System, method, and computer program product for providing machine learning-based healthcare information analysis

Abstract

A system for providing machine learning analysis of healthcare information may include at least one processor to: receive healthcare data related to a patient from a plurality of data sources, the plurality of data sources comprise at least one of an electronic medical record system, a patient program tracking system, a hospital information system, a radiation information system, a radiation analysis system, a laboratory information system, a digital pathology system, a picture archiving and communication system, or any combination of the electronic medical record system, the patient program tracking system, the hospital information system, the radiation information system, the radiation analysis system, the laboratory information system and the picture archiving and communication system; determining a medical discovery of the patient using a machine learning model based on the healthcare data related to the patient, where the medical discovery includes at least one of a formulated diagnosis for the patient or a suggestion to perform one or more tests on the patient; and providing the data relating to the medical discovery of the patient to a device associated with the patient for display. Methods and computer program products are also disclosed.

Description

Technical Field

[0001] This disclosure generally relates to systems and / or devices for providing healthcare information, and in some non-limiting embodiments, to systems, methods, and computer program products for providing machine learning-based healthcare information analysis. Background Technology

[0002] Health informatics may refer to a scientific and engineering field that develops methods and technologies for acquiring, processing, and / or studying patient-related healthcare information (e.g., medical data, patient data, etc.). In some cases, 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, as well as the analysis of such medical data using computational techniques.

[0003] Artificial intelligence (AI) can be used in healthcare as a way to mimic human cognition in the analysis, presentation, and / or understanding of healthcare data. AI can describe the ability of computer programs (such as computer algorithms) to approximate conclusions based on input data, which may be healthcare data. In some cases, computer algorithms can be used to identify patterns in data and create logic to recognize those patterns. Such computer algorithms can include machine learning models that are trained to perform certain tasks using large amounts of input data.

[0004] However, health informatics applications can be implemented in environments requiring complex programming and processing resources to receive input data in the form of patient-related healthcare information for data analysis tasks. Therefore, the manual programming process developed to provide input data to health informatics applications may require significant network resources, may involve manual updates, and may be inaccurate. Furthermore, devices running health informatics applications may require specially configured network equipment and / or reprogramming to communicate with various medical devices and information systems based on network communication configurations. Summary of the Invention

[0005] Therefore, systems, methods, and computer program products are provided for providing machine learning-based healthcare information analysis.

[0006] Further non-limiting embodiments or aspects are set forth in the following numbered clauses:

[0007] Clause 1: A system for providing machine learning analysis of healthcare information, comprising: at least one processor programmed or configured to: receive patient-related healthcare data from multiple data sources, wherein the multiple data sources include at least one of: 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 analysis system (RAS), a laboratory information system (LIS), a digital pathology system (DPS), a picture archiving and communication system (PACS), or any combination thereof; generate a patient health history profile based on the patient-related healthcare data; determine medical findings about the patient based on the patient health history profile using a machine learning model, wherein the medical findings include 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 related to the patient's medical findings to a patient-related device for display.

[0008] Clause 2: The system according to Clause 1, wherein, when generating the patient health history profile based on healthcare data associated with the patient, the at least one processor is programmed or configured to: receive healthcare data associated with the patient; determine a longitudinal healthcare dataset of the patient; and generate the patient health history profile based on the longitudinal healthcare dataset.

[0009] Clause 3: The system according to Clause 2, wherein the at least one processor is further programmed or configured to: provide a web-accessible link to the patient's health history file to allow display of patient information on the device and to allow receiving the patient information from the device via the web-accessible link.

[0010] Clause 4: The system according to Clauses 1-3, wherein, when generating a patient health history file, the at least one processor is programmed or configured to: receive demographic information related to the patient; and generate the patient health history file based on the demographic information related to the patient.

[0011] Clause 5: The system according to any one of Clauses 1-4, wherein, when generating the patient health history file, the at least one processor is programmed or configured to: receive protected health information related to the patient; and generate the patient health history file based on the protected health information related to the patient.

