WO2019136201A1

WO2019136201A1 - Vehicular artificial intelligence triage of collisions

Info

Publication number: WO2019136201A1
Application number: PCT/US2019/012264
Authority: WO
Inventors: Timothy Phillip KASPRZAK
Original assignee: The Metrohealth System
Priority date: 2018-01-04
Filing date: 2019-01-04
Publication date: 2019-07-11

Abstract

A system and method for triaging vehicular accidents that utilizes various data collected from vehicles during a collision, and data collected from patient trauma grading of passengers in vehicular collisions, while patients are treated at hospitals or other trauma centers, to triage subsequent accident victims in real-time. The vehicle data is correlated with the patient data to generate a probability algorithm of the severity and type of injury likely resulting from a collision as indicated by the vehicle data. This predictive model can be generated for medical personnel and communities to use to intelligently triage passengers in a collision, i.e., dispatch appropriate response resources, alert nearby hospitals, etc. The vehicle is capable of automatically transmitting the probability of severity of injuries for the passengers to the community emergency response services so that medical personnel and communities may prioritize resources and the location to which said passengers are transported.

Description

VEHICULAR ARTIFICIAL INTELLIGENCE TRIAGE OF COLLISIONS

CROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims priority to U.S. Provisional Patent Application Serial No. 62/613,629, filed January 4, 2018, titled VEHICULAR ARTIFICIAL INTELLIGENCE TRIAGE OF COLLISIONS, the disclosure of which is incorporated by reference in its entirety herein.

BACKGROUND

[0002] The present application relates generally to the automotive arts, the medical arts, the sensor arts, and the like. More particularly, the present application relates to automated immediate automotive accident triage utilizing vehicular and medical information.

[0003] Current vehicles incorporate several computer systems coupled to a plurality of sensors, distributed throughout the vehicle. These sensors collect data related to speed, brake application, steering, airbag deployment, tire pressure, direction, temperature, and the like. Existing accident services, such as ONSTAR, monitor this information, and upon detection of, for example, and airbag deployment, contacts the occupants of the vehicle and possibly emergency services. Given the communications systems typically incorporated into vehicles, this data may be communicated via proprietary or public cellular networks. Similarly, hospitals and medical personnel and sensors collect data related to patients involved in vehicular traumas. The data related to medical traumas includes, for example and without limitation, lacerations, contusions, etc. This information is necessarily collected after the accident, once the passengers in the vehicle have been evaluated at the hospital.

[0004] When an accident occurs, emergency services are contacted and dispatched, based solely upon the information provided to the dispatcher by a good Samaritan or a service, such as ONSTAR. Unfortunately, this information may be limited due to the lack of medical training of the original contact with emergency services. As a result, the community emergency response is sometimes not immediate, or calibrated to severity of trauma by objective data. For example, an ambulance with EMTs may be dispatched, when no injuries were sustained, or an ambulance unequipped for the type of trauma is dispatched to the scene. Stated another way, communities typically have limited resources available for responding to traffic accidents, and the incorrect allocation of these scarce resources can result in the misappropriate initial care (which can be life threatening), wasteful spending and unnecessary risk exposure for insurance carriers.

[0005] Accordingly, what is needed are systems and methods to automatically and objectively generate probabilities of injury severity to assist in the appropriate dispatching of emergency services. Furthermore, what is needed are systems and methods that can alert medical personnel as to the type of trauma associated with a vehicular accident, enabling appropriate allocation of resources, identification of appropriate facilities, and dispatching of appropriate personnel and equipment to the scene of the accident.

BRIEF DESCRIPTION

[0006] In one embodiment of this disclosure, described is an automated system for triaging vehicular accidents. The system includes an emergency response computer system that utilizes a processor in communication with memory, and a communication module in communication with the processor. The communication module is configured to communicate via a distributed computer network with at least one vehicle. The memory stores instructions which are executed by the processor, causing the processor to receive vehicle data corresponding to a plurality of vehicles involved in vehicular accidents, and receive trauma data corresponding to a plurality of passengers involved in the vehicular accidents. The memory further stores instructions for correlating the vehicle data with the trauma data, and generating a predictive algorithm as to the severity and type of injury in accordance with the correlated vehicle and trauma data.

