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WO2025080267A1 - System, method, and computer program product for providing machine learning based analytics of healthcare information - Google Patents

System, method, and computer program product for providing machine learning based analytics of healthcare information Download PDF

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Publication number
WO2025080267A1
WO2025080267A1 PCT/US2023/076566 US2023076566W WO2025080267A1 WO 2025080267 A1 WO2025080267 A1 WO 2025080267A1 US 2023076566 W US2023076566 W US 2023076566W WO 2025080267 A1 WO2025080267 A1 WO 2025080267A1
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WO
WIPO (PCT)
Prior art keywords
patient
healthcare
data associated
information
processor
Prior art date
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Pending
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PCT/US2023/076566
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French (fr)
Inventor
Ankur-Aaron SHARMA
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Bayer Healthcare LLC
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Bayer Healthcare LLC
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Filing date
Publication date
Application filed by Bayer Healthcare LLC filed Critical Bayer Healthcare LLC
Priority to PCT/US2023/076566 priority Critical patent/WO2025080267A1/en
Publication of WO2025080267A1 publication Critical patent/WO2025080267A1/en
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

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Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Definitions

  • Clause 8 The system of any of clauses 1-7, wherein the proposed diagnosis comprises one of a proposed differential diagnosis and a proposed final diagnosis.
  • Clause 9 A method for providing machine learning analytics of healthcare information, comprising: receiving healthcare data associated with a patient from a plurality of data sources, wherein the plurality of data sources comprises at least one of the following: an electronic medical record (EMR) system, an electronic health record (EHR) system, a patient procedure tracking system, a hospital information system (HIS), a radiology information system (RIS), a radiology analytics system (RAS), a laboratory information system (LIS), a digital pathology system (DPS), a picture archive and communication system (PACS), or any combination thereof; generating a patient health history profile based on the healthcare data associated Atty Ref.
  • EMR electronic medical record
  • EHR electronic health record
  • HIS hospital information system
  • RIS radiology information system
  • RAS radiology analytics system
  • LIS laboratory information system
  • DPS digital pathology system
  • PPS picture archive and
  • a computer program product for providing machine learning analytics of healthcare information comprising at least one non-transitory computer-readable medium including one or more instructions that, when executed by at least one processor, cause the at least one processor to: receive healthcare data associated with a patient from a plurality of data sources, wherein the plurality of data sources comprises at least one of the following: an electronic medical record (EMR) system, an electronic health record (EHR) system, a patient procedure tracking system, a hospital information system (HIS), a radiology information system (RIS), a radiology analytics system (RAS), a laboratory information system (LIS), a digital pathology system (DPS), a picture archive and communication system (PACS), or any combination thereof; generate a patient health history profile based on the healthcare data associated with the patient; determine a medical finding for the patient based on the patient health history profile using a machine learning model, wherein the medical finding includes at least one of a proposed diagnosis for the patient or a recommendation to perform one
  • EMR electronic medical record
  • EHR electronic health record
  • HIS
  • Clause 26 The system of clause 25, wherein, when processing the healthcare data associated with the patient, the at least one processor is programmed Atty Ref. BHC219020WO or configured to: arrange the healthcare data associated with the patient in a sequence for a time period.
  • Clause 27 The system of clause 25 or 26, wherein the at least one processor is programmed or configured to: collect the healthcare data associated with the patient from the plurality of data sources and store the healthcare data associated with the patient in a data structure.
  • Clause 28 The system of any of clauses 25-27, wherein the patient health history profile for the patient comprises a longitudinal dataset of healthcare data for the patient over a time period.
  • a method for providing machine learning analytics of healthcare information comprising: receiving, with at least one processor, healthcare data associated with a patient from a plurality of data sources, wherein the plurality of data sources comprises at least one of the following: an electronic medical record (EMR) system, an electronic health record (EHR) system, a patient procedure tracking system, a hospital information system (HIS), a radiology information system (RIS), a radiology analytics system (RAS), a laboratory information system (LIS), a digital pathology system (DPS), a picture archive and communication system (PACS), or any combination thereof; processing, with the at least one processor, the healthcare data associated with the patient; and generating, with the at least one processor, a patient health history profile based on processing the healthcare data associated with the patient.
  • EMR electronic medical record
  • EHR electronic health record
  • HIS hospital information system
  • RIS radiology information system
  • RAS radiology analytics system
  • LIS laboratory information system
  • DPS digital pathology system
  • PES picture archive and communication system
  • Clause 34 The computer program product of clause 25, wherein the one or more instructions that cause the at least one processor to process the healthcare data associated with the patient cause the at least one processor to: arrange the healthcare data associated with the patient in a sequence for a time period.
  • Clause 35 The computer program product of clause 25, wherein the one or more instructions further cause the at least one processor to: collect the healthcare data associated with the patient from the plurality of data sources and store the healthcare data associated with the patient in a data structure.
  • Clause 36 The computer program product of clause 25, wherein the patient health history profile comprises a longitudinal dataset of healthcare data for the patient over a time period.
  • the terms “has,” “have,” “having,” or the like are intended to be open-ended terms. Further, the phrase “based on” is intended to mean “based at least partially on” unless explicitly stated otherwise.
  • the terms “communication” and “communicate” may refer to the reception, receipt, transmission, transfer, provision, and/or the like of information (e.g., data, signals, messages, instructions, commands, and/or the like).
  • one unit e.g., a device, a system, a component of a device or system, combinations thereof, and/or the like
  • to be in communication with another unit means that the one unit is able to directly or indirectly receive information from and/or transmit information to the other unit.
  • a message may refer to a network packet (e.g., a data packet and/or the like) that includes data. It will be appreciated that numerous other arrangements are possible.
  • the term “system” may refer to one or more computing devices or combinations of computing devices such as, but not limited to, processors, servers, client devices, software applications, and/or other like components.
  • reference to “a server” or “a processor,” as used herein may refer to a previously-recited server and/or processor that is recited as performing a previous step or function, a different server and/or processor, and/or a combination of servers and/or processors.
  • a first server and/or a first processor that is recited as performing a first step or function may refer Atty Ref. BHC219020WO to the same or different server and/or a processor recited as performing a second step or function.
  • Non-limiting embodiments of the present disclosure are directed to systems, methods, and computer program products for providing machine learning based analytics of healthcare information.
  • an Artificial Intelligence (AI) shuttle system may receive healthcare data associated with a patient from a plurality of data sources, wherein the plurality of data sources may include at least one of the following: an electronic medical record (EMR) system, an electronic health record (EHR) system, a patient procedure tracking system, a hospital information system (HIS), a radiology information system (RIS), a radiology analytics system (RAS), a laboratory information system (LIS), a digital pathology system (DPS), a picture archive and communication system (PACS), or any combination thereof, determine a medical finding for the patient using a machine learning model based on the healthcare data associated with the patient, wherein the medical finding includes at least one of a proposed diagnosis for the patient or a recommendation to perform one or more tests on the patient, and provide data associated with the medical finding for the patient to a device associated with the patient for display.
  • EMR electronic medical record
  • EHR electronic health record
  • HIS hospital information system
  • RIS radiology information system
  • RAS radiology analytics system
  • LIS laboratory
  • the AI shuttle system may generate a patient profile based on the healthcare data associated with the patient.
  • the AI shuttle system may provide a web-accessible link to the patient profile to allow patient information to be displayed on the device and to allow the patient information to be received from the device via the web-accessible link.
  • the AI shuttle system may receive demographic information associated with the patient and generate the patient profile based on the demographic information associated with the patient.
  • the AI shuttle system may receive protected health information associated with the patient and generate the patient profile based on the protected health information associated with the patient.
  • data source 104 may include additional devices located at a location, such as a hospital, which provides medical care, such as an Internet of Things (IOT) device, and/or other devices at locations relevant to a patient (e.g., a device at a home of a patient, a device located in a vehicle of a patient, etc.).
  • IOT Internet of Things
  • user device 106 may include one or more devices capable of being in communication with AI shuttle system 102, data sources 104, fluid injection system 108, and/or hospital information system 110 via communication network 112.
  • fluid injection system 108 is configured to inject a dose of contrast fluid along with and/or followed by administration of a particular volume of the aqueous fluid.
  • fluid injection system 108 may include one or more exemplary fluid injection devices that are disclosed in: U.S. Patent Application Serial No. 09/715,330, filed on November 17, 2000, issued as U.S. Patent No. 6,643,537; U.S. Patent Application Serial No. 09/982,518, filed on October 18, 2001, issued as U.S. Patent No. 7,094,216; U.S. Patent Application Serial No. 10/825,866, filed on April 16, 2004, issued as U.S. Patent No. 7,556,619; U.S.
  • communication network 112 may include one or more wired and/or wireless networks.
  • communication network 112 may include a cellular network (e.g., a long-term evolution (LTE ® ) network, a third generation (3G) network, a fourth generation (4G) network, a fifth generation (5G) network, a sixth generation (6G) network, a code division multiple access (CDMA) network, etc.), a public land mobile network (PLMN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a telephone network (e.g., the public switched telephone network (PSTN)), a private network, an ad hoc network, an intranet, the Internet, a fiber optic-based network, a cloud computing network, a short range wireless communication network (e.g., a Bluetooth ® network, a near field communication (NFC) network, etc.) and/or the like, and/or a combination of these or other types
  • LTE ® long-term evolution
  • AI shuttle system 102 may interconnect (e.g., establish a connection to communicate with and/or the like) fluid injection system 108, workstation device 206, hospital information system 210, EMR system 212, EHR system 213, digital pathology system 214, medical imaging system 216, and/or laboratory information system 218 via wired connections, wireless connections, or a combination of wired and wireless connections.
  • workstation device 206 may be the same as or similar to user device 106.
  • hospital information system 210 may be the same as or similar to hospital information system 110.
  • hospital information system 210 may include a plurality of subsystems.
  • AI shuttle system 102 may receive data associated with a patient procedure from hospital information system 210 (e.g., from patient procedure tracking system 210A) via the communication network according to a Digital Imaging and Communications in Medicine (DICOM) communications protocol, data associated with an operation of the fluid injection system 108 from hospital information system 210 via the communication network based on an API call (e.g., an API call Atty Ref.
  • DICOM Digital Imaging and Communications in Medicine
  • EMR system 212 may include one or more devices capable of being in communication with AI shuttle system 102, workstation device 206, fluid injection system 108, hospital information system 210, EMR system 212, digital pathology system 214, medical imaging system 216, and/or laboratory information system 218 via a communication network (e.g., communication network 114).
  • a communication network e.g., communication network 114
  • EHR system 213 may include one or more devices capable of being in communication with AI shuttle system 102, workstation device 206, fluid injection system 108, hospital information system 210, EMR system 212, digital pathology system 214, medical imaging system 216, and/or laboratory information system 218 via a communication network (e.g., communication network 112).