[0012] Clause 6: A system according to any one of Clauses 1-5, wherein, upon 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 plurality of data sources based on a unique patient identifier associated with the patient.

[0013] Clause 7: A system according to any one of Clauses 1-6, wherein, when providing data related to the medical findings of the patient, the at least one processor is programmed or configured to: based on receiving a request for medical findings including a unique patient identifier associated with the patient, provide the data related to the medical findings of the patient to the device associated with the patient.

[0014] Clause 8: A system according to any one of Clauses 1-7, wherein the proposed diagnosis includes one of the proposed differential diagnosis and the proposed final diagnosis.

[0015] Clause 9: A method for providing machine learning analysis of healthcare information, comprising: receiving patient-related healthcare data from multiple data sources, wherein the multiple data sources 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 analysis system (RAS), a laboratory information system (LIS), a digital pathology system (DPS), a picture archiving and communication system (PACS), or any combination thereof; generating a patient health history profile based on the patient-related healthcare data; using a machine learning model to determine medical findings about the patient based on the patient health history profile, wherein the medical findings include 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 related to the patient's medical findings to a patient-related device for display.

[0016] Clause 10: The method according to Clause 9, wherein generating the patient health history profile based on healthcare data related to the patient includes: receiving healthcare data related to 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 according to Clause 10 further includes: providing a web-accessible link to the patient's health history file to allow display of patient information on the device and to allow receiving the patient information from the device via the web-accessible link.

[0018] Clause 12: The method according to Clause 10 or 11, wherein generating the patient health history file includes: receiving demographic information related to the patient; and generating the patient health history file based on the demographic information related to the patient.

[0019] Clause 13: The method according to any one of Clauses 10-12, wherein generating the patient health history file comprises: receiving protected health information related to the patient; and generating the patient health history file based on the protected health information related to the patient.

[0020] Clause 14: The method according to any one of Clauses 9 to 13, wherein receiving the healthcare data associated with the patient comprises: retrieving the healthcare data associated with the patient from at least one of the plurality of data sources based on a unique patient identifier associated with the patient.

[0021] Clause 15: The method according to any one of Clauses 9 to 14, wherein providing data related to the medical findings of the patient comprises: providing the data related to the medical findings of the patient to the device associated with the patient based on receiving a request for medical findings including a unique patient identifier associated with the patient.

[0022] Clause 16: The method according to any one of Clauses 9 to 15, wherein the proposed diagnosis includes one of the proposed differential diagnosis and the proposed final diagnosis.

[0023] Clause 17: A computer program product for providing machine learning analysis of healthcare information, the computer program product comprising at least one non-transitory computer-readable medium containing one or more instructions, which, when executed by at least one processor, cause the at least one processor to: receive patient-related healthcare data from a plurality of data sources, wherein the plurality of data sources include at least one of: 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 analysis system (RAS), a laboratory information system (LIS), a digital pathology system (DPS), a picture archiving and communication system (PACS), or any combination thereof; generate a patient health history profile based on the patient-related healthcare data; determine medical findings about the patient based on the patient health history profile using a machine learning model, wherein the medical findings include 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 related to the patient's medical findings to a patient-related device for display.

[0024] Clause 18: A computer program product pursuant to 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 of the patient; and generate the patient health history profile based on the longitudinal healthcare dataset.

[0025] Clause 19: A computer program product pursuant to Clause 18, wherein the one or more instructions further cause the at least one processor to: provide a web-accessible link to the patient's health history file to allow display of patient information on the device, and allow reception of the patient information from the device via the web-accessible link.

[0026] Clause 20: A computer program product pursuant to Clause 18 or 19, wherein the one or more instructions that cause the at least one processor to generate the patient health history file cause the at least one processor to: receive demographic information related to the patient; and generate the patient health history file based on the demographic information related to the patient.