[0007] In another embodiment of this disclosure, described is an automated system for triaging vehicular accidents. The system includes a vehicle computer system that utilizes a processor in communication with memory, an analysis module in communication with the processor, and at least one sensor in communication with the vehicle computer system, the at least one sensor collecting data associated with a vehicle. The memory stores instructions which are executed by the processor causing the processor to receive data from the at least one sensor indicative of a collision of the vehicle, and determine a probability of severity and type of injury in accordance with a predictive algorithm and the analysis module. The memory further stores instructions to communicate the output of the probability of severity and type of injury to an emergency response computer system via an associated communications network.

[0008] In still another embodiment of this disclosure, described is a method for automatically triaging vehicular accidents. The method includes the steps of receiving, with a processor in communication with memory, from a plurality of vehicles, vehicle data corresponding to operations of each of the plurality of vehicles and incidents corresponding thereto. The method also includes receiving trauma data corresponding to a type and a severity of injury of at least one passenger in each of the plurality of vehicles, and correlating, with the processor, the trauma data with the corresponding vehicle data. In addition, the method includes the step of generating a predictive algorithm in accordance with the correlated trauma and vehicle data.

[0009] In yet another embodiment of this disclosure, described is a method for automatically triaging a passenger of a vehicular accident. The method includes the steps of receiving vehicle data corresponding to a vehicle in an accident from a vehicle computer system of the vehicle, and analyzing, in accordance with a predictive algorithm, the received vehicle data. The method further includes the steps of determining, in accordance with an output of the predictive algorithm, a probability of a type and a severity of injury of the passenger, and communicating, via a distributed communications network, the probability of the type and the severity of injury to the passenger to an emergency response system. Additionally, the method includes the steps of identifying, from the probability of the type and the severity of injury, an appropriate emergency response, and dispatching the identified appropriate emergency response to the passenger of the vehicle.

BRIEF DESCRIPTION OF THE DRAWINGS

[0010] FIGURE 1 is an illustration of a system for automated triaging of vehicular accidents in accordance with one embodiment of the subject application; [0011] FIGURE 2 is an illustrative flowchart of a method for generating the predictive algorithm for automated triaging of vehicular accidents in accordance with one embodiment of the subject application.

[0012] FIGURE 3 is an illustrative flowchart of a method for utilizing the predictive algorithm for automated triaging of vehicular accidents in accordance with one embodiment of the subject application.

[0013] FIGURE 4 is an illustrative example of data collection points for use in the system and method for automated triaging of vehicular accidents.

[0014] FIGURE 5 is an illustrative example of a suitable learning algorithm methodology for use in the system and method for automated triaging of vehicular accidents.

DETAILED DESCRIPTION

[0015] Disclosed herein are systems and methods for triaging vehicular accidents. In varying embodiments discussed hereinafter, the systems and methods utilize various data collected from vehicles during a collision, e.g., speed, brake application, steering, etc., and data collected from patient trauma grading of passengers in vehicular collisions, e.g., severity and type of injury, internal, external, etc., while patients are treated at hospitals or other trauma centers. In such embodiments, the vehicle data is correlated with the patient data to generate a probability algorithm of the severity and type of injury likely resulting from a collision as indicated by the vehicle data. This predictive model can be generated for medical personnel and communities to use to intelligently triage passengers in a collision, i.e., dispatch appropriate response resources, alert nearby hospitals, etc. Further embodiments implement the aforementioned predictive algorithm in onboard computer systems, e.g., vehicle computer systems including, for example and without limitation, proprietary and standard event data recorders, along with cellular (or other wireless) connectivity capabilities on vehicles to assist in the triaging of passengers in an accident. Thus, the vehicle is capable of automatically transmitting the probability of severity of injuries for the passengers to the community emergency response services so that medical personnel and communities may prioritize resources and the location to which said passengers are transported. [0016] It will be appreciated by those skilled in the art that the foregoing, and the discussion hereinafter, enables first responders to know which care facility each passenger should go to based on the probability of severity of injuries from the predictive algorithm, thereby saving considerable time for inter-facility transportation. The skilled artisan will further appreciated that such embodiments disclosed herein enable prioritization of community resources, e.g., LifeFlight-type response, and which need minimal resources, e.g. fire department EMS. As a result, it will be understood that patient care is substantially improved by providing the right level care at first response to an accident.