  • a communication network e.g., communication network 112
  • EHR system 213 may include one or more devices that receive, manage, store, and/or transmit electronic health records that include medical record data associated with a medical record of a patient, such as demographics, medical history, medication and allergies, immunization status, laboratory test results, radiology images, vital signs, personal statistics (e.g., age, weight, height, etc.), billing information, and/or the like, associated with various providers and/or locations (e.g., offices, clinics, hospitals, etc.) of medical care. Atty Ref. BHC219020WO Additionally or alternatively, EHR system 213 may include a patient portal (e.g., a web- based interface) to allow a patient to interact with a respective electronic medical record for the patient.
  • a patient portal e.g., a web- based interface
  • EMR system 212 may be a data source for EHR system 213.
  • digital pathology system 214 may include one or more devices capable of being in communication with AI shuttle system 102, workstation device 206, fluid injection system 108, hospital information system 210, EMR system 212, EHR system 213, medical imaging system 216, and/or laboratory information system 218 via a communication network (e.g., communication network 112 in FIG.1).
  • medical imaging system 216 may include one or more devices that receive, manage, transmit, and/or interpret pathology information including data (e.g., image data, slide data, etc.) analyzed by a microscope, a scanner, and/or other like devices.
  • AI shuttle system 102 may provide a communication interface between hospital information system 210 and fluid injection system 108 such that fluid injection system 108 is able to receive data based on an API call from fluid injection system 108 to AI shuttle system 102.
  • AI shuttle system 102 may transmit data associated with informatics received from hospital information system 210 to fluid injection system 108 via a communication network (e.g., communication network 112).
  • AI shuttle system 102 may transmit data associated with informatics received from hospital information system 210 to fluid injection system 108 via the communication network based on an API call from fluid injection system 108.
  • FIG.3 is a diagram of example components of device 300.
  • Device 300 may correspond to one or more devices of AI shuttle system 102, data source 104, user device 106, workstation device 206, one or more devices of fluid injection system 108, one or more devices of hospital information system 110, one or more devices of hospital information system 210, one or more devices of EMR system 212, one or more devices of EHR system 213, one or more devices of digital pathology system 214, one or more devices of medical imaging system 216, and/or one or more devices of laboratory information system 218.
  • AI shuttle system 102, data source 104, user device 106, workstation device 206, fluid injection system 108, hospital information system 110, hospital information system 210, EMR system 212, EHR system 213, digital pathology system 214, medical imaging system 216, and/or laboratory information system 218 may include at least one device 300 and/or at least one component of device 300.
  • device 300 may include bus 302, processor 304, memory Atty Ref. BHC219020WO 306, storage component 308, input component 310, output component 312, and communication interface 314.
  • Bus 302 may include a component that permits communication among the components of device 300.
  • Memory 306 may include random access memory (RAM), read only memory (ROM), and/or another type of dynamic or static storage device (e.g., flash memory, magnetic memory, optical memory, etc.) that stores information and/or instructions for use by processor 304.
  • Storage component 308 may store information and/or software related to the operation and use of device 300.
  • storage component 308 may include a hard disk (e.g., a magnetic disk, an optical disk, a magneto-optic disk, a solid state disk, etc.), a compact disc (CD), a digital versatile disc (DVD), a floppy disk, a cartridge, a magnetic tape, and/or another type of computer-readable medium, along with a corresponding drive.
  • Communication interface 314 may include a transceiver-like component (e.g., a transceiver, a separate receiver and transmitter, etc.) that enables device 300 to communicate with other devices, such as via a wired connection, a wireless connection, or a combination of wired and wireless connections.
  • Communication interface 314 may permit device 300 to receive information from another device and/or provide information to another device.
  • communication interface 314 may include an Ethernet interface, an optical interface, a coaxial interface, an infrared Atty Ref. BHC219020WO interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, a Wi-Fi ® interface, a cellular network interface, and/or the like.
  • RF radio frequency
  • USB universal serial bus
  • Device 300 may perform one or more processes described herein. Device 300 may perform these processes based on processor 304 executing software instructions stored by a computer-readable medium, such as memory 306 and/or storage component 308.
  • a computer-readable medium e.g., a non-transitory computer-readable medium
  • a memory device may include memory space located inside of a single physical storage device or memory space spread across multiple physical storage devices.
  • Software instructions may be read into memory 306 and/or storage component 308 from another computer-readable medium or from another device via communication interface 314. When executed, software instructions stored in memory 306 and/or storage component 308 may cause processor 304 to perform one or more processes described herein.
  • FIG.4 is a flowchart of a non-limiting embodiment of a process 400 for providing machine learning analytics of healthcare information.
  • one or more of the steps of process 400 are performed (e.g., completely, partially, etc.) by AI shuttle system 102.
  • the healthcare data associated with a patient may include medical record data associated with a medical record of a patient, protected health information associated with the patient, demographic information associated with the patient, identification data associated with an identifier of a patient, data associated with a patient examination procedure (e.g., a fluid injection procedure and/or a medical imaging procedure performed on a patient), such as data associated with a contrast fluid provided during a fluid injection procedure, a gauge of a catheter used during a fluid injection procedure, and a fluid injection protocol for a fluid injection procedure.
  • AI shuttle system 102 may store the healthcare data associated with the patient in a data structure (e.g., a database).
  • AI shuttle system 102 may provide a web- accessible link to a patient profile to allow patient information, such as a medical finding for a patient, to be displayed on a device (e.g., user device 106, a display unit 206A of workstation device 206, etc.) and/or to allow patient information to be received from the device via the web-accessible link.
  • AI shuttle system 102 may provide a user interface with the web-accessible link and AI shuttle system 102 may receive a selection of an identifier of a patient via a user interface.
  • AI shuttle system 102 may receive the selection of the identifier based on user input.
  • AI shuttle system 102 may determine that healthcare data for the patient is not complete for the time period.
  • AI shuttle system 102 may collect healthcare data from all data sources 104 for each patient of a plurality of patients and store the healthcare data associated with each patient of the plurality of patients individually in a data structure (e.g., a database).
  • AI shuttle system 102 when processing the healthcare data associated with the patient, may detect whether an error is present in the healthcare data in each data source 104 relative to healthcare data in one or more of other data sources 104. In some non-limiting embodiments, AI shuttle system 102 may correct an error that is detected based on detecting the error. Atty Ref. BHC219020WO Additionally or alternatively, AI shuttle system 102 may provide a notification (e.g., a notification message to an operator) with regard to an error that is detected. In some non-limiting embodiments, AI shuttle system 102 may provide a prompt (e.g., a prompt in a user interface) to accept or decline a proposed correction of the error.
  • a prompt e.g., a prompt in a user interface
  • FIGS. 6A–6C are diagrams of a non-limiting embodiment or aspect of implementation 600 relating to a process (e.g., process 600) for providing machine learning analytics of healthcare information.
  • AI shuttle system 102 may provide data associated with the medical finding for the patient and provide a web-accessible link to the patient profile to a user device (e.g., user device 106, workstation 206 and/or the like) via communication network 112.
  • a user device e.g., user device 106, workstation 206 and/or the like

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  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Public Health (AREA)
  • Epidemiology (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Data Mining & Analysis (AREA)
  • Biomedical Technology (AREA)
  • Databases & Information Systems (AREA)
  • Pathology (AREA)
  • Measuring And Recording Apparatus For Diagnosis (AREA)

Abstract

Systems for providing machine learning analytics of healthcare information may include at least one processor to: receive healthcare data associated with a patient from a plurality of data sources including at least one of an electronic medical record system, a patient procedure tracking system, a hospital information system, a radiology information system, a radiology analytics system, a laboratory information system, a digital pathology system, a picture archive and communication system, or any combination thereof; determine a medical finding for the patient using a machine learning model based on the healthcare data associated with the patient, wherein the medical finding includes at least one of a proposed diagnosis for the patient or a recommendation to perform one or more tests on the patient: and provide data associated with the medical finding for the patient to a device associated with the patient for display. Methods and computer program products are also disclosed.