[0027] Clause 21: A computer program product according to any one of Clauses 18-20, wherein the one or more instructions that cause the at least one processor to generate the patient health history file cause the at least one processor to: receive protected health information related to the patient; and generate the patient health history file based on the protected health information related to the patient.

[0028] Clause 22: A computer program product pursuant to any one of Clauses 17-21, wherein the at least one processor receives one or more instructions relating to the healthcare data of the patient, and the at least one processor retrieves the healthcare data relating to the patient from at least one of the plurality of data sources based on a unique patient identifier associated with the patient.

[0029] Clause 23: A computer program product according to any one of Clauses 17-22, wherein the at least one instruction causing the at least one processor to provide the data associated with the medical findings of the patient causes the at least one processor to: provide the data associated with the medical findings of the patient to a device associated with the patient based on receiving a request for medical findings including a unique patient identifier associated with the patient.

[0030] Clause 24: A computer program product pursuant to any one of Clauses 17-23, wherein the proposed diagnosis includes one of the proposed differential diagnosis and the proposed final diagnosis.

[0031] Clause 25: A system for providing machine learning analysis of healthcare information, comprising: at least one processor programmed or configured to: receive patient-related healthcare data from multiple data sources, wherein said multiple data sources 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 analysis system (RAS), a laboratory information system (LIS), a digital pathology system (DPS), a picture archiving and communication system (PACS), or any combination thereof; process the patient-related healthcare data; and generate a patient health history profile based on the processed patient-related healthcare data.

[0032] Clause 26: The system according to Clause 25, wherein, when processing patient-related healthcare data, the at least one processor is programmed or configured to: sequentially arrange the patient-related healthcare data over a period of time.

[0033] Clause 27: A system according to Clause 25 or 26, wherein the at least one processor is programmed or configured to: collect patient-related healthcare data from the plurality of data sources and store patient-related healthcare data in a data structure.

[0034] Clause 28: A system pursuant to any one of Clauses 25-27, wherein the patient’s patient health history file comprises a longitudinal dataset of the patient’s healthcare data over a period of time.

[0035] Clause 29: A method for providing machine learning analysis of healthcare information, comprising: receiving patient-related healthcare data from multiple data sources using at least one processor, wherein the multiple data sources 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 analysis system (RAS), a laboratory information system (LIS), a digital pathology system (DPS), a picture archiving and communication system (PACS), or any combination thereof; processing the patient-related healthcare data using the at least one processor; and generating a patient health history profile based on the patient-related healthcare data using the at least one processor.

[0036] Clause 30: The method according to Clause 29, wherein processing the healthcare data related to the patient includes: sequentially arranging the healthcare data related to the patient over a period of time.

[0037] Clause 31: The method according to Clause 29 or 30, wherein the method further comprises: collecting patient-related healthcare data from the plurality of data sources and storing the patient-related healthcare data in a data structure.

[0038] Clause 32: The method according to any one of Clauses 29 to 31, wherein the patient’s patient health history file comprises a longitudinal dataset of the patient’s healthcare data over a period of time.

[0039] Clause 33: A computer program product for providing machine learning analysis of healthcare information, the computer program product comprising at least one non-transitory computer-readable medium, the at least one non-transitory computer-readable medium comprising one or more instructions, which, when executed by at least one processor, cause the at least one processor to: receive patient-related healthcare data from a plurality of data sources, said plurality of data sources including at least one of: 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 analysis system (RAS), a laboratory information system (LIS), a digital pathology system (DPS), a picture archiving and communication system (PACS), or any combination thereof; process the patient-related healthcare data; and generate a patient health history profile based on the processing of the patient-related healthcare data.

[0040] Clause 34: The computer program product of claim 25, wherein one or more instructions causing the at least one processor to process healthcare data related to the patient cause the at least one processor to: sequentially arrange the healthcare data related to the patient over a period of time.