[0017] Turning now to FIGURE 1 , there is shown a system 100 for automatic triaging of passengers in a vehicular accident in accordance with one embodiment of the subject application. As used herein,“collision” and“accident” may be used interchangeably to refer to an event involving probable injury to passengers or damage to the vehicle. It will be appreciated that the various components depicted in FIGURE 1 are for purposes of illustrating aspects of the exemplary embodiment, and that other similar components, implemented via hardware, software, or a combination thereof, are capable of being substituted therein.

[0018] As shown in FIGURE 1 , the triaging system 100 includes an emergency response computer system 102 (hereinafter computer system 102) configured to interact with a plurality of devices, components, personnel, facilities, and the like, as further illustrated herein. The exemplary computer system 102 includes a processor 104, which performs the exemplary method by execution of processing instructions 106 that are stored in memory 108 connected to the processor 104, as well as controlling the overall operation of the computer system 102.

[0019] The instructions 106 include a communication module 110 that is configured to communicate with a plurality of different devices and facilities as will be appreciated by those skilled in the art. As illustrated in FIGURE 1 , the communication module 110 may be configured to collect vehicle data 120 and trauma data 122, received from vehicles 158 and care facilities 146, 148, and 150, as discussed in greater detail below. FIGURE 4 illustrates example data collection points from an associated vehicle 158 and trauma injury scales. Alternatively, the data 120 and 122 may be retrieved from an associated data storage 124.

[0020] The instructions 106 may also include an artificial intelligence module 112 that is configured to correlate the received vehicle data 120 and trauma data 122 so as to generate a predictive algorithm 132 in accordance with the systems and methods described herein. According to one embodiment, the artificial intelligence module 112 is configured to model severity and type of injuries associated with particular collisions, as indicated by the vehicle data 120 correlated with trauma data 122. Those skilled in the art will appreciate that such an artificial intelligence module 112 may be implemented as any of a myriad of artificial intelligence-type embodiments. One particular example of an artificial intelligence driven generation of a probability algorithm 132 based on event data recorder data 120 and trauma grading data 122 is illustrated in FIGURE 5. It will be understood by those skilled in the art that the representation of the artificial intelligence module 112 as resident in the instructions 106 is for example purposes only, and the module 112 may be implemented as a neural network or cloud-based artificial intelligence to generate the probability algorithm 132 in accordance with varying embodiments contemplated herein. The skilled artisan will further appreciate that the vehicle data 120 and the trauma data 122 utilized by the artificial intelligence module 112 are suitable anonymized, along with additional data points, e.g., make, model, year, environmental conditions, etc. Furthermore, data collected and utilized by the artificial intelligence module 112 may also include correlative passenger-patient data, such demographics and final injury diagnosis as based on grading criteria of the American Association for the Surgery of Trauma, the entirety of which is incorporated by reference herein. In addition, the artificial intelligence module 112 may be configured to analyze the above-referenced data to identify associates between vehicle data 120 and the type/severity of patient injuries.

[0021] The instructions 106 may further include an analysis module 114, configured to analyze vehicle data 120 in real-time, i.e., transmitted from an associated vehicle 158 immediately after a collision. It will be understood that the analysis module 114 shown in the computer system 102 of FIGURE 1 may be optional, and correspond to those instances when the vehicle 158 itself does not include such an analysis module 114, i.e., the onboard computer thereof lacks the ability to process the algorithm 132, such as older model vehicles. In varying embodiments, the analysis module 114 utilizes the received vehicle data 120 in conjunction with the predictive algorithm 132 generated via the artificial intelligence module 112 to determine both the probability of severity of injury and type of injuries to occupants of the vehicle 158. Stated another way, the analysis module 114 is configured to triage passengers in the vehicle 158 prior the active involvement of medical personnel, i.e., first responders.

[0022] In addition to the foregoing, the instructions 106 also include an identification module 116 configured to receive the output of the analysis module 114 and identify the appropriate response to the vehicular accident/collision. That is, in one embodiment, the identification module 116 identifies the appropriate first responders, the appropriate facility to receive the passengers, medical/law enforcement personnel, and the like. This identification is returned to the communication module 110, which then dispatches the appropriate response elements.