Description

Atty Ref. BHC219020WO SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT FOR PROVIDING MACHINE LEARNING BASED ANALYTICS OF HEALTHCARE INFORMATION BACKGROUND 1. Field [0001] This disclosure relates generally to systems and/or devices that provide healthcare information and, in some non-limiting embodiments, to systems, methods, and computer program products for providing machine learning based analytics of healthcare information. 2. Technical Considerations [0002] Health informatics may refer to a field of science and engineering that develops methods and technologies for acquiring, processing, and/or studying of healthcare information (e.g., medical data, patient data, etc.) associated with a patient. In some instances, the healthcare data may come from different sources and/or modalities, such as electronic medical records (e.g., electronic health records), diagnostic test results, and/or medical scans. Health informatics applications may include solutions to problems encountered with medical data and analysis of such medical data using computational techniques. [0003] Artificial intelligence (AI) may be used in healthcare as a way to mimic human cognition in analysis, presentation, and/or comprehension of healthcare data. AI may describe the ability of computer programs, such as computer algorithms, to approximate conclusions based on input data, which may be medical data. In some instances, computer algorithms can be used to recognize patterns in data and create logic for identifying such patterns. Such computer algorithms may include machine learning models that are trained to perform certain tasks using extensive amounts of input data. [0004] However, health informatics applications may be implemented in a setting where complex programming and processing resources are required to receive input data, in the form of healthcare information associated with a patient, to the health informatics applications for data analysis tasks. Accordingly, manually programmed processes that are developed for providing the input data to the health informatics applications may require intensive amounts of network resources, may require manual updating, and may be inaccurate. Furthermore, a device that runs health informatics Atty Ref. BHC219020WO applications may require specially configured network equipment and/or reprogramming in order to communicate with various medical devices and information systems based on a network communication configuration. SUMMARY [0005] Accordingly, provided are systems, methods, and computer program products for providing machine learning based analytics of healthcare information. [0006] Further non-limiting embodiments or aspects are set forth in the following numbered clauses: [0007] Clause 1: A system for providing machine learning analytics of healthcare information, comprising: at least one processor programmed or configured to: receive healthcare data associated with a patient from a plurality of data sources, wherein the plurality of data sources comprises at least one of the following: an electronic medical record (EMR) system, an electronic health record (EHR) system, a patient procedure tracking system, a hospital information system (HIS), a radiology information system (RIS), a radiology analytics system (RAS), a laboratory information system (LIS), a digital pathology system (DPS), a picture archive and communication system (PACS), or any combination thereof; generate a patient health history profile based on the healthcare data associated with the patient; determine a medical finding for the patient based on the patient health history profile using a machine learning model, wherein the medical finding includes at least one of a proposed diagnosis for the patient or a recommendation to perform one or more tests on the patient; and provide data associated with the medical finding for the patient to a device associated with the patient for display. [0008] Clause 2: The system of clause 1, wherein, when generating the patient health history profile based on the healthcare data associated with the patient, the at least one processor is programmed or configured to: receive the healthcare data associated with the patient; determine a longitudinal healthcare dataset for the patient; and generate the patient health history profile based on the longitudinal healthcare dataset. [0009] Clause 3: The system of clause 2, wherein the at least one processor is further programmed or configured to: provide a web-accessible link to the patient health history profile to allow patient information to be displayed on the device and to Atty Ref. BHC219020WO allow the patient information to be received from the device via the web-accessible link. [0010] Clause 4: The system of clauses 1-3, wherein, when generating the patient health history profile, the at least one processor is programmed or configured to: receive demographic information associated with the patient; and generate the patient health history profile based on the demographic information associated with the patient. [0011] Clause 5: The system of any of clauses 1-4, wherein, when generating the patient health history profile, the at least one processor is programmed or configured to: receive protected health information associated with the patient; and generate the patient health history profile based on the protected health information associated with the patient. [0012] Clause 6: The system of any of clauses 1-5, wherein, when receiving the healthcare data associated with the patient, the at least one processor is programmed or configured to: retrieve the healthcare data associated with the patient from at least one of the data sources of the plurality of data sources based on a unique patient identifier associated with the patient. [0013] Clause 7: The system of any of clauses 1-6, wherein, when providing the data associated with the medical finding for the patient, the at least one processor is programmed or configured to: provide the data associated with the medical finding for the patient to the device associated with the patient based on receiving a request for the medical finding that includes a unique patient identifier associated with the patient. [0014] Clause 8: The system of any of clauses 1-7, wherein the proposed diagnosis comprises one of a proposed differential diagnosis and a proposed final diagnosis. [0015] Clause 9: A method for providing machine learning analytics of healthcare information, comprising: receiving healthcare data associated with a patient from a plurality of data sources, wherein the plurality of data sources comprises at least one of the following: an electronic medical record (EMR) system, an electronic health record (EHR) system, a patient procedure tracking system, a hospital information system (HIS), a radiology information system (RIS), a radiology analytics system (RAS), a laboratory information system (LIS), a digital pathology system (DPS), a picture archive and communication system (PACS), or any combination thereof; generating a patient health history profile based on the healthcare data associated Atty Ref. BHC219020WO with the patient; determining a medical finding for the patient based on the patient health history profile using a machine learning model, wherein the medical finding includes at least one of a proposed diagnosis for the patient or a recommendation to perform one or more tests on the patient; and providing data associated with the medical finding for the patient to a device associated with the patient for display. [0016] Clause 10: The method of clause 9, wherein generating a patient health history profile based on the healthcare data associated with the patient comprises receiving the healthcare data associated with the patient; determining a longitudinal healthcare dataset for the patient; and generating the patient health history profile based on the longitudinal healthcare dataset. [0017] Clause 11: The method of clause 10, further comprising: providing a web- accessible link to the patient health history profile to allow patient information to be displayed on the device and to allow the patient information to be received from the device via the web-accessible link. [0018] Clause 12: The method of clauses 10 or 11, wherein generating the patient health history profile comprises: receiving demographic information associated with the patient; and generating the patient health history profile based on the demographic information associated with the patient. [0019] Clause 13: The method of any of clauses 10-12, wherein generating the patient health history profile comprises: receiving protected health information associated with the patient; and generating the patient health history profile based on the protected health information associated with the patient. [0020] Clause 14: The method of any of clauses 9-13, wherein receiving the healthcare data associated with the patient comprises: retrieve the healthcare data associated with the patient from at least one of the data sources of the plurality of data sources based on a unique patient identifier associated with the patient. [0021] Clause 15: The method of any of clauses 9-14, wherein providing the data associated with the medical finding for the patient comprises: providing the data associated with the medical finding for the patient to the device associated with the patient based on receiving a request for the medical finding that includes a unique patient identifier associated with the patient. [0022] Clause 16: The method of any of clauses 9-15, wherein the proposed diagnosis comprises one of a proposed differential diagnosis and a proposed final diagnosis. Atty Ref. BHC219020WO [0023] Clause 17: A computer program product for providing machine learning analytics of healthcare information, the computer program product comprising at least one non-transitory computer-readable medium including one or more instructions that, when executed by at least one processor, cause the at least one processor to: receive healthcare data associated with a patient from a plurality of data sources, wherein the plurality of data sources comprises at least one of the following: an electronic medical record (EMR) system, an electronic health record (EHR) system, a patient procedure tracking system, a hospital information system (HIS), a radiology information system (RIS), a radiology analytics system (RAS), a laboratory information system (LIS), a digital pathology system (DPS), a picture archive and communication system (PACS), or any combination thereof; generate a patient health history profile based on the healthcare data associated with the patient; determine a medical finding for the patient based on the patient health history profile using a machine learning model, wherein the medical finding includes at least one of a proposed diagnosis for the patient or a recommendation to perform one or more tests on the patient; and provide data associated with the medical finding for the patient to a device associated with the patient for display. [0024] Clause 18: The computer program product of clause 17, wherein the one or more instructions that cause the at least one processor to generate a patient health history profile based on the healthcare data associated with the patient cause the at least one processor to: receive the healthcare data associated with the patient; determine a longitudinal healthcare dataset for the patient; and generate a patient health history profile based on the longitudinal healthcare dataset. [0025] Clause 19: The computer program product of clause 18, wherein the one or more instructions further cause the at least one processor to: provide a web-accessible link to the patient health history profile to allow patient information to be displayed on the device and to allow the patient information to be received from the device via the web-accessible link. [0026] Clause 20: The computer program product of clauses 18 or 19, wherein, the one or more instructions that cause the at least one processor to generate the patient health history profile cause the at least one processor to: receive demographic information associated with the patient; and generate the patient health history profile based on the demographic information associated with the patient. Atty Ref. BHC219020WO [0027] Clause 21: The computer program product of any of clauses 18-20, wherein, the one or more instructions that cause the at least one processor to generate the patient health history profile cause the at least one processor to: receive protected health information associated with the patient; and generate the patient health history profile based on the protected health information associated with the patient. [0028] Clause 22: The computer program product of any of clauses 17-21, wherein, the one or more instructions that cause the at least one processor to receive the healthcare data associated with the patient cause the at least one processor to: retrieve the healthcare data associated with the patient from at least one of the data sources of the plurality of data sources based on a unique patient identifier associated with the patient. [0029] Clause 23: The computer program product of any of clauses 17-22, wherein, the one or more instructions that cause the at least one processor to provide the data associated with the medical finding for the patient cause the at least one processor to: provide the data associated with the medical finding for the patient to the device associated with the patient based on receiving a request for the medical finding that includes a unique patient identifier associated with the patient. [0030] Clause 24: The computer program product of any of clauses 17-23, wherein the proposed diagnosis comprises one of a proposed differential diagnosis and a proposed final diagnosis. [0031] Clause 25: A system for providing machine learning analytics of healthcare information, comprising: at least one processor programmed or configured to: receive healthcare data associated with a patient from a plurality of data sources, wherein the plurality of data sources comprises at least one of the following: an electronic medical record (EMR) system, an electronic health record (EHR) system, a patient procedure tracking system, a hospital information system (HIS), a radiology information system (RIS), a radiology analytics system (RAS), a laboratory information system (LIS), a digital pathology system (DPS), a picture archive and communication system (PACS), or any combination thereof; process the healthcare data associated with the patient; and generate a patient health history profile based on processing the healthcare data associated with the patient. [0032] Clause 26: The system of clause 25, wherein, when processing the healthcare data associated with the patient, the at least one processor is programmed Atty Ref. BHC219020WO or configured to: arrange the healthcare data associated with the patient in a sequence for a time period. [0033] Clause 27: The system of clause 25 or 