[0041] Clause 35: The computer program product of claim 25, wherein the one or more instructions further cause the at least one processor to: collect patient-related healthcare data from the plurality of data sources and store patient-related healthcare data in a data structure.

[0042] Clause 36: The computer program product of claim 25, wherein the patient health history file comprises a longitudinal dataset of the patient's healthcare data over a period of time.

[0043] These and other features and characteristics of this disclosure, as well as the methods of operation and function of the related elements of the structure, and the economic efficiency of the assembly of parts and manufacture, will become more apparent upon consideration of the following description and appended claims with reference to the accompanying drawings, all of which form part of this specification, wherein the same reference numerals denote corresponding parts in the figures. However, it should be clearly understood that the drawings are for illustrative and descriptive purposes only and are not intended to be construed as limiting the scope of this disclosure. As used in the specification and claims, the singular forms “a,” “an,” and “the” include plural indicators unless the context clearly specifies otherwise. Attached Figure Description

[0044] Additional advantages and details of non-limiting embodiments or aspects will be explained in more detail below with reference to exemplary embodiments shown in the accompanying drawings, in which:

[0045] Figure 1 The figures are for non-limiting embodiments of the environments in which the systems, devices, products, apparatuses and / or methods described herein can be implemented based on the principles of this disclosure;

[0046] Figure 2 A diagram illustrating a non-limiting embodiment of a system for providing machine learning analytics of healthcare information;

[0047] Figure 3 for Figure 1 Figures of non-limiting embodiments of components of one or more systems or devices of A and 1B;

[0048] Figure 4 A flowchart of a non-limiting embodiment of a process for providing machine learning-based healthcare information analysis;

[0049] Figure 5 A flowchart of a non-limiting embodiment of a process for generating patient records;

[0050] Figures 6A-6C A diagram illustrating a non-limiting embodiment or aspect of a process for providing machine learning-based healthcare information analysis. Detailed Implementation

[0051] For the purposes described below, the terms “end,” “upper,” “lower,” “right,” “left,” “vertical,” “horizontal,” “top,” “bottom,” “lateral,” “longitudinal,” and their derivatives should be used in relation to the orientation of this disclosure in the accompanying drawings. However, it should be understood that this disclosure may assume various alternative variations and sequences of steps unless explicitly specified otherwise. It should also be understood that the specific devices and processes shown in the accompanying drawings and described in the following specification are merely exemplary embodiments of this disclosure. Therefore, unless otherwise stated, specific dimensions and other physical characteristics relating to the embodiments or aspects of the embodiments disclosed herein should not be considered limiting.

[0052] Unless explicitly stated otherwise, no aspect, component, element, structure, action, step, function, instruction, etc., used herein should be construed as critical or necessary. Furthermore, as used herein, the articles “a” and “an” are intended to include one or more items and may be used interchangeably with “one or more” and “at least one.” Additionally, as used herein, the term “set” is intended to include one or more items (e.g., related items, unrelated items, combinations of related and unrelated items, etc.) and may be used interchangeably with “one or more” or “at least one.” The term “an” or similar language is used where only one item is anticipated. Furthermore, as used herein, the terms “have,” “possess,” “own,” etc., are intended to be open-ended terms. Furthermore, unless explicitly stated otherwise, the phrase “based on” is intended to mean “at least partially based on.”

[0053] As used herein, the terms "communication" and "transmission" can refer to the receiving, receiving, transmitting, delivering, providing, etc., of information (e.g., data, signals, messages, instructions, commands, etc.). For one unit (e.g., a device, system, component of a device or system, combination thereof, etc.) to communicate with another unit means that one unit is able to receive information directly or indirectly from and / or transmit information to the other unit. This can refer to a direct or indirect connection that is essentially wired and / or wireless. Furthermore, two units can communicate with each other even if the transmitted information can be modified, processed, relayed, and / or routed between the first and second units. For example, the first unit can communicate with the second unit even if it passively receives information and does not actively transmit information to the second unit. As another example, the first unit can communicate with the second unit if at least one intermediate unit (e.g., a third unit located between the first and second units) processes information received from the first unit and transmits the processed information to the second unit. In some non-limiting embodiments, a message can refer to a network packet (e.g., a data packet, etc.) that includes data. It should be understood that many other arrangements are possible.