[0023] As shown in FIGURE 1 , the instructions 106 further include an importation module 117 configured to import (i.e., format and communicate) the probability of severity of injury and type of injuries output via the predictive algorithm 132 directly into an electronic medical record 119 (also known as an electronic health record). It will be appreciated by those skilled in the art that the electronic medical record 119 corresponds to an electronic document containing patient information, medical diagnoses, medical history, patient vitals, demographics, medication and allergies, immunization status, laboratory test results, images (e.g., X-ray, CT, MRI, etc.), patient billing information, insurance information, and myriad other types of information utilized by medical personnel in treating and administering to patients. In varying embodiments, the electronic medical record 119 may be specific to the driver of the vehicle 158, or known occupants of the vehicle 158, e.g., pre-registered seating by vehicle owner, etc.

[0024] It will further be appreciated by those skilled in the art that the format of the electronic medical record 119 may adhere to any known standard promulgated for electronic medical records including, for example and without limitation, the Accredited Standards Committee X12 (ASC X12), European Committee for Standardization TC/251 (EN 13606, CONTSYS (EN 13940), HISA (EN 12967)), DICOM, HL7, Fast Healthcare Interoperability Resources (FHIR), ISO TC 215, xDT, openEHR, and the like, the entire disclosures of which are incorporated by reference herein. Preferably, the importation module 117 is configured to import the severity and type of injury predicted into a plurality of different types of electronic medical records 119. Additionally, the importation module 117 may be configured to utilize one or more encryption mechanisms to securely transfer any patient information, electronic medical record 119, severity/type of injury, etc. The skilled artisan will appreciate that suitable security mechanisms may include, for example and without limitation, encryption algorithms, hardware, software, etc. adhering the requirements of HIPAA and the HITECH Act of 2009 in the U.S., as well as other similar obligations for medical information transfer and storage.

[0025] The various components of the emergency response computer system 102 may all be connected by a data/control bus 138. The processor 104 of the computer system 102 is in communication with an associated data storage 124 via a link 126. A suitable communications link 146 may include, for example, the public switched telephone network, a proprietary communications network, infrared, optical, or other suitable wired or wireless data communications. The data storage 124 is capable of implementation on components of the computer system 102, e.g., stored in local memory 108, i.e. , on hard drives, virtual drives, or the like, or on remote memory accessible to the computer system 102.

[0026] The associated data storage 124 corresponds to any organized collections of data (e.g. , medical information, personnel information, collision data, vehicle data 120, trauma data 122, patient data, facility information, etc.) used for one or more purposes. Implementation of the associated data storage 124 is capable of occurring on any mass storage device(s), for example, magnetic storage drives, a hard disk drive, optical storage devices, flash memory devices, or a suitable combination thereof. The associated data storage 124 may be implemented as a component of the computer system 102, e.g., resident in memory 108, or the like.

[0027] The computer system 102 may include one or more input/output (I/O) interface devices 134 and 136 for communicating with external devices. The I/O interface 134 may communicate, via communications link 148, with one or more of a display device 140, for displaying information, such estimated destinations, and a user input device 142, such as a keyboard or touch or writable screen, for inputting text, and/or a cursor control device, such as mouse, trackball, or the like, for communicating user input information and command selections to the processor 104.

[0028] It will be appreciated that the vehicle collision triage system 100 is capable of implementation using a distributed computing environment, such as a computer network, which is representative of any distributed communications system capable of enabling the exchange of data between two or more electronic devices. It will be further appreciated that such a computer network includes, for example and without limitation, a virtual local area network, a wide area network, a personal area network, a local area network, the Internet, an intranet, or the any suitable combination thereof. Accordingly, such a computer network comprises physical layers and transport layers, as illustrated by various conventional data transport mechanisms, such as, for example and without limitation, Token-Ring, Ethernet, or other wireless or wire-based data communication mechanisms. Furthermore, while depicted in FIGURE 1 as a networked set of components, the system and method are capable of implementation on a stand-alone device adapted to perform the methods described herein.

[0029] The computer system 102 may include a computer server, workstation, personal computer, cellular telephone, tablet computer, pager, combination thereof, or other computing device capable of executing instructions for performing the exemplary method.

[0030] According to one example embodiment, the computer system 102 includes hardware, software, and/or any suitable combination thereof, configured to interact with an associated user, a networked device, networked storage, remote devices, or the like.