26, wherein the at least one processor is programmed or configured to: collect the healthcare data associated with the patient from the plurality of data sources and store the healthcare data associated with the patient in a data structure. [0034] Clause 28: The system of any of clauses 25-27, wherein the patient health history profile for the patient comprises a longitudinal dataset of healthcare data for the patient over a time period. [0035] Clause 29: A method for providing machine learning analytics of healthcare information, comprising: receiving, with at least one processor, healthcare data associated with a patient from a plurality of data sources, wherein the plurality of data sources comprises at least one of the following: an electronic medical record (EMR) system, an electronic health record (EHR) system, a patient procedure tracking system, a hospital information system (HIS), a radiology information system (RIS), a radiology analytics system (RAS), a laboratory information system (LIS), a digital pathology system (DPS), a picture archive and communication system (PACS), or any combination thereof; processing, with the at least one processor, the healthcare data associated with the patient; and generating, with the at least one processor, a patient health history profile based on processing the healthcare data associated with the patient. [0036] Clause 30: The method of clause 29, wherein processing the healthcare data associated with the patient comprises: arranging the healthcare data associated with the patient in a sequence for a time period. [0037] Clause 31: The method of clause 29 or 30, wherein the method further comprises: collecting the healthcare data associated with the patient from the plurality of data sources and storing the healthcare data associated with the patient in a data structure. [0038] Clause 32: The method of any of clauses 29-31, wherein the patient health history profile for the patient comprises a longitudinal dataset of healthcare data for the patient over a time period. [0039] Clause 33: A computer program product for providing machine learning analytics of healthcare information, the computer program product comprising at least one non-transitory computer-readable medium including one or more instructions that, Atty Ref. BHC219020WO when executed by at least one processor, cause the at least one processor to: receive healthcare data associated with a patient from a plurality of data sources, wherein the plurality of data sources comprises at least one of the following: an electronic medical record (EMR) system, an electronic health record (EHR) system, a patient procedure tracking system, a hospital information system (HIS), a radiology information system (RIS), a radiology analytics system (RAS), a laboratory information system (LIS), a digital pathology system (DPS), a picture archive and communication system (PACS), or any combination thereof; process the healthcare data associated with the patient; and generate a patient health history profile based on processing the healthcare data associated with the patient. [0040] Clause 34: The computer program product of clause 25, wherein the one or more instructions that cause the at least one processor to process the healthcare data associated with the patient cause the at least one processor to: arrange the healthcare data associated with the patient in a sequence for a time period. [0041] Clause 35: The computer program product of clause 25, wherein the one or more instructions further cause the at least one processor to: collect the healthcare data associated with the patient from the plurality of data sources and store the healthcare data associated with the patient in a data structure. [0042] Clause 36: The computer program product of clause 25, wherein the patient health history profile comprises a longitudinal dataset of healthcare data for the patient over a time period. [0043] These and other features and characteristics of the present disclosure, as well as the methods of operation and functions of the related elements of structures and the combination of parts and economies of manufacture, will become more apparent upon consideration of the following description and the appended claims with reference to the accompanying drawings, all of which form a part of this specification, wherein like reference numerals designate corresponding parts in the various figures. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the present disclosure. As used in the specification and the claims, the singular form of “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise. Atty Ref. BHC219020WO BRIEF DESCRIPTION OF THE DRAWINGS [0044] Additional advantages and details of non-limiting embodiments or aspects are explained in greater detail below with reference to the exemplary embodiments that are illustrated in the accompanying schematic figures, in which: [0045] FIG.1 is a diagram of a non-limiting embodiment of an environment in which systems, devices, products, apparatus, and/or methods, described herein, may be implemented, according to the principles of the present disclosure; [0046] FIG. 2 is a diagram of a non-limiting embodiment of a system for providing machine learning analytics of healthcare information; [0047] FIG. 3 is a diagram of a non-limiting embodiment of components of one or more systems or one or more devices of FIGS.1A and 1B; [0048] FIG.4 is a flowchart of a non-limiting embodiment of a process for providing machine learning based analytics of healthcare information; [0049] FIG.5 is a flowchart of a non-limiting embodiment of a process for generating a patient profile; [0050] FIGS.6A-6C are diagrams of an implementation of a non-limiting embodiment or aspect of a process for providing machine learning based analytics of healthcare information. DETAILED DESCRIPTION [0051] For purposes of the description hereinafter, the terms “end,” “upper,” “lower,” “right,” “left,” “vertical,” “horizontal,” “top,” “bottom,” “lateral,” “longitudinal,” and derivatives thereof shall relate to the present disclosure as it is oriented in the drawing figures. However, it is to be understood that the present disclosure may assume various alternative variations and step sequences, except where expressly specified to the contrary. It is also to be understood that the specific devices and processes illustrated in the attached drawings, and described in the following specification, are simply exemplary embodiments of the present disclosure. Hence, specific dimensions and other physical characteristics related to the embodiments or aspects of the embodiments disclosed herein are not to be considered as limiting unless otherwise indicated. [0052] No aspect, component, element, structure, act, step, function, instruction, and/or the like used herein should be construed as critical or essential unless explicitly described as such. Also, as used herein, the articles “a” and “an” are intended to Atty Ref. BHC219020WO include one or more items, and may be used interchangeably with “one or more” and “at least one.” Furthermore, as used herein, the term “set” is intended to include one or more items (e.g., related items, unrelated items, a combination of related and unrelated items, etc.) and may be used interchangeably with “one or more” or “at least one.” Where only one item is intended, the term “one” or similar language is used. Also, as used herein, the terms “has,” “have,” “having,” or the like are intended to be open-ended terms. Further, the phrase “based on” is intended to mean “based at least partially on” unless explicitly stated otherwise. [0053] As used herein, the terms “communication” and “communicate” may refer to the reception, receipt, transmission, transfer, provision, and/or the like of information (e.g., data, signals, messages, instructions, commands, and/or the like). For one unit (e.g., a device, a system, a component of a device or system, combinations thereof, and/or the like) to be in communication with another unit means that the one unit is able to directly or indirectly receive information from and/or transmit information to the other unit. This may refer to a direct or indirect connection that is wired and/or wireless in nature. Additionally, two units may be in communication with each other even though the information transmitted may be modified, processed, relayed, and/or routed between the first and second unit. For example, a first unit may be in communication with a second unit even though the first unit passively receives information and does not actively transmit information to the second unit. As another example, a first unit may be in communication with a second unit if at least one intermediary unit (e.g., a third unit located between the first unit and the second unit) processes information received from the first unit and communicates the processed information to the second unit. In some non-limiting embodiments, a message may refer to a network packet (e.g., a data packet and/or the like) that includes data. It will be appreciated that numerous other arrangements are possible. [0054] As used herein, the term “system” may refer to one or more computing devices or combinations of computing devices such as, but not limited to, processors, servers, client devices, software applications, and/or other like components. In addition, reference to “a server” or “a processor,” as used herein, may refer to a previously-recited server and/or processor that is recited as performing a previous step or function, a different server and/or processor, and/or a combination of servers and/or processors. For example, as used in the specification and the claims, a first server and/or a first processor that is recited as performing a first step or function may refer Atty Ref. BHC219020WO to the same or different server and/or a processor recited as performing a second step or function. [0055] Non-limiting embodiments of the present disclosure are directed to systems, methods, and computer program products for providing machine learning based analytics of healthcare information. In some non-limiting embodiments or aspects, an Artificial Intelligence (AI) shuttle system may receive healthcare data associated with a patient from a plurality of data sources, wherein the plurality of data sources may include at least one of the following: an electronic medical record (EMR) system, an electronic health record (EHR) system, a patient procedure tracking system, a hospital information system (HIS), a radiology information system (RIS), a radiology analytics system (RAS), a laboratory information system (LIS), a digital pathology system (DPS), a picture archive and communication system (PACS), or any combination thereof, determine a medical finding for the patient using a machine learning model based on the healthcare data associated with the patient, wherein the medical finding includes at least one of a proposed diagnosis for the patient or a recommendation to perform one or more tests on the patient, and provide data associated with the medical finding for the patient to a device associated with the patient for display. [0056] In some non-limiting embodiments, the AI shuttle system may generate a patient profile based on the healthcare data associated with the patient. In some non- limiting embodiments, the AI shuttle system may provide a web-accessible link to the patient profile to allow patient information to be displayed on the device and to allow the patient information to be received from the device via the web-accessible link. In some non-limiting embodiments, when generating the patient profile, the AI shuttle system may receive demographic information associated with the patient and generate the patient profile based on the demographic information associated with the patient. [0057] In some non-limiting embodiments, when generating the patient profile, the AI shuttle system may receive protected health information associated with the patient and generate the patient profile based on the protected health information associated with the patient. In some non-limiting embodiments, when receiving the healthcare data associated with the patient, the AI shuttle system may retrieve the healthcare data associated with the patient from at least one of the data sources of the plurality of data sources based on a unique patient identifier associated with the patient. In some non-limiting embodiments, when providing the data associated with the medical Atty Ref. BHC219020WO finding for the patient, the AI shuttle system may provide the data associated with the medical finding for the patient to the device associated with the patient based on receiving a request for the medical finding that includes a unique patient identifier associated with the patient. In some non-limiting embodiments, the proposed diagnosis comprises one of a proposed differential diagnosis and a proposed final diagnosis. [0058] In this way, non-limiting embodiments of the present disclosure provide the AI shuttle system that allows for the use of a machine learning model that reduces the need for complex programming and processing resources with regard to health informatics applications for healthcare data analysis tasks. Further, the need for manually programmed processes may be reduced or eliminated along with the need for specially configured network equipment and/or reprogramming of devices in order to communicate with various medical devices and information systems based on network communication configurations. [0059] Referring now to FIG.1, FIG.1 is a diagram of a non-limiting embodiment of an environment 100 in which devices, systems, methods, and/or computer program products, described herein, may be implemented. As shown in FIG.1, environment 100 includes AI shuttle system 102, data sources 104-1 through 104-N (referred to hereafter individually as data source 104, or together as data sources 104, where appropriate), user device 106, fluid injection system 108, and communication network 112. In some non-limiting embodiments, AI shuttle system 102, data sources 104, user device 106, and/or fluid injection system 108, may interconnect (e.g., establish a connection to communicate) via wired connections, wireless connections, or a combination of wired and wireless connections. Any devices or systems in environment 100 may communicate with each other in a same or different communication network 112 as other devices or systems. [0060] In some non-limiting embodiments, AI shuttle system 102 may include one or more devices capable of being in communication with data sources 104, user device 106, and/or hospital information system 110, via communication network 112. For example, AI shuttle system 102 may include one or more computing devices, such as one or more computers, one or more servers (e.g., a cloud server, a group of servers, etc.), one or more desktop computers, one or more mobile devices (e.g., one or more tablets, one or more smartphones, etc.), and/or the like. In some non-limiting embodiments, AI shuttle system 