[0054] As used herein, the term "system" can refer to one or more computing devices or a combination of computing devices, such as, but not limited to, processors, servers, client devices, software applications, and / or other similar components. Furthermore, as used herein, "server" or "processor" can refer to the server and / or processor described above as performing the preceding steps or functions, different servers and / or processors, and / or combinations of servers and / or processors. For example, as used in the specification and claims, a first server and / or a first processor described as performing a first step or function can refer to the same or different server and / or processor described as performing a second step or function.

[0055] Non-limiting embodiments of this disclosure relate to systems, methods, and computer program products for providing machine learning-based healthcare information analysis. In some non-limiting embodiments or aspects, an artificial intelligence (AI) shuttle system may receive patient-related healthcare data from multiple data sources, including at least one of: electronic medical record (EMR) systems, electronic health record (EHR) systems, patient procedure tracking systems, hospital information systems (HIS), radiology information systems (RIS), radiology analysis systems (RAS), laboratory information systems (LIS), digital pathology systems (DPS), picture archiving and communication systems (PACS), or any combination thereof. Based on the patient-related healthcare data, the system uses a machine learning model to determine medical findings about the patient, wherein the medical findings include at least one of a proposed diagnosis for the patient or a recommendation to perform one or more tests on the patient; and provides data related to the patient's medical findings to a patient-related device for display.

[0056] In some non-limiting embodiments, the AI ​​shuttle system can generate patient profiles based on patient-related healthcare data. In some non-limiting embodiments, the AI ​​shuttle system can provide web-accessible links to patient profiles to allow patient information to be displayed on the device and to allow patient information to be received from the device via the web-accessible links. In some non-limiting embodiments, when generating patient profiles, the AI ​​shuttle system can receive patient-related demographic information and generate patient profiles based on that patient-related demographic information.

[0057] In some non-limiting embodiments, when generating a patient profile, the AI ​​shuttle system may receive protected health information associated with the patient and generate a patient profile based on the protected health information associated with the patient. In some non-limiting embodiments, when receiving healthcare data associated with the patient, the AI ​​shuttle system may retrieve the healthcare data associated with the patient from at least one data source among multiple data sources based on a unique patient identifier associated with the patient. In some non-limiting embodiments, when providing data associated with a patient's medical findings, the AI ​​shuttle system may provide data associated with the patient's medical findings to a device associated with the patient based on a received request for the medical findings, which includes a unique patient identifier associated with the patient. In some non-limiting embodiments, proposing a diagnosis includes proposing one of proposing a differential diagnosis and proposing a final diagnosis.

[0058] In this way, non-limiting embodiments of this disclosure provide an AI shuttle system that allows the use of machine learning models, reducing the need for complex programming and processing resources in health informatics applications for healthcare data analysis tasks. Furthermore, it can reduce or eliminate the need for manual programming processes, as well as the need for specially configured network devices and / or reprogramming of equipment to communicate with various medical devices and information systems based on network communication configurations.

[0059] Now for reference Figure 1 , Figure 1 This is a diagram of a non-limiting embodiment of environment 100, in which the apparatus, system, method, and / or computer program product described herein may be implemented. Figure 1 As shown, environment 100 includes an AI shuttle system 102, data sources 104-1 to 104-N (hereinafter individually referred to as data source 104, or collectively as data source 104 where appropriate), user equipment 106, fluid injection system 108, and communication network 112. In some non-limiting embodiments, the AI ​​shuttle system 102, data source 104, user equipment 106, and / or fluid injection system 108 may be interconnected (e.g., establishing connections for communication) via wired connections, wireless connections, or a combination of wired and wireless connections. Any device or system in environment 100 may communicate with each other in the same or different communication network 112.