[0031] The memory 108 may represent any type of non-transitory computer readable medium such as random access memory (RAM), read only memory (ROM), magnetic disk or tape, optical disk, flash memory, or holographic memory. In one embodiment, the memory 108 comprises a combination of random access memory and read only memory. In some embodiments, the processor 104 and memory 108 may be combined in a single chip. The network interface(s) 134, 136 allow the computer to communicate with other devices via a computer network, and may comprise a modulator/demodulator (MODEM). Memory 108 may store data the processed in the method as well as the instructions for performing the exemplary method.

[0032] The digital processor 104 can be variously embodied, such as by a single core processor, a dual core processor (or more generally by a multiple core processor), a digital processor and cooperating math coprocessor, a digital controller, or the like. The digital processor 104, in addition to controlling the operation of the computer 102, executes instructions 106 stored in memory 108 for performing the method outlined in FIGURES 2-3.

[0033] The system 100 further includes an exemplary vehicle computer system 160 located in an associated vehicle 158, also referenced herein as an event data recorder 160, which is in communication with the emergency response computer system 102 via a suitable network, e.g., the Internet 101 , as discussed below. The vehicle computer system 160 may be configured to interact with one or more external sensors, devices, or the like, depicted in FIGURE 1 as Sensor A 176, Sensor B 178 through Sensor Z 189. It will be appreciated that vehicle 158 may include any number of sensors relating to not only the vehicle itself, the position of the vehicle, etc., but also sensors relating to the passengers, e.g., medical devices on the passengers, smart watches, smart phones, exercise monitors, or the like. The vehicle computer system 160 includes a processor 162, which performs portions of the exemplary method by execution of the predictive algorithm 118 via an associated analysis module 172 that is stored in memory 164 connected to the processor 162, as well as controlling the overall operation of the vehicle computer system 160.

[0034] It will also be appreciated that the vehicle computer system 160 may be implemented as a specific device configured to interact/communicate with the various sensors 176-180, a set of connected components (as illustrated) configured to interact with the emergency response computer system 102, the Internet 101 , and other devices or entities (not shown). As shown in FIGURE 1 , the vehicle computer system 160 includes a communications transceiver 176 coupled to the bus 166 and in communication with the processor 162. In one embodiment, the transceiver 176 is a cellular telephone transceiver, configured to communicate with the emergency response computer system 102 via the Internet 101 or other proprietary networks, to communicate with third party service providers (not shown) or other such systems.

[0035] The following, non-exhaustive listing, depicts examples of suitable sensors 176-180 that may be in communication with the vehicle computer system 160 and coupled to the vehicle or passengers located therein. It will be appreciated that other sensors or devices may also be used as sensors, e.g., electronic controls, automotive computers, etc. Such examples comprise, without limitation, accelerometers, brake sensors, gyroscopic sensors, temperature sensors, barometers, position sensors, GPS, speedometers, airbag deployment sensors, parking sensors, blind-spot detectors, heartrate monitors, blood pressure monitors, weight sensors, seatbelt sensors, and the like.

[0036] Suitable vehicle data 120, collected by the vehicle computer system or event data recorder 160 and used in both generating the predictive algorithm 118 is specified by the National Highway Transportation Safety Administration, codified in the Federal Register Vol. 77, No. 240, 49 C.F.R. §571 -Event Data Recorders, incorporated herein by reference in its entirety. Such exemplary data 120 includes, for example and without limitation, engine RPM, front/lateral force, event duration, vehicle speed, vehicle position, accelerator position, roll angle, airbag deployment, number of crashes, and the like. Further exemplary data 120 includes steering wheel angle, stability control engagement, occupant size, safety belt engagement, antilock brake activation, pretensioner or force limiter engagement, airbag deployment speed or faults, and the like. Furthermore, the structure and components of the vehicle computer system or event data recorder 160 are specified by the National Highway Transportation Safety Administration in regards to the survivability of the recorder 160 in a crash.

[0037] Associated with the system 100 are a plurality of care providers, illustrated in FIGURE 1 as hospital 1 146, hospital 2 148, and urgent care facility 150, respectively in communication with the emergency response computer system 102 and the vehicle computer system 160 via suitable communications links 152, 154, 156. It will be understood that the number of facilities 146-150 may change depending upon location associated with the vehicle 158. Further, the emergency response computer system 102 may be in direct communication with such facilities 146-150 so as to enable appropriate identification of each facility’s care capabilities, e.g. trauma levels, available personnel, available transport, available resources (capacity, medicines, etc.). An exemplary first responder 144 in direct communication with the emergency response computer system 102 is also shown as an example of what resources are capable of being dispatched in response to a crash/collision/accident of the vehicle 158. Such first responders 144 may include, for example and without limitation, aircraft, ambulances, police, fire department, non-emergency transport, and the like.