102 may include one or more (e.g., a plurality of) Atty Ref. BHC219020WO applications (e.g., software applications) that perform a set of functionalities on an external application programming interface (API) that allows AI shuttle system 102 to send data to an external system associated with the external API and to receive data from the external system associated with the external API. In some non-limiting embodiments, the application may be supported by an application associated with fluid injection system 108 that would allow AI shuttle system 102, which may function as a control room display, to be the only one device that controls other systems and/or devices, and, in such an example, AI shuttle system 102 may provide an authentication function. In some non-limiting embodiments, AI shuttle system 102 may be a component of user device 106, fluid injection system 108, and/or hospital information system 110. [0061] In some non-limiting embodiments, data source 104 may include one or more devices capable of being in communication with AI shuttle system 102, user device 106, and fluid injection system 108 via communication network 112. For example, data source 104 may include a server, a computing device, such as a desktop computer, a mobile device (e.g., a tablet, a smartphone, a wearable, such as a wearable health sensor, an implantable device, such as a pacemaker, an internal body sensor, etc.), and/or the like. In some non-limiting embodiments, data source 104 may include an electronic medical record (EMR) system, an electronic health record (EHR) system, a patient procedure tracking system, a hospital information system (HIS), a radiology information system (RIS), a radiology analytics system (RAS), a laboratory information system (LIS), a pathology system, such as a digital pathology system (DPS), and/or an image archive and communication system, such as a picture archive and communication system (PACS). Additionally or alternatively, data source 104 may include a medical imaging system (e.g., an imaging scanner), a fluid injection system (e.g., a fluid injector), a device associated with a facility, such as a communication device associated with a medical device (e.g., a hand-held medical device, a wearable medical device, such as a portable health sensor, etc.), a fluid injection system, and/or a device associated with a patient (e.g., a user device, such as a computing device operated by a patient). Additionally or alternatively, data source 104 may include additional devices located at a location, such as a hospital, which provides medical care, such as an Internet of Things (IOT) device, and/or other devices at locations relevant to a patient (e.g., a device at a home of a patient, a device located in a vehicle of a patient, etc.). Atty Ref. BHC219020WO [0062] In some non-limiting embodiments, user device 106 may include one or more devices capable of being in communication with AI shuttle system 102, data sources 104, fluid injection system 108, and/or hospital information system 110 via communication network 112. For example, user device 106 may include a computing device, such as one or more computers, including a desktop computer, a workstation device, a laptop, tablet, and/or the like. In some non-limiting embodiments, at least a portion of the processes executed at the user device 106 may be executed at a remote server (e.g., a cloud computing server). In some non-limiting embodiments, user device 106 may provide a user interface for controlling operation of fluid injection system 108, including to generate instructions for and/or provide instructions to fluid injection system 108. Additionally or alternatively, user device 106 may display operational parameters of fluid injection system 108 during operation (e.g., during real- time operation) of fluid injection system 108. In some non-limiting embodiments, user device 106 may provide interconnectivity between fluid injection system 108 and other devices or systems, such as a scanner device (not shown). In some non-limiting embodiments, user device 106 may include the Certegra® Workstation provided by Bayer HealthCare LLC. In some non-limiting embodiments, user device 106 may include a display unit (e.g., a display device, a display screen, etc.,), such as a computer monitor, a touchscreen, a heads-up display, and/or the like, which may be used to display a user interface (e.g., a graphical user interface (GUI) of a software application), via which a user may interact with user device 106 to view parameters and/or control operation of fluid injection system 108. For example, a user of user device 106 may provide inputs to user device 106 using one or more hardware or software components of the user device 106 in connection with a touch screen, a mouse, a trackpad, a keyboard, a stylus, a gesture-sensing camera, a microphone for receiving voice commands, and/or the like. [0063] In some non-limiting embodiments, fluid injection system 108 may include one or more devices capable of being in communication with AI shuttle system 102, data sources 104, user device 106, and/or hospital information system 110 via communication network 112. For example, fluid injection system 108 may include one or more computing devices, such as one or more computers, one or more servers (e.g., a cloud server, a group of servers, etc.), one or more desktop computers, one or mobile devices (e.g., one or more tablets, one or more smartphones, etc.), and/or the like. In some non-limiting embodiments, fluid injection system 108 may include Atty Ref. BHC219020WO one or more injection devices (e.g., one or more fluid injection devices, one or more fluid injectors). In some non-limiting embodiments, fluid injection system 108 is configured to administer (e.g., inject, deliver, etc.) contrast fluid including a contrast agent to a patient, and/or administer an aqueous fluid, such as saline, to a patient before, during, and/or after administering the contrast fluid. For example, fluid injection system 108 can inject one or more prescribed dosages of contrast fluid directly into a patient’s blood stream via a hypodermic needle and syringe. In some non-limiting embodiments, fluid injection system 108 may be configured to continually administer the aqueous fluid to a patient through a peripheral intravenous line (PIV) and catheter, and one or more prescribed dosages of contrast fluid may be introduced into the PIV and administered via the catheter to the patient. In some non-limiting embodiments, fluid injection system 108 is configured to inject a dose of contrast fluid along with and/or followed by administration of a particular volume of the aqueous fluid. In some non-limiting embodiments, fluid injection system 108 may include one or more exemplary fluid injection devices that are disclosed in: U.S. Patent Application Serial No. 09/715,330, filed on November 17, 2000, issued as U.S. Patent No. 6,643,537; U.S. Patent Application Serial No. 09/982,518, filed on October 18, 2001, issued as U.S. Patent No. 7,094,216; U.S. Patent Application Serial No. 10/825,866, filed on April 16, 2004, issued as U.S. Patent No. 7,556,619; U.S. Patent Application Serial No.12/437,011, filed May 7, 2009, issued as U.S. Patent No.8,337,456; U.S. Patent Application Serial No. 12/476,513, filed June 2, 2009, issued as U.S. Patent No. 8,147,464; and U.S. Patent Application Serial No. 11/004,670, filed on December 3, 2004, issued as U.S. 8,540,698, the disclosures of each of which are incorporated herein by reference in their entireties. In some non-limiting embodiments, fluid injection system 108 may include the MEDRAD® Stellant CT Injection System, the MEDRAD® Stellant FLEX CT Injection System, the MEDRAD® MRXperion MR Injection System, the MEDRAD® Mark 7 Arterion Injection System, the MEDRAD® Intego PET Infusion System, or the MEDRAD® Centargo CT Injection System, all of which are provided by Bayer Healthcare LLC. [0064] In some non-limiting embodiments, hospital information system 110 may include one or more devices capable of being in communication with AI shuttle system 102, data sources 104, user device 106, and/or fluid injection system 108 via communication network 112. For example, hospital information system 110 may include one or more computing devices, such as one or more desktop computers, one Atty Ref. BHC219020WO or mobile devices, one or more servers, and/or the like. In some non-limiting embodiments, hospital information system 110 may include one or more subsystems, such as a patient procedure tracking system (e.g., a system that operates a modality worklist, a system that provides patient demographic information for fluid injection procedures and/or medical imaging procedures, etc.), a fluid injector management system, an image archive and communication system (e.g., a picture archive and communication system (PACS)), a radiology information system, a radiology analytics system (e.g., the Radimetrics® Enterprise Application marketed and sold by Bayer HealthCare LLC), and/or other like systems or devices. [0065] In some non-limiting embodiments, communication network 112 may include one or more wired and/or wireless networks. For example, communication network 112 may include a cellular network (e.g., a long-term evolution (LTE®) network, a third generation (3G) network, a fourth generation (4G) network, a fifth generation (5G) network, a sixth generation (6G) network, a code division multiple access (CDMA) network, etc.), a public land mobile network (PLMN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a telephone network (e.g., the public switched telephone network (PSTN)), a private network, an ad hoc network, an intranet, the Internet, a fiber optic-based network, a cloud computing network, a short range wireless communication network (e.g., a Bluetooth® network, a near field communication (NFC) network, etc.) and/or the like, and/or a combination of these or other types of networks. [0066] The number and arrangement of systems and/or devices shown in FIG.1 are provided as an example. There may be additional systems and/or devices, fewer systems and/or devices, different systems and/or devices, or differently arranged systems and/or devices than those shown in FIG. 1. Furthermore, two or more systems and/or devices shown in FIG.1 may be implemented within a single system or a single device, or a single system or a single device shown in FIG. 1 may be implemented as multiple, distributed systems or devices. Additionally or alternatively, a set of systems or a set of devices (e.g., one or more systems, one or more devices) of environment 100 may perform one or more functions described as being performed by another set of systems or another set of devices of environment 100. [0067] Referring now to FIG.2, FIG.2 is a diagram of a non-limiting embodiment of system 200 for providing machine learning based analytics of healthcare information. In some non-limiting embodiments, one or more of the functions described herein with Atty Ref. BHC219020WO respect to system 200 may be performed (e.g., completely, partially, and/or the like) by AI shuttle system 102. In some non-limiting embodiments, one or more of the functions described with respect to system 200 may be performed (e.g., completely, partially, and/or the like) by another device or a group of devices separate from and/or including AI shuttle system 102, such as workstation device 206 (e.g., which includes display unit 206A), fluid injection system 108, hospital information system 210, and/or medical imaging system 216. [0068] As shown in FIG.2, system 200 includes AI shuttle system 102, fluid injection system 108, workstation device 206, which includes display unit 206A, fluid injection system 108, hospital information system 210, electronic medical record (EMR) system 212, electronic health record (EHR) system 213, digital pathology system 214, medical imaging system 216, and laboratory information system 218. In some non-limiting embodiments, AI shuttle system 102 may interconnect (e.g., establish a connection to communicate with and/or the like) fluid injection system 108, workstation device 206, hospital information system 210, EMR system 212, EHR system 213, digital pathology system 214, medical imaging system 216, and/or laboratory information system 218 via wired connections, wireless connections, or a combination of wired and wireless connections. In some non-limiting embodiments, workstation device 206 may be the same as or similar to user device 106. In some non-limiting embodiments, hospital information system 210 may be the same as or similar to hospital information system 110. [0069] As further shown in FIG. 2, hospital information system 210 may include a plurality of subsystems. The plurality of subsystems may include patient procedure tracking system 210A, image archive and communication system 210B, radiology information system 210C, and radiology analytics system 210D. In some non-limiting embodiments, AI shuttle system 102 may receive healthcare data from hospital information system 210 via a communication network (e.g., communication network 112), according to a communications protocol for communicating the data associated with informatics. For example, AI shuttle system 102 may receive data associated with a patient procedure from hospital information system 210 (e.g., from patient procedure tracking system 210A) via the communication network according to a Digital Imaging and Communications in Medicine (DICOM) communications protocol, data associated with an operation of the fluid injection system 108 from hospital information system 210 via the communication network based on an API call (e.g., an API call Atty Ref. BHC219020WO from AI shuttle system 102), data associated with a radiology image from hospital information system 210 (e.g., from image archive and communication system 210B) via the communication network according to a DICOM communications protocol, data associated with a patient examination procedure from hospital information system 210 (e.g., from radiology information system 210C) via the communication network according to a Health Level Seven (HL7) standard communications protocol, and/or data associated with radiation dosage during a medical imaging procedure from hospital information system 210 (e.g., from radiology analytics system 210D) via the communication network based on an API call (e.g., an API call from AI shuttle system 102 to radiology analytics system 210D). [0070] In some non-limiting embodiments, EMR system 212 may include one or more devices capable of being in communication with AI shuttle system 102, workstation device 206, fluid injection system 108, hospital information system 210, EMR system 212, digital pathology system 214, medical imaging system 216, and/or laboratory information system 218 via a communication network (e.g., communication network 114). In some non-limiting embodiments, EMR system 212 may include one or more devices that receive, manage, store, and/or transmit electronic medical records that include medical