[0060] In some non-limiting embodiments, the AI ​​shuttle system 102 may include one or more devices capable of communicating with the data source 104, user equipment 106, and / or hospital information system 110 via a communication network 112. For example, the AI ​​shuttle system 102 may include one or more computing devices, such as one or more computers, one or more servers (e.g., cloud servers, a set of servers, etc.), one or more desktop computers, one or more mobile devices (e.g., one or more tablets, one or more smartphones, etc.). In some non-limiting embodiments, the AI ​​shuttle system 102 may include one or more applications (e.g., multiple applications) that perform a set of functions on an external application programming interface (API), which allows the AI ​​shuttle system 102 to send data to and receive data from external systems associated with the external API. In some non-limiting embodiments, the application may be supported by an application associated with the fluid injection system 108, which would allow the AI ​​shuttle system 102, which can be used as a control room display, to be the sole device controlling other systems and / or devices, and in such an example, the AI ​​shuttle system 102 may provide authentication functionality. In some non-limiting embodiments, the AI ​​shuttle system 102 may be a component of the user equipment 106, the fluid injection system 108, and / or the hospital information system 110.

[0061] In some non-limiting embodiments, data source 104 may include one or more devices capable of communicating with AI shuttle system 102, user device 106, and fluid injection system 108 via communication network 112. For example, data source 104 may include servers, computing devices (such as desktop computers), mobile devices (e.g., tablets, smartphones), wearable devices (such as wearable health sensors), implantable devices (such as pacemakers, internal body sensors, etc.), etc. In some non-limiting embodiments, data source 104 may include electronic medical record (EMR) systems, electronic health record (EHR) systems, patient procedure tracking systems, hospital information systems (HIS), radiology information systems (RIS), radiology analysis systems (RAS), laboratory information systems (LIS), pathology systems (such as digital pathology systems (DPS)), and / or image archiving and communication systems (such as picture archiving and communication systems (PACS)). Additionally or alternatively, data source 104 may include medical imaging systems (e.g., imaging scanners), fluid infusion systems (e.g., fluid injectors), facility-associated devices such as communication devices associated with medical devices (e.g., handheld medical devices, wearable medical devices, such as portable health sensors), fluid infusion systems, and / or patient-associated devices (e.g., user devices, such as patient-operated computing devices). Additionally or alternatively, data source 104 may include additional devices located at locations providing medical care, such as hospitals, such as Internet of Things (IoT) devices, and / or other devices located at patient-associated locations (e.g., devices in the patient's home, devices in the patient's vehicle, etc.).

[0062] In some non-limiting embodiments, user equipment 106 may include one or more devices capable of communicating with AI shuttle system 102, data source 104, fluid injection system 108, and / or hospital information system 110 via communication network 112. For example, user equipment 106 may include computing devices, such as one or more computers, including desktop computers, workstation devices, laptop computers, tablets, etc. In some non-limiting embodiments, at least a portion of the process performed at user equipment 106 may be performed at a remote server (e.g., a cloud computing server). In some non-limiting embodiments, user equipment 106 may provide a user interface for controlling the operation of fluid injection system 108, including generating instructions for and / or providing instructions to fluid injection system 108. Additionally or alternatively, user equipment 106 may display operating parameters of fluid injection system 108 during operation of fluid injection system 108 (e.g., during real-time operation). In some non-limiting embodiments, user equipment 106 may provide interconnectivity between fluid injection system 108 and other devices or systems (such as scanner devices (not shown)). In some non-limiting embodiments, user equipment 106 may include Certegra, provided by Bayer HealthCare LLC. ® Workstation. In some non-limiting embodiments, user equipment 106 may include a display unit (e.g., display device, display screen, etc.), such as a computer monitor, touchscreen, head-up display, etc., which can be used to display a user interface (e.g., a graphical user interface (GUI) of a software application) through which a user can interact with user equipment 106 to view parameters and / or control the operation of fluid injection system 108. For example, a user of user equipment 106 may use one or more hardware or software components of user equipment 106 in conjunction with a touchscreen, mouse, touchpad, keyboard, stylus, gesture sensing camera, microphone for receiving voice commands, etc., to provide input to user equipment 106.