[0038] Turning now to FIGURE 2, there is shown a flowchart 200 illustrating a method utilized by the system 100 to generate a suitable predictive algorithm 118 in accordance with an exemplary embodiment of the subject application. In accordance with varying embodiments of the subject application, several of the operations presented in FIGURE 2 are performed by the artificial intelligence module 112, operable on the computer system 102 (as referenced above), or alternatively performed via a distributed implementation of the artificial intelligence module 112 (e.g., cloud-based, multiple processor embodiment). The methodology begins at 202, whereupon the communication module 110 or other suitable component associated with the emergency response computer system 102 receives vehicle data 120 from one or more vehicles 158 involved in an accident, collision, crash, incident, etc., in which one or more passengers were injured. It will be appreciated that such data collection may utilize real-time or past direct communications from a vehicle 158 to the system 102, or alternatively may be collected from a third-party service or governmental agency, e.g., NTSB, NFITSA, etc.

[0039] At 204, trauma data 122 is received by the communication module 110 or other suitable component associated with the computer system 102 corresponding to passengers involved in vehicular accidents/collisions. According to one embodiment, the received trauma data 122 utilizes known or accepted medical designations (see, e.g., FIGURE 4) to designate the type and severity of an injury sustained by a passenger. This data 122 may be received from various care facilities 146-150 by the computer system 102 which have treated passengers in such accidents or collisions. At 206, the artificial intelligence module 112 or other suitable component of the computer system 102 correlates the received vehicle data 120 with the received trauma data 122. That is, the particular vehicle data 122 that corresponds to the vehicle 158 in which a passenger associated with the trauma data 122 was in are correlated with each other, so as to associate particular types of vehicle data 120 with particular types of trauma, i.e. type and severity of injuries sustained. The artificial intelligence module 112, via operations of the processor 104 of the computer system 102 then generates, at 208, a predictive algorithm 118 that may be used to determine the type and severity of an injury in the event of a similar vehicular accident, i.e., a vehicular accident wherein the vehicle data is similar to the vehicle data corresponding to the particular type of trauma/injury sustained. This predictive algorithm 118 may be distributed to various emergency medical personnel, facilities 146-150, vehicles 158, and the like, for subsequent usage.

[0040] Referring now to FIGURE 3, there is shown a flowchart 300 illustrating a methodology of applying the predictive algorithm 118 in the event of a vehicular collision or accident in accordance with one embodiment of the subject application. Upon the occurrence of an accident, vehicle data 120 is received at 302 by the vehicle computer system 160 from the various sensors 176-180 associated with the vehicle 158 involved in the accident. In one embodiment, wherein the vehicle 158 is incapable of performing the analysis, the vehicle data 120 is communicated to the emergency response computer system 102 via any suitable means.

[0041] Thereafter, at 304, the vehicle data 120 corresponding to the accident, is input into the predictive algorithm 118 of the analysis module 173 located in memory 164 of the vehicle computer system 160 (or the analysis module 114 of the emergency response computer system 102). The probability of severity of injury and associated type of injury is then output by the analysis module 172, 114 at 306. At 308, the importation module 117 modifies the output into an appropriate electronic medical record format (as referenced above) and imports the probability of severity of injury and associated type(s) of injury into an electronic medical record 119 associated with the passenger. It will be understood by those skilled in the art that one or more emergency medical records 119 may be updated at 308 in accordance with the number of passengers determined to be present in the vehicle 158 (e.g., based upon the received vehicle data 120 such as seatbelts engaged, weight sensors, mobile devices detected, etc.). When the analysis performed at 306 is by the vehicle computer system 160, this output is thereafter communicated to the emergency response computer system 102 at 310. When, however, the analysis is performed by the emergency response computer system 102, operations proceed past 310 to 312.