record data associated with a medical record of a patient, such as demographics, medical history, medication and allergies, immunization status, laboratory test results, radiology images, vital signs, personal statistics (e.g., age, weight, height, etc.), billing information, and/or the like, associated with specific instances of medical care. [0071] In some non-limiting embodiments, EHR system 213 may include one or more devices capable of being in communication with AI shuttle system 102, workstation device 206, fluid injection system 108, hospital information system 210, EMR system 212, digital pathology system 214, medical imaging system 216, and/or laboratory information system 218 via a communication network (e.g., communication network 112). In some non-limiting embodiments, EHR system 213 may include one or more devices that receive, manage, store, and/or transmit electronic health records that include medical record data associated with a medical record of a patient, such as demographics, medical history, medication and allergies, immunization status, laboratory test results, radiology images, vital signs, personal statistics (e.g., age, weight, height, etc.), billing information, and/or the like, associated with various providers and/or locations (e.g., offices, clinics, hospitals, etc.) of medical care. Atty Ref. BHC219020WO Additionally or alternatively, EHR system 213 may include a patient portal (e.g., a web- based interface) to allow a patient to interact with a respective electronic medical record for the patient. In some non-limiting embodiments, EMR system 212 may be a data source for EHR system 213. [0072] In some non-limiting embodiments, digital pathology system 214 may include one or more devices capable of being in communication with AI shuttle system 102, workstation device 206, fluid injection system 108, hospital information system 210, EMR system 212, EHR system 213, medical imaging system 216, and/or laboratory information system 218 via a communication network (e.g., communication network 112 in FIG.1). In some non-limiting embodiments, medical imaging system 216 may include one or more devices that receive, manage, transmit, and/or interpret pathology information including data (e.g., image data, slide data, etc.) analyzed by a microscope, a scanner, and/or other like devices. [0073] In some non-limiting embodiments, medical imaging system 216 may include one or more devices capable of being in communication with AI shuttle system 102, workstation device 206, fluid injection system 108, hospital information system 210, EMR system 212, digital pathology system 214, and/or laboratory information system 218 via a communication network (e.g., communication network 112). In some non- limiting embodiments, medical imaging system 216 may include one or more scanners, such as a computed tomography (CT) scanner and/or a magnetic resonance imaging (MRI) scanner, capable of communicating via a communication network and capable of performing medical imaging procedures involving the use of a radiological contrast material. [0074] In some non-limiting embodiments, laboratory information system 218 may include one or more devices capable of being in communication with AI shuttle system 102, fluid injection system 108, workstation device 206, hospital information system 210, EMR system 212, digital pathology system 214, and/or medical imaging system 216 via a communication network (e.g., communication network 112). In some non- limiting embodiments, medical imaging system 216 may include one or more devices that record, manage, update, and/or store patient data and/or testing data for clinical and/or anatomic pathology laboratories, including receiving test orders, transmitting orders to laboratory analyzers, tracking orders, results, and/or quality control information, and/or transmitting results to other systems or devices. Atty Ref. BHC219020WO [0075] In some non-limiting embodiments, AI shuttle system 102 may include a plurality of applications, and each of the plurality of applications may be associated with an API associated with a respective application (e.g., a first API associated with a first application, a second API associated with a second application, a third API associated with a third application, etc.) that allows other systems and/or devices to interface (e.g., communicate, establish a communication interface, etc.) with AI shuttle system 102 and/or that allows AI shuttle system 102 to interface with other systems and/or devices (e.g., individual subsystems of hospital information system 210, such as patient procedure tracking system 210A, image archive and communication system 210B, radiology information system 210C, and/or radiology analytics system 210D). [0076] In some non-limiting embodiments, AI shuttle system 102 may provide a user interface (e.g., via an application that includes a user interface, such as a web-based user interface) that allows a user to access information such as a medical finding (e.g., a medical finding for the patient). [0077] As further shown in FIG.2, workstation device 206 may include display unit 206A. In some non-limiting embodiments, display unit 206A may be capable of displaying the user interface (e.g., the web-based user interface) provided by AI shuttle system 102. In some non-limiting embodiments, display unit 206A may include a computing device, such as a smart display unit, a portable computer, such as a tablet, a laptop, and/or the like. In some non-limiting embodiments, display unit 206A may include a touchscreen for receiving inputs by a user. In some non-limiting embodiments, display unit 206A may include a display device (e.g., a monitor, a screen, and/or the like for displaying visual information). [0078] In some non-limiting embodiments, AI shuttle system 102 may transmit data associated with an image received from medical imaging system 216 to fluid injection system 108 via a communication network. For example, AI shuttle system 102 may transmit data associated with the image received from medical imaging system 216 to fluid injection system 108 via the communication network based on an API call from fluid injection system 108. In some non-limiting embodiments, AI shuttle system 102 may transmit data associated with a fluid injection procedure (e.g., data associated with a volume, a flow rate and/or an amount of time for injecting a radiological contrast material into a patient) received from fluid injection system 108 to medical imaging system 216 via the communication network. For example, AI shuttle system 102 may transmit the data associated with the fluid injection procedure received from fluid Atty Ref. BHC219020WO injection system 108 to medical imaging system 216 via the communication network based on an API call (e.g., an API call for an imaging system interface (ISI), an API call for an ISI2 interface, an API call for a Connect CT interface, etc.) from medical imaging system 216. In some non-limiting embodiments, medical imaging system 216 may perform a medical imaging procedure on a patient based on the data, inclusive of an injection protocol, associated with the fluid injection procedure. In some non- limiting embodiments, AI shuttle system 102 may receive data associated with an operation of medical imaging system 216 from medical imaging system 216 via the communication network based on an API call (e.g., an API call from AI shuttle system 102 to medical imaging system 216). [0079] In some non-limiting embodiments, AI shuttle system 102 may provide a communication interface between hospital information system 210 and fluid injection system 108 such that fluid injection system 108 is able to receive data based on an API call from fluid injection system 108 to AI shuttle system 102. In some non-limiting embodiments, AI shuttle system 102 may transmit data associated with informatics received from hospital information system 210 to fluid injection system 108 via a communication network (e.g., communication network 112). For example, AI shuttle system 102 may transmit data associated with informatics received from hospital information system 210 to fluid injection system 108 via the communication network based on an API call from fluid injection system 108. [0080] Referring now to FIG.3, FIG.3 is a diagram of example components of device 300. Device 300 may correspond to one or more devices of AI shuttle system 102, data source 104, user device 106, workstation device 206, one or more devices of fluid injection system 108, one or more devices of hospital information system 110, one or more devices of hospital information system 210, one or more devices of EMR system 212, one or more devices of EHR system 213, one or more devices of digital pathology system 214, one or more devices of medical imaging system 216, and/or one or more devices of laboratory information system 218. In some non-limiting embodiments, AI shuttle system 102, data source 104, user device 106, workstation device 206, fluid injection system 108, hospital information system 110, hospital information system 210, EMR system 212, EHR system 213, digital pathology system 214, medical imaging system 216, and/or laboratory information system 218 may include at least one device 300 and/or at least one component of device 300. [0081] As shown in FIG.3, device 300 may include bus 302, processor 304, memory Atty Ref. BHC219020WO 306, storage component 308, input component 310, output component 312, and communication interface 314. Bus 302 may include a component that permits communication among the components of device 300. In some non-limiting embodiments, processor 304 may be implemented in hardware, firmware, or a combination of hardware and software. For example, processor 304 may include a processor (e.g., a central processing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), etc.), a microprocessor, a digital signal processor (DSP), and/or any processing component (e.g., a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), etc.) that can be programmed to perform a function. Memory 306 may include random access memory (RAM), read only memory (ROM), and/or another type of dynamic or static storage device (e.g., flash memory, magnetic memory, optical memory, etc.) that stores information and/or instructions for use by processor 304. [0082] Storage component 308 may store information and/or software related to the operation and use of device 300. For example, storage component 308 may include a hard disk (e.g., a magnetic disk, an optical disk, a magneto-optic disk, a solid state disk, etc.), a compact disc (CD), a digital versatile disc (DVD), a floppy disk, a cartridge, a magnetic tape, and/or another type of computer-readable medium, along with a corresponding drive. [0083] Input component 310 may include a component that permits device 300 to receive information, such as via user input (e.g., a touch screen display, a keyboard, a keypad, a mouse, a button, a switch, a microphone, etc.). Additionally, or alternatively, input component 310 may include a sensor for sensing information (e.g., a global positioning system (GPS) component, an accelerometer, a gyroscope, an actuator, etc.). Output component 312 may include a component that provides output information from device 300 (e.g., a display, a speaker, one or more light-emitting diodes (LEDs), etc.). [0084] Communication interface 314 may include a transceiver-like component (e.g., a transceiver, a separate receiver and transmitter, etc.) that enables device 300 to communicate with other devices, such as via a wired connection, a wireless connection, or a combination of wired and wireless connections. Communication interface 314 may permit device 300 to receive information from another device and/or provide information to another device. For example, communication interface 314 may include an Ethernet interface, an optical interface, a coaxial interface, an infrared Atty Ref. BHC219020WO interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, a Wi-Fi® interface, a cellular network interface, and/or the like. [0085] Device 300 may perform one or more processes described herein. Device 300 may perform these processes based on processor 304 executing software instructions stored by a computer-readable medium, such as memory 306 and/or storage component 308. A computer-readable medium (e.g., a non-transitory computer-readable medium) is defined herein as a non-transitory memory device. A memory device may include memory space located inside of a single physical storage device or memory space spread across multiple physical storage devices. [0086] Software instructions may be read into memory 306 and/or storage component 308 from another computer-readable medium or from another device via communication interface 314. When executed, software instructions stored in memory 306 and/or storage component 308 may cause processor 304 to perform one or more processes described herein. Additionally, or alternatively, hardwired circuitry may be used in place of or in combination with software instructions to perform one or more processes described herein. Thus, embodiments described herein are not limited to any specific combination of hardware circuitry and software. [0087] The number and arrangement of components shown in FIG.3 are provided as an example. In some non-limiting embodiments, device 300 may include additional components, fewer components, different components, or differently arranged components than those shown in FIG. 3. Additionally, or alternatively, a set of components (e.g., one or more components) of device 300 may perform one or more functions described as being performed by another set of components of device 300. [0088] Referring now to FIG.4, FIG.4 is a flowchart of a non-limiting embodiment of a process 400 for providing machine learning analytics of healthcare information. In some non-limiting embodiments, one or more of the steps of process 400 are performed (e.g., completely, partially, etc.) by AI shuttle system 102. In some non- limiting embodiments, one or more of the steps of process 400 are performed (e.g., completely, partially, etc.) by another device or a group of devices separate from or including AI shuttle system 102, such as a data source (e.g., data source 104), a user device (e.g., user device 106, workstation device 206), a fluid injection system (e.g., fluid injection system 108, such as one or more devices of fluid injection system 108), and/or a hospital information system (e.g., hospital information system 110, such as one or more devices of hospital information system 110, hospital information system Atty Ref. BHC219020WO 210, such as one or more subsystems of hospital information system 210, etc.). [0089] As shown in FIG. 4, at step 402, process 400 may include receiving healthcare data associated with a patient from a plurality of data sources. For example, AI shuttle system 102 may receive healthcare data associated with a patient from data sources 104. In some non-limiting embodiments, data sources 104 may include at