[0063] In some non-limiting embodiments, the fluid injection system 108 may include one or more devices capable of communicating with the AI ​​shuttle system 102, the data source 104, the user device 106, and / or the hospital information system 110 via the communication network 112. For example, the fluid injection system 108 may include one or more computing devices, such as one or more computers, one or more servers (e.g., cloud servers, a set of servers, etc.), one or more desktop computers, one or more mobile devices (e.g., one or more tablets, one or more smartphones, etc.). In some non-limiting embodiments, the fluid injection system 108 may include one or more injection devices (e.g., one or more fluid injection devices, one or more fluid injectors). In some non-limiting embodiments, the fluid injection system 108 is configured to administer (e.g., inject, deliver, etc.) a contrast agent fluid comprising a contrast agent to a patient, and / or administer an aqueous fluid (such as saline) to the patient before, during, and / or after the administration of the contrast agent fluid. For example, the fluid injection system 108 may directly inject one or more prescribed doses of contrast agent fluid into the patient's bloodstream via a subcutaneous injection needle and syringe. In some non-limiting embodiments, the fluid infusion system 108 may be configured to continuously administer an aqueous fluid to a patient via a peripheral intravenous line (PIV) and catheter, and may introduce one or more prescribed doses of contrast agent fluid into the PIV and administer it to the patient via the catheter. In some non-limiting embodiments, the fluid infusion system 108 is configured to infuse a dose of contrast agent fluid concurrently with and / or after the administration of a specific volume of aqueous fluid. In some non-limiting embodiments, the fluid infusion system 108 may include one or more exemplary fluid infusion devices disclosed in: U.S. Patent Application Serial No. 09 / 715,330, filed November 17, 2000, granted U.S. Patent No. 6,643,537; U.S. Patent Application Serial No. 09 / 982,518, filed October 18, 2001, granted U.S. Patent No. 7,094,216; and U.S. Patent Application Serial No. 10 / 825,866, filed April 16, 2004. The disclosures of the following patents are incorporated herein by reference in their entirety: U.S. Patent No. 7,556,619; U.S. Patent Application Serial No. 12 / 437,011, filed May 7, 2009, entitled U.S. Patent No. 8,337,456; U.S. Patent Application Serial No. 12 / 476,513, filed June 2, 2009, entitled U.S. Patent No. 8,147,464; and U.S. Patent Application Serial No. 11 / 004,670, filed December 3, 2004, entitled U.S. Patent No. 8,540,698. In some non-limiting embodiments, the fluid injection system 108 may include a MEDRAD. ® Stellant CT Injection System, MEDRAD ®Stellant FLEX CT Injection System, MEDRAD ® MRXperion MR Injection System, MEDRAD ® Mark 7 Arterial Injection System, MEDRAD ® Intego PET infusion system or MEDRAD ® The Centargo CT injection system was entirely supplied by Bayer Healthcare LLC.

[0064] In some non-limiting embodiments, the hospital information system 110 may include one or more devices capable of communicating with the AI ​​shuttle system 102, the data source 104, the user device 106, and / or the fluid injection system 108 via the communication network 112. For example, the hospital information system 110 may include one or more computing devices, such as one or more desktop computers, one or more mobile devices, one or more servers, etc. In some non-limiting embodiments, the hospital information system 110 may include one or more subsystems, such as a patient procedure tracking system (e.g., a system for operating modal work lists, a system for providing patient demographic information for fluid injection procedures and / or medical imaging procedures, etc.), a fluid injector management system, an image archiving and communication system (e.g., a picture archiving and communication system (PACS)), a radiology information system, and a radiology analysis system (e.g., Radimetrics marketed and sold by Bayer HealthCare LLC). ® Enterprise applications) and / or other similar systems or devices.