[0042] At 312, the identification module 116 or other suitable component associated with the emergency response computer system 102 identifies the appropriate response personnel and/or vehicle 144 in accordance with the received probability of severity of injury and type of injury. It will be appreciated that such identification may include, but is not limited to, determining the appropriate mode of transport (e.g., ambulance, helicopter, non-emergency vehicle), the appropriate personnel (e.g., flight surgeon, EMT, flight nurse, etc.), and the like. At 314, the facility 146-150 most capable of receiving and caring for the passenger(s) as determined by the severity and type of injury is identified by the identification module 116 or the like of the emergency response computer system 102.

[0043] The emergency response computer system 102 then interfaces, via the communication module 110, with the identified first responder to dispatch the identified transport, personnel and alert the identified facility 146-150 of the probable severity and type of injury at 316. It will be understood that the foregoing thereby enables an appropriate response to a vehicular accident, informs first responders as to the probable type and severity of traumas of the passengers, informs facilities 146-150 to except incoming casualties, and ensures that community resources are appropriately allocated.

[0044] Some portions of the detailed description herein are presented in terms of algorithms and symbolic representations of operations on data bits performed by conventional computer components, including a central processing unit (CPU), memory storage devices for the CPU, and connected display devices. These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is generally perceived as a self-consistent sequence of steps leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like.

[0045] It should be understood, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise, as apparent from the discussion herein, it is appreciated that throughout the description, discussions utilizing terms such as "processing" or "computing" or "calculating" or "determining" or "displaying" or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.

[0046] The exemplary embodiment also relates to an apparatus for performing the operations discussed herein. This apparatus may be specially constructed for the required purposes, or it may comprise a general-purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a computer readable storage medium, such as, but is not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, and magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, or any type of media suitable for storing electronic instructions, and each coupled to a computer system bus.

[0047] The algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Various general-purpose systems may be used with programs in accordance with the teachings herein, or it may prove convenient to construct more specialized apparatus to perform the methods described herein. The structure for a variety of these systems is apparent from the description above. In addition, the exemplary embodiment is not described with reference to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the exemplary embodiment as described herein.

[0048] A machine-readable medium includes any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computer). For instance, a machine-readable medium includes read only memory ("ROM"); random access memory ("RAM"); magnetic disk storage media; optical storage media; flash memory devices; and electrical, optical, acoustical or other form of propagated signals (e.g., carrier waves, infrared signals, digital signals, etc.), just to mention a few examples.

[0049] The methods illustrated throughout the specification, may be implemented in a computer program product that may be executed on a computer. The computer program product may comprise a non-transitory computer-readable recording medium on which a control program is recorded, such as a disk, hard drive, or the like. Common forms of non-transitory computer-readable media include, for example, floppy disks, flexible disks, hard disks, magnetic tape, or any other magnetic storage medium, CD-ROM, DVD, or any other optical medium, a RAM, a PROM, an EPROM, a FLASH-EPROM, or other memory chip or cartridge, or any other tangible medium from which a computer can read and use.

[0050] Alternatively, the method may be implemented in transitory media, such as a transmittable carrier wave in which the control program is embodied as a data signal using transmission media, such as acoustic or light waves, such as those generated during radio wave and infrared data communications, and the like.

[0051] It will be appreciated that variants of the above-disclosed and other features and functions, or alternatives thereof, may be combined into many other different systems or applications. Various presently unforeseen or unanticipated alternatives, modifications, variations or improvements therein may be subsequently made by those skilled in the art which are also intended to be encompassed by the following claims.

[0052] To aid the Patent Office and any readers of this application and any resulting patent in interpreting the claims appended hereto, applicants do not intend any of the appended claims or claim elements to invoke 35 U.S.C. 1 12(f) unless the words“means for” or“step for” are explicitly used in the particular claim.

Claims

CLAIMS:

1. An automated system for triaging vehicular accidents, comprising: an emergency response computer system, comprising:

a processor in communication with memory; and a communication module in communication with the processor, the communication module configured to communicate via a distributed computer network with at least one vehicle;

wherein the memory stores instructions which are executed by the processor to:

receive vehicle data corresponding to a plurality of vehicles involved in vehicular accidents,

receive trauma data corresponding to a plurality of passengers involved in the vehicular accidents,

correlate the vehicle data with the trauma data, and generate a predictive algorithm as to the severity and type of injury in accordance with the correlated vehicle and trauma data.

2. The automated system for triaging vehicular accidents of claim 1 , wherein the correlation of the vehicle data with the trauma data and the generation of the predictive algorithm are performed by an artificial intelligence component.

