all intermediate biomarker samples.

After the intermediate samples are collected 1034, end samples may be collected. In the embodiment of FIG. 20B, all end samples are collected; however, skipping one or more end samples is contemplated in further embodiments. For example, if the last sample is eliminated, the duration of the collection period may be shortened and the patient has fewer samples to collect. If the processor requires data throughout the collection period, the end sample before the last sample may be eliminated.

In another embodiment shown in fig. 20C, the sample sets may also be dynamically adjusted by changing the frequency of the sample sets. After the event (e.g., meal) is implemented, the collection frequency may be changed for start, middle, and end samples. By varying the frequency, the collection period can be reduced, which is desirable for the patient. For example, for a starting biomarker sample collected after the starting biomarker sample is collected, the collection frequency of the samples is increased 1050. In an exemplary case, the increased frequency provides the processor with more information about how the patient reacted to the meal or any insulin administered to cover the rise in blood glucose produced by the meal. Further, when the sampling indicates an adverse event (such as a hypoglycemic event), it may be desirable to increase the collection frequency. For intermediate samples that may occur at least several hours after a meal, they may be collected 1060 at a reduced frequency. It is contemplated that intermediate samples may be collected at a greater frequency if the start of sampling complies with the second criterion. For end sampling, it is contemplated that frequency 1070 may be increased or frequency 1072 may be decreased for end sampling.

Thus, by the foregoing disclosure, embodiments are disclosed that relate to a system and method for managing the execution of collection procedures, data collection, and data analysis concurrently running on a meter. Those skilled in the art will appreciate that the teachings can be practiced with embodiments other than those disclosed. The disclosed embodiments are presented for purposes of illustration and not limitation, and the present invention is limited only by the claims that follow.

Claims (48)

1. A method of executing a structured collection protocol on a collection device comprising a processor and a memory component, wherein the method comprises:

-providing a plurality of previous biomarker sample data stored in a memory of the collection device, wherein the previous biomarker samples comprise at least one numerical value based on a measurement of a body fluid and a plurality of contextualized data components associated to the previous biomarker samples;

-setting a first criterion, wherein the first criterion classifies previous biomarker samples as similar if they share at least one same contextualized data component;

-determining whether previous biomarker samples are similar based on the first criterion;

-grouping biomarker samples determined to be similar based on a first criterion;

-calculating an expected value for future biomarker samples satisfying a first criterion, wherein the calculation is based on at least a subset of the set of similar previous biomarker samples;

-setting a second criterion, wherein the second criterion is an acceptable difference from the calculated expected value, a threshold value, or a combination thereof;

-collecting one or more biomarker samples satisfying the first criterion; and

-assessing, via the processor, compliance of the collected biomarker samples with a second criterion.

2. The method of claim 1, wherein the first criterion requires a biomarker sample to have a plurality of shared identical contextualized data components.

3. A method according to claim 1 or 2, further comprising recalculating expected values for future biomarker samples.

4. A method according to any one of the preceding claims, wherein the range is a predicted variance or standard deviation from the collected biomarker samples.

5. A method according to any one of the preceding claims, wherein the processor labels similar biomarker samples.

6. The method according to any of the preceding claims, further comprising: triggering an alarm system in the collection device if one or more of the collected biomarker samples fail to meet a second criterion.

7. The method according to any of the preceding claims, further comprising: displaying, via the collection device, an educational course if one or more of the collected biomarker samples fails to meet a second criterion.

8. The method according to any of the preceding claims, further comprising: if the one or more collected biomarker samples do not meet the second criterion, the diabetic patient is instructed to collect a new set of samples.

9. The method according to any of the preceding claims 1 to 7, further comprising: if the one or more collected biomarker samples do not meet the second criteria, the patient is prompted via the collection device.

10. The method according to any of the preceding claims, wherein the calculation of the second criterion is based on pattern recognition of the contextualized data and the measured values of previous biomarker samples.