least one of the following: an electronic medical record (EMR) system (e.g., EMR system 212), a patient procedure tracking system (e.g., patient procedure tracking system 210A), a hospital information system (HIS) (e.g., hospital information system 210), a radiology information system (RIS) (e.g., radiology information system 210C), a radiology analytics system (RAS) (e.g., radiology analytics system 210D), a laboratory information system (LIS) (e.g., laboratory information system 218), a digital pathology system (DPS) (e.g., digital pathology system 214), a picture archive and communication system (PACS) (e.g., image archive and communication system 210B), or any combination thereof. [0090] In some non-limiting embodiments, the healthcare data associated with a patient may include medical record data associated with a medical record of a patient, protected health information associated with the patient, demographic information associated with the patient, identification data associated with an identifier of a patient, data associated with a patient examination procedure (e.g., a fluid injection procedure and/or a medical imaging procedure performed on a patient), such as data associated with a contrast fluid provided during a fluid injection procedure, a gauge of a catheter used during a fluid injection procedure, and a fluid injection protocol for a fluid injection procedure. In some non-limiting embodiments, AI shuttle system 102 may store the healthcare data associated with the patient in a data structure (e.g., a database). For example, AI shuttle system 102 may store the healthcare data associated with the patient in the data structure with a unique identifier for the patient. [0091] In some non-limiting embodiments, AI shuttle system 102 may retrieve (e.g., based on a polling software operation and/or a pulling software operation) healthcare data associated with the patient from at least one of the data sources based on an identifier associated with the patient. For example, AI shuttle system 102 may transmit an identifier (e.g., a unique patient identifier, such as a unique patient identifier for a patient medical record) associated with a patient (e.g., a patient undergoing an examination procedure) to data source 104, such as a hospital information system, and AI shuttle system 102 may receive the healthcare data associated with the patient Atty Ref. BHC219020WO from that data source 104. In some non-limiting embodiments, data source 104 may receive the identifier associated with the patient, retrieve healthcare data associated with the patient based on the identifier, and transmit the healthcare data associated with the patient to AI shuttle system 102. In some non-limiting embodiments, the identifier may be associated with a patient record of a patient (e.g., a patient record of a patient stored in patient procedure tracking system 210A of hospital information system 210). [0092] As shown in FIG.4, at step 404, process 400 may include generating a patient profile based on the healthcare data associated with the patient. For example, AI shuttle system 102 may generate a patient profile (e.g., a patient health history profile) based on the healthcare data associated with the patient. In some non-limiting embodiments, AI shuttle system 102 may generate the patient profile based on demographic information associated with the patient and/or protected health information associated with the patient. [0093] As shown in FIG. 4, at step 406, process 400 may include determining a medical finding for the patient using a machine learning model. For example, AI shuttle system 102 may determine a medical finding for the patient using a machine learning model based on the healthcare data associated with the patient. In such an example, AI shuttle system 102 may provide the healthcare data associated with the patient as an input to the machine learning model and the machine learning model may provide a medical finding for the patient as an output. In some non-limiting embodiments, the medical finding may include a proposed diagnosis for the patient and/or a recommendation to perform one or more tests (e.g., examination procedures, such as an imaging procedure) on the patient. In some non-limiting embodiments, the proposed diagnosis may include a proposed differential diagnosis and/or a proposed final diagnosis. [0094] In some non-limiting embodiments, the machine learning model may be configured to receive, as an input, healthcare data associated with the patient and the machine learning model may be configured to provide, as an output, a prediction of a proposed diagnosis for the patient and/or a prediction of a recommendation to perform one or more tests on the patient. [0095] In some non-limiting embodiments, AI shuttle system 102 may store the medical finding for the patient with a patient profile for the patient. For example, AI Atty Ref. BHC219020WO shuttle system 102 may store the medical finding for the patient with the patient profile for the patient based on determining the medical finding for the patient. [0096] As shown in FIG. 4, at step 408, process 400 may include providing data associated with the medical finding for the patient. For example, AI shuttle system 102 may provide data associated with the medical finding for the patient to a device associated with the patient for display. In some non-limiting embodiments, AI shuttle system 102 may provide data associated with the medical finding for the patient to a user device (e.g., user device 106, workstation device 206, etc.), a fluid injection system (e.g., fluid injection system 108), a hospital information system (e.g., hospital information system 110, hospital information system 210 and/or the like) and/or other systems and devices. In some non-limiting embodiments, the user device may be associated with the patient. In some non-limiting embodiments, the user device may be associated with a physician or caregiver providing services to the patient. [0097] In some non-limiting embodiments, AI shuttle system 102 may provide the data associated with the medical finding for the patient to a device associated with the patient based on receiving a request for the medical finding. For example, AI shuttle system 102 may receive a request for the medical finding that includes a unique patient identifier associated with the patient. AI shuttle system 102 may retrieve the medical finding from a data structure based on the unique patient identifier associated with the patient and AI shuttle system 102 may transmit the medical finding to a system or device that provided the request for the medical finding. [0098] In some non-limiting embodiments, AI shuttle system 102 may provide a web- accessible link to a patient profile to allow patient information, such as a medical finding for a patient, to be displayed on a device (e.g., user device 106, a display unit 206A of workstation device 206, etc.) and/or to allow patient information to be received from the device via the web-accessible link. In some non-limiting embodiments, AI shuttle system 102 may provide a user interface with the web-accessible link and AI shuttle system 102 may receive a selection of an identifier of a patient via a user interface. In some non-limiting embodiments, AI shuttle system 102 may receive the selection of the identifier based on user input. For example, a user may select the identifier (e.g., a patient identifier that includes a unique identifier) associated with a patient profile by selecting (e.g., touching, clicking, pressing, etc.) the identifier in a user interface. Atty Ref. BHC219020WO [0099] Referring now to FIG.5, FIG.5 is a flowchart of a non-limiting embodiment of a process 500 for generating a patient profile for a patient. In some non-limiting embodiments, one or more of the steps of process 500 are performed (e.g., completely, partially, etc.) by AI shuttle system 102. In some non-limiting embodiments, one or more of the steps of process 500 are performed (e.g., completely, partially, etc.) by another device or a group of devices separate from or including AI shuttle system 102, such as a data source (e.g., data source 104), a user device (e.g., user device 106, workstation device 206), a fluid injection system (e.g., fluid injection system 108, such as one or more devices of fluid injection system 108), and/or a hospital information system (e.g., hospital information system 110, such as one or more devices of hospital information system 110, hospital information system 210, such as one or more subsystems of hospital information system 210, etc.). [0100] As shown in FIG. 5, at step 502, process 500 may include receiving healthcare data associated with a patient from a plurality of data sources. For example, AI shuttle system 102 may receive the data associated with a patient from data sources 104 in the same or similar fashion as described above in step 402 of process 400. In some non-limiting embodiments, AI shuttle system 102 may receive the healthcare data associated with the patient from first data source 104 of data sources 104 asynchronously as compared to (e.g., not at the same time as) second data source 104. In some non-limiting embodiments, AI shuttle system 102 may receive the healthcare data associated with the patient from all of data sources 104 synchronously. [0101] As shown in FIG. 5, at step 504, process 500 may include processing the healthcare data associated with the patient. For example, AI shuttle system 102 may process the healthcare data associated with the patient based on receiving the healthcare data. In some non-limiting embodiments, the healthcare data associated with the patient may include healthcare data associated with each individual event of a plurality of individual events regarding medical care over a time period for the patient. Each individual event of the plurality of individual events may involve a specific instance of medical care for the patient during a time period (e.g., a quarterly time period, an annual time period, a specified time period, such as a time period of hours, days, weeks, months, years, etc.). [0102] In some non-limiting embodiments, when processing the healthcare data associated with the patient, AI shuttle system 102 may determine that healthcare data Atty Ref. BHC219020WO received from data sources 104 is associated with a correct patient profile and/or that the healthcare data is complete for a time period. For example, AI shuttle system 102 may compare a unique patient identifier of a patient to a patient identifier included in the healthcare data associated with a patient. If AI shuttle system 102 determines that the unique patient identifier of the patient corresponds to a patient identifier included in the healthcare data, AI shuttle system 102 may determine that the healthcare data is associated with a correct patient profile. If AI shuttle system 102 determines that the unique patient identifier of the patient does not correspond to a patient identifier included in the healthcare data, AI shuttle system 102 may determine that the healthcare data is not associated with the correct patient profile. [0103] For example, AI shuttle system 102 may determine whether healthcare data for a patient is complete for a time period based on a unique patient identifier of the patient. For example, AI shuttle system 102 may compare a unique patient identifier of a patient to a patient identifier included in the healthcare data associated with the patient (e.g., a plurality of records included in the healthcare data associated with a patient) and determine if the healthcare data is complete for the time period. If AI shuttle system 102 determines that the unique patient identifier of the patient corresponds to all of the healthcare data (e.g., all of the plurality of records) associated with the patient for the time period, AI shuttle system 102 may determine that healthcare data for the patient is complete for the time period. If AI shuttle system 102 determines that the unique patient identifier of the patient does not correspond to all of the healthcare data associated with the patient for the time period or that healthcare data associated with the patient is missing for a time interval of the time period, AI shuttle system 102 may determine that healthcare data for the patient is not complete for the time period. [0104] In some non-limiting embodiments, when processing the healthcare data associated with the patient, AI shuttle system 102 may collect healthcare data from all data sources 104 for each patient of a plurality of patients and store the healthcare data associated with each patient of the plurality of patients individually in a data structure (e.g., a database). In some non-limiting embodiments, when processing the healthcare data associated with the patient, AI shuttle system 102 may detect whether an error is present in the healthcare data in each data source 104 relative to healthcare data in one or more of other data sources 104. In some non-limiting embodiments, AI shuttle system 102 may correct an error that is detected based on detecting the error. Atty Ref. BHC219020WO Additionally or alternatively, AI shuttle system 102 may provide a notification (e.g., a notification message to an operator) with regard to an error that is detected. In some non-limiting embodiments, AI shuttle system 102 may provide a prompt (e.g., a prompt in a user interface) to accept or decline a proposed correction of the error. [0105] In some non-limiting embodiments, when processing the healthcare data associated with the patient, AI shuttle system 102 may arrange all of the healthcare data for a patient in a sequence (e.g., for a patient profile). For example, AI shuttle system 102 may receive healthcare data from all data sources 104 for the patient based on the unique patient identifier of the patient and AI shuttle system 102 may arrange all of the healthcare data for a patient in a time-based sequence to provide a patient health history profile. In some non-limiting embodiments, when processing the healthcare data associated with the patient, AI shuttle system 102 may arrange the healthcare data for the patient into sub-sequences based on a characteristic of medical care associated with the healthcare data for the patient. For example, AI shuttle system 102 may arrange the healthcare data for the patient into sub-sequences based on a type of treatment, a type of medicine and/or type of medical equipment used, a location of a medical issue on the body of the patient, and/or the like, with regard to medical care associated with the healthcare data for the patient. In some non-limiting embodiments, AI shuttle system 102 may arrange all of the healthcare data for a patient in a sequence for a time period based on determining that the healthcare data for the patient is complete for the time period. [0106] As shown in FIG.5, at step 506, process 500 may include generating a patient profile. For example, AI shuttle system 102 may generate the patient profile based on processing the