3. The automated system for triaging vehicular accidents of claim 2, wherein the vehicle data is received from a plurality of event data recorders of an associated plurality of vehicles involved in the vehicular accidents.

4. The automated system for triaging vehicular accidents of claim 3, wherein the vehicle data is received in at least one of real-time or past direct communications from the plurality of event data recorders.

5. The automated system for triaging vehicular accidents of any of claims 1 -3, wherein the trauma data corresponds to passengers involved in vehicular accidents.

6. The automated system for triaging vehicular accidents of claim 5, wherein the trauma data includes medical designations designating the type and severity of an injury sustained by the passenger.

7. An automated system for triaging vehicular accidents, comprising: a vehicle computer system, comprising:

a processor in communication with memory, and an analysis module in communication with the processor; and at least one sensor in communication with the vehicle computer system, the at least one sensor collecting data associated with a vehicle; wherein the memory stores instructions which are executed by the processor to:

receive data from the at least one sensor indicative of a collision of the vehicle,

determine a probability of severity and type of injury in accordance with a predictive algorithm and the analysis module, and communicate the output of the probability of severity and type of injury to an emergency response computer system via an associated communications network.

8. The automated system for triaging vehicular accidents of claim 7, wherein the emergency response computer system comprises an importation module configured to import the output of the probability of severity and type of injury into an electronic medical record.

9. The automated system for triaging vehicular accidents of claim 8, wherein the importation module is further configured to format the output into a format used by the electronic medical record.

10. The automated system for triaging vehicular accidents of any of claims 8-9, wherein the emergency response computer system further comprises an identification module configured to identify at least one of an appropriate response vehicle or personnel responsive to the output type and severity of injury probability of severity and type of injury.

1 1 . The automated system for triaging vehicular accidents of claim 10, wherein the identification module is further configured to identify an appropriate medical facility responsive to the output type and severity of injury probability of severity and type of injury.

12. The automated system for triaging vehicular accidents of any of claims 7-1 1 , wherein the vehicle data includes at least one of engine RPM, front/lateral force, event duration, vehicle speed, vehicle position, accelerator position, roll angle, airbag deployment, or number of crashes.

13. The automated system for triaging vehicular accidents of claim 12, wherein the vehicle data includes a number of passengers in the vehicle at the time of the collision of the vehicle.

14. A method for automatically triaging vehicular accidents, comprising: receiving, with a processor in communication with memory, from a plurality of vehicles, vehicle data corresponding to operations of each of the plurality of vehicles and incidents corresponding thereto;

receiving trauma data corresponding to a type and a severity of injury of at least one passenger in each of the plurality of vehicles;

correlating, with the processor, the trauma data with the corresponding vehicle data; and

generating a predictive algorithm in accordance with the correlated trauma and vehicle data.

15. The method for automatically triaging vehicular accidents, wherein the wherein the correlating of the vehicle data with the trauma data and the generating of the predictive algorithm are performed by an artificial intelligence module in communication with the processor.

16. The method for automatically triaging vehicular accidents of claim 15, wherein the vehicle data is received from a plurality of event data recorders corresponding to the plurality of vehicles.

17. The method for automatically triaging vehicular accidents of claim 16, wherein the vehicle data is received in at least one of real-time or past direct communications from the plurality of event data recorders.

18. The method for automatically triaging vehicular accidents of claim 17, wherein the trauma data includes medical designations designating the type and severity of an injury sustained by the passenger.

19. A method for automatically triaging a passenger of a vehicular accident, comprising:

receiving vehicle data corresponding to a vehicle in an accident from a vehicle computer system of the vehicle;

analyzing, in accordance with a predictive algorithm, the received vehicle data;

determining, in accordance with an output of the predictive algorithm, a probability of a type and a severity of injury of the passenger;

communicating, via a distributed communications network, the probability of the type and the severity of injury to the passenger to an emergency response system;

identifying, from the probability of the type and the severity of injury, an appropriate emergency response; and

dispatching the identified appropriate emergency response to the passenger of the vehicle.

20. The method for automatically triaging vehicular accidents of claim 19, further comprising formatting the output the probability of severity and type of injury into a format used by an electronic medical record.

21. The method for automatically triaging vehicular accidents of claim 20, further comprising communicating the formatted output of the probability of severity and type of injury into the electronic medical record.