11. The method of any preceding claim, wherein the threshold defines a lower limit, an upper limit, or both.

12. The method of claim 11, wherein a biomarker sample value below the lower limit indicates a hypoglycemic event.

13. The method of claim 11, wherein a biomarker sample value above the upper limit indicates a hyperglycemic event.

14. A method of executing a structured collection protocol on a collection device comprising a processor and a memory component, wherein the method comprises:

-providing a plurality of previous biomarker sample data stored in a memory of the collection device, wherein the previous biomarker samples comprise at least one numerical value based on a measurement of a body fluid and a plurality of contextualized data components associated to the previous biomarker samples;

-setting a first criterion, wherein the first criterion classifies previous biomarker samples as similar if they share at least one same contextualized data component;

-determining whether previous biomarker samples are similar based on the first criterion;

-grouping biomarker samples determined to be similar based on a first criterion;

-calculating an expected value for future biomarker samples satisfying a first criterion, wherein the calculation is based on at least a subset of the set of similar previous biomarker samples;

-setting a second criterion, wherein the second criterion is an acceptable difference from the calculated expected value, a threshold value, or a combination thereof;

-collecting one or more biomarker samples of a set of samples, the biomarker samples complying with the first criterion, wherein the set of samples comprises a predicted number of biomarker samples to be recorded over a collection time period;

-assessing, via the processor, compliance of the collected biomarker samples with a second criterion; and

-determining whether an adjustment of the set of samples is required based on a compliance or lack of compliance of the collected biomarker samples with the second criterion, wherein the adjustment comprises recalculating the number of biomarker samples in the set of samples, adjusting the collection frequency of the samples, adjusting the duration of the collection time period, or a combination thereof.

15. The method of claim 14, further comprising recalculating the predicted number of biomarker samples for future collections of the sample set.

16. The method according to claim 14 or 15, further comprising: increasing the number of biomarker samples if one or more biomarker samples fail to comply with the second criterion.

17. The method according to any one of claims 14 to 16, further comprising: increasing the collection frequency of biomarker samples if one or more biomarker samples fail to comply with the second criterion.

18. The method according to any one of claims 14 to 17, further comprising: increasing the duration of the collection period if one or more biomarker samples fail to comply with the second criterion.

19. The method according to claim 14 or 15, further comprising: reducing the number of biomarker samples if the biomarker samples comply with the second criterion.

20. The method of claim 19, wherein the last biomarker sample in the set of samples is eliminated.

21. The method of claim 19, wherein at least one or more biomarker samples in the set of samples immediately preceding the last biomarker sample are eliminated.

22. The method of any of claims 14, 15, and 19 to 21, further comprising: reducing the collection frequency of biomarker samples if the biomarker samples comply with the second criterion.

23. The method of any of claims 14, 15, and 19 to 22, further comprising: reducing the duration of the collection period if the biomarker sampling complies with the second criterion.

24. The method according to any one of claims 14 to 23, wherein the predicted number of biomarker samples comprises at least one start biomarker sample, at least one end biomarker sample, and at least one intermediate biomarker sample between the start biomarker sample and the end biomarker sample.

25. The method of claim 24, wherein the start biomarker sampling is recorded after an event, wherein the event is a diabetic patient's action to adjust a diabetic patient's blood glucose or insulin level.

26. The method of claim 25, wherein the event is a meal, exercise, insulin administration, or a combination thereof.

27. The method according to claim 24, wherein at least one biomarker sample is recorded prior to the event.

28. The method according to any of the claims 24 to 27, wherein at least one intermediate biomarker sample is eliminated if at least one assessed starting biomarker sample complies with the second criterion.

29. A method according to any one of claims 24 to 28, wherein the first start sample is collected at a time period offset from a set interval between biomarker sample collections within the sample set.

30. A method according to any one of claims 24 to 29, further comprising skipping the first start biomarker sample.

31. The method of any of claims 24 to 30, further comprising: assessing a second starting biomarker sample to determine whether it complies with the second criterion; and instructing the user to collect all intermediate biomarker samples if the second starting biomarker sample fails to comply with the second criterion.

32. A method according to any one of claims 24 to 32, further comprising reducing the collection frequency of biomarker samples collected after collection of the start biomarker sample.

33. The method of claim 32, wherein the collection frequency is reduced for intermediate samples and further reduced for end samples.