healthcare data associated with the patient. In some non-limiting embodiments, AI shuttle system 102 may generate the patient profile as a patient health history profile for a patient that includes a longitudinal dataset of healthcare data (e.g., from all data sources 104) and/or a cross-sectional dataset of the healthcare data. In some non-limiting embodiments, the longitudinal dataset of healthcare data may include healthcare data collected from data sources 104 (e.g., data sources 104 of multiple different systems, such as different hospital systems) over a time period (e.g., a specified time period, a predetermined time period, an automatically selected time period, etc.) for a plurality of instances of medical care for the patient. In some non-limiting embodiments, the cross-sectional dataset of healthcare data may include healthcare data collected from one or more specified data Atty Ref. BHC219020WO sources 104 of data sources 104 a time instance (e.g., a specified time instance, a predetermined time instance, an automatically selected time instance, etc.) for a given instance of medical care for the patient. In some non-limiting embodiments, the cross- sectional dataset of healthcare data may be a subset of the longitudinal dataset of healthcare data. [0107] Referring now to FIGS. 6A–6C, FIGS 6A-6C are diagrams of a non-limiting embodiment or aspect of implementation 600 relating to a process (e.g., process 600) for providing machine learning analytics of healthcare information. In some non- limiting embodiments or aspects, one or more of the steps of the process may be performed (e.g., completely, partially, etc.) by AI shuttle system 102 (e.g., by one or more devices of AI shuttle system 102). In some non-limiting embodiments or aspects, one or more of the steps of the process may be performed (e.g., completely, partially, etc.) by another device or a group of devices separate from or including AI shuttle system 102, such as a data source (e.g., data source 104), a user device (e.g., user device 106, workstation device 206), a fluid injection system (e.g., fluid injection system 108, such as one or more devices of fluid injection system 108), and/or a hospital information system (e.g., hospital information system 110, such as one or more devices of hospital information system 110, hospital information system 210, such as one or more subsystems of hospital information system 210, etc.). [0108] As shown by reference number 605 in FIG. 6A, AI shuttle system 102 may receive healthcare data associated with a patient from one or more data sources 104 via communication network 112. As shown by reference number 610 in FIG. 6B, AI shuttle system 102 may generate a patient profile for the patient. For example, AI shuttle system 102 may generate the patient profile for the patient based on receiving the healthcare data associated with the patient. As further shown by reference number 615 in FIG.6B, AI shuttle system 102 may determine a medical finding for the patient using a machine learning model based on the healthcare data associated with the patient. As shown by reference numbers 620 and 625 in FIG.6C, AI shuttle system 102 may provide data associated with the medical finding for the patient and provide a web-accessible link to the patient profile to a user device (e.g., user device 106, workstation 206 and/or the like) via communication network 112. [0109] Although the above systems, methods, and computer program products have been described in detail for the purpose of illustration based on what is currently considered to be the most practical and preferred embodiments, it is to be understood Atty Ref. BHC219020WO that such detail is solely for that purpose and that the present disclosure is not limited to the described embodiments or aspects but, on the contrary, is intended to cover modifications and equivalent arrangements that are within the spirit and scope of the appended claims. For example, it is to be understood that the present disclosure contemplates that, to the extent possible, at least one feature of any embodiment or aspect can be combined with at least one feature of any other embodiment.

Claims

Atty Ref. BHC219020WO WHAT IS CLAIMED IS: 1. A system for providing machine learning analytics of healthcare information, comprising: at least one processor programmed or configured to: receive healthcare data associated with a patient from a plurality of data sources, wherein the plurality of data sources comprises at least one of the following: an electronic medical record (EMR) system, an electronic health record (EHR) system, a patient procedure tracking system, a hospital information system (HIS), a radiology information system (RIS), a radiology analytics system (RAS), a laboratory information system (LIS), a digital pathology system (DPS), a picture archive and communication system (PACS), or any combination thereof; generate a patient health history profile based on the healthcare data associated with the patient; determine a medical finding for the patient based on the patient health history profile using a machine learning model, wherein the medical finding includes at least one of a proposed diagnosis for the patient or a recommendation to perform one or more tests on the patient; and provide data associated with the medical finding for the patient to a device associated with the patient for display. 2. The system of claim 1, wherein, when generating the patient health history profile based on the healthcare data associated with the patient, the at least one processor is programmed or configured to: receive the healthcare data associated with the patient; determine a longitudinal healthcare dataset for the patient; and generate the patient health history profile based on the longitudinal healthcare dataset. Atty Ref. BHC219020WO 3. The system of claim 2, wherein the at least one processor is further programmed or configured to: provide a web-accessible link to the patient health history profile to allow patient information to be displayed on the device and to allow the patient information to be received from the device via the web-accessible link. 4. The system of claims 1-3, wherein, when generating the patient health history profile, the at least one processor is programmed or configured to: receive demographic information associated with the patient; and generate the patient health history profile based on the demographic information associated with the patient. 5. The system of any of claims 1-4, wherein, when generating the patient health history profile , the at least one processor is programmed or configured to: receive protected health information associated with the patient; and generate the patient health history profile based on the protected health information associated with the patient. 6. The system of any of claims 1-5, wherein, when receiving the healthcare data associated with the patient, the at least one processor is programmed or configured to: retrieve the healthcare data associated with the patient from at least one of the data sources of the plurality of data sources based on a unique patient identifier associated with the patient. 7. The system of any of claims 1-6, wherein, when providing the data associated with the medical finding for the patient, the at least one processor is programmed or configured to: provide the data associated with the medical finding for the patient to the device associated with the patient based on receiving a request for the medical finding that includes a unique patient identifier associated with the patient. Atty Ref. BHC219020WO 8. The system of any of claims 1-7, wherein the proposed diagnosis comprises one of a proposed differential diagnosis and a proposed final diagnosis. 9. A method for providing machine learning analytics of healthcare information, comprising: receiving healthcare data associated with a patient from a plurality of data sources, wherein the plurality of data sources comprises at least one of the following: an electronic medical record (EMR) system, an electronic health record (EHR) system, a patient procedure tracking system, a hospital information system (HIS), a radiology information system (RIS), a radiology analytics system (RAS), a laboratory information system (LIS), a digital pathology system (DPS), a picture archive and communication system (PACS), or any combination thereof; generating a patient health history profile based on the healthcare data associated with the patient; determining a medical finding for the patient based on the patient health history profile using a machine learning model, wherein the medical finding includes at least one of a proposed diagnosis for the patient or a recommendation to perform one or more tests on the patient; and providing data associated with the medical finding for the patient to a device associated with the patient for display. 10. The method of claim 9, wherein generating a patient health history profile based on the healthcare data associated with the patient comprises: receiving the healthcare data associated with the patient; determining a longitudinal healthcare dataset for the patient; and generating the patient health history profile based on the longitudinal healthcare dataset. Atty Ref. BHC219020WO 11. The method of claim 10, further comprising: providing a web-accessible link to the patient health history profile to allow patient information to be displayed on the device and to allow the patient information to be received from the device via the web-accessible link. 12. The method of claims 10 or 11, wherein generating the patient health history profile comprises: receiving demographic information associated with the patient; and generating the patient health history profile based on the demographic information associated with the patient. 13. The method of any of claims 10-12, wherein generating the patient health history profile comprises: receiving protected health information associated with the patient; and generating the patient health history profile based on the protected health information associated with the patient. 14. The method of any of claims 9-13, wherein receiving the healthcare data associated with the patient comprises: retrieve the healthcare data associated with the patient from at least one of the data sources of the plurality of data sources based on a unique patient identifier associated with the patient. 15. The method of any of claims 9-14, wherein providing the data associated with the medical finding for the patient comprises: providing the data associated with the medical finding for the patient to the device associated with the patient based on receiving a request for the medical finding that includes a unique patient identifier associated with the patient. 16. The method of any of claims 9-15, wherein the proposed diagnosis comprises one of a proposed differential diagnosis and a proposed final diagnosis. Atty Ref. BHC219020WO 17. A computer program product for providing machine learning analytics of healthcare information, the computer program product comprising at least one non-transitory computer-readable medium including one or more instructions that, when executed by at least one processor, cause the at least one processor to: receive healthcare data associated with a patient from a plurality of data sources, wherein the plurality of data sources comprises at least one of the following: an electronic medical record (EMR) system, an electronic health record (EHR) system, a patient procedure tracking system, a hospital information system (HIS), a radiology information system (RIS), a radiology analytics system (RAS), a laboratory information system (LIS), a digital pathology system (DPS), a picture archive and communication system (PACS), or any combination thereof; generate a patient health history profile based on the healthcare data associated with the patient; determine a medical finding for the patient based on the patient health history profile using a machine learning model, wherein the medical finding includes at least one of a proposed diagnosis for the patient or a recommendation to perform one or more tests on the patient; and provide data associated with the medical finding for the patient to a device associated with the patient for display. 18. The computer program product of claim 17, wherein the one or more instructions that cause the at least one processor to generate a patient health history profile based on the healthcare data associated with the patient cause the at least one processor to: receive the healthcare data associated with the patient; determine a longitudinal healthcare dataset for the patient; and generate the patient health history profile based on the longitudinal healthcare dataset. Atty Ref. BHC219020WO 19. The computer program product of claim 18, wherein the one or more instructions further cause the at least one processor to: provide a web-accessible link to the patient health history profile to allow patient information to be displayed on the device and to allow the patient information to be received from the device via the web-accessible link. 20. The computer program product of claims 18 or 19, wherein, the one or more instructions that cause the at least one processor to generate the patient health history profile cause the at least one processor to: receive demographic information associated with the patient; and generate the patient health history profile based on the demographic information associated with the patient. 21. The computer program product of any of claims 18-20, wherein, the one or more instructions that cause the at least one processor to generate the patient health history profile cause the at least one processor to: receive protected health information associated with the patient; and generate the patient health history profile based on the protected health information associated with the patient. 22. The computer program product of any of claims 17-21, wherein, the one or more instructions that cause the at least one processor to receive the healthcare data associated with the patient cause the at least one processor to: retrieve the healthcare data associated with the patient from at least one of the data sources of the plurality of data sources based on a unique patient identifier associated with the patient. 23. The computer program product of any of claims 17-22, wherein, the one or more instructions that cause the at least one processor to provide the data associated with the medical finding for the patient cause the at least one processor to: provide the data associated with the medical finding for the patient to the device associated with the patient based on receiving a request for the medical finding that includes a unique patient identifier associated with the patient. Atty Ref. BHC219020WO 24. The computer program product of any of claims 17-23, wherein the proposed diagnosis comprises one of a proposed differential diagnosis and a proposed final diagnosis.
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