34. The method of claim 32, wherein the collection frequency is decreased for intermediate samples and increased for end samples.

35. The method of any of claims 14 to 34, further comprising: if there are a sufficient number of collected samples that comply with the first and second criterion, a calculation is performed on the set of samples.

36. The method of any one of claims 14 to 35, wherein the threshold defines a lower limit, an upper limit, or both.

37. The method of claim 35, wherein a biomarker sample value below the lower limit indicates a hypoglycemic event.

38. The method of claim 35, wherein a biomarker sample value above the upper limit indicates a hyperglycemic event.

39. A collection device configured to guide a diabetic patient through a structured collection protocol, comprising:

-a meter configured to measure one or more selected biomarkers;

-a processor disposed inside the meter and coupled to a memory, wherein the memory comprises a collection procedure; and

-software having instructions which, when executed by a processor, cause the processor to perform the method steps of at least one of claims 1 to 13.

40. A collection device configured to guide a diabetic patient through a structured collection protocol, comprising:

-a meter configured to measure one or more selected biomarkers;

-a processor disposed inside the meter and coupled to a memory, wherein the memory comprises a collection procedure; and

-software having instructions which, when executed by a processor, cause the processor to perform the method steps of at least one of claims 14 to 38.

41. A method of executing a structured collection protocol on a collection device comprising a processor, wherein the method comprises:

-providing a plurality of previous biomarker sample data stored in a memory, wherein the previous biomarker samples comprise at least one numerical value based on a measurement of a body fluid, whereby the previous biomarker samples are associated to contextualized data;

-defining the biomarker samples as similar based on a predefined first criterion, whereby the first criterion consists of a comparison of one or more contextualized data of the biomarker samples;

-tagging, by the processor, similar biomarker samples;

-calculating, via the processor, an expected value for a future similar biomarker sample based on the measured values, whereby the calculation is based on at least a subset of similar biomarker samples comprising more than one previous biomarker sample;

-setting a second criterion based on the calculated expected value; and

-configuring a structured collection protocol by means of said second criterion.

42. The method of claim 41, wherein the second criterion comprises a threshold, a range, or both.

43. A method according to claim 41 or 42, wherein an alarm system is triggered if one or more future biomarker samples do not meet the second criterion.

44. A method according to any of claims 41 to 43, wherein the user is prompted if one or more future biomarker samples do not meet the second criterion.

45. A method according to any of claims 41 to 44, wherein the calculation of the second criterion is based on pattern recognition taking into account contextualized data of the measured values and/or previous biomarker samples.

46. The method according to any one of claims 41 to 45, wherein the structured collection protocol comprises a set of samples comprising a predicted number of biomarker samples to be recorded, whereby the processor recalculates the number of biomarker samples in the set of samples based on the compliance of future biomarker samples with the second criterion.

47. A collection device configured to guide a diabetic patient through a structured collection protocol, comprising:

-a meter configured to measure one or more selected biomarkers;

-a processor disposed inside the meter and coupled to a memory, wherein the memory comprises a collection procedure; and

-software having instructions which, when executed by a processor, cause the processor to perform the method steps of at least one of claims 41 to 46.

48. A collection device configured to guide a diabetic patient through a structured collection protocol, comprising:

-a meter configured to measure one or more selected biomarkers;

-a processor disposed inside the meter and coupled to a memory, wherein the memory comprises a collection procedure; and

-software having instructions that, when executed by a processor, cause the processor to:

-accessing a plurality of previous biomarker sample data stored in a memory, wherein the previous biomarker samples comprise at least one numerical value based on a measurement of a body fluid, whereby the previous biomarker samples are associated to contextualized data;

-defining the biomarker samples as similar based on a pre-defined or user-defined first criterion, whereby the first criterion consists of a comparison of one or more contextualized data of the biomarker samples;

-tagging similar biomarker samples;

-calculating, via the processor, an expected value for a future similar biomarker sample based on the measured values, whereby the calculation is based on at least a subset of similar biomarker samples comprising more than one previous biomarker sample;

-setting a second criterion based on the calculated expected value; and

-configuring a structured collection protocol by means of said second criterion.