[0065] In some non-limiting embodiments, the communication network 112 may include one or more wired and / or wireless networks. For example, the communication network 112 may include a cellular network (e.g., Long Term Evolution (LTE)). ® Networks include: third-generation (3G), fourth-generation (4G), fifth-generation (5G), sixth-generation (6G), code division multiple access (CDMA), etc.; public land mobile network (PLMN); local area network (LAN); wide area network (WAN); metropolitan area network (MAN); telephone network (e.g., public switched telephone network (PSTN)); private network; ad hoc network; intranet; Internet; fiber-based network; cloud computing network; short-range wireless communication network (e.g., Bluetooth). ® Networks, near field communication (NFC) networks, and / or similar devices, and / or combinations of these or other types of networks.

[0066] Figure 1 The number and arrangement of the systems and / or equipment shown are provided as examples. Figure 1Compared to those shown, there may be additional systems and / or equipment, fewer systems and / or equipment, different systems and / or equipment, or systems and / or equipment with different arrangements. Furthermore, Figure 1 The two or more systems and / or devices shown can be implemented within a single system or a single device, or Figure 1 The single system or single device shown can be implemented as multiple distributed systems or devices. Additionally or alternatively, a group of systems or a group of devices in environment 100 (e.g., one or more systems, one or more devices) can perform one or more functions described as being performed by another group of systems or another group of devices in environment 100.

[0067] Now for reference Figure 2 , Figure 2 This is a diagram of a non-limiting embodiment of a system 200 for providing machine learning-based healthcare information analysis. In some non-limiting embodiments, one or more of the functions described herein with respect to system 200 may be performed by AI shuttle system 102 (e.g., entirely, partially, etc.). In some non-limiting embodiments, one or more of the functions described with respect to system 200 may be performed by another device or group of devices separate from and / or including AI shuttle system 102 (e.g., entirely, partially, etc.), 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] like Figure 2 As shown, system 200 includes an AI shuttle system 102, a fluid injection system 108, a workstation device 206 (which includes a display unit 206A), a hospital information system 210, an electronic medical record (EMR) system 212, an electronic health record (EHR) system 213, a digital pathology system 214, a medical imaging system 216, and a laboratory information system 218. In some non-limiting embodiments, the AI ​​shuttle system 102 can interconnect with the 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, wireless, or a combination of wired and wireless connections (e.g., establishing connections for communication and / or similar operations). In some non-limiting embodiments, the workstation device 206 may be the same as or similar to the 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] like Figure 2As further shown, the hospital information system 210 may include multiple subsystems. These subsystems may include a patient procedure tracking system 210A, an image archiving and communication system 210B, a radiology information system 210C, and a radiology analysis system 210D. In some non-limiting embodiments, the AI ​​shuttle system 102 may receive healthcare data from the hospital information system 210 via a communication network (e.g., communication network 112) according to communication protocols used for transmitting informatics-related data. For example, AI Shuttle System 102 may receive data related to patient procedures from Hospital Information System 210 (e.g., from Patient Procedure Tracking System 210A) via a communication network, according to the Medical Digital Imaging and Communications (DICOM) communication protocol; may receive data related to the operation of Fluid Injection System 108 from Hospital Information System 210 via a communication network, based on API calls (e.g., API calls from AI Shuttle System 102); may receive data related to radiographic images from Hospital Information System 210 (e.g., from Image Archive and Communications System 210B) via a communication network, according to the DICOM communication protocol; may receive data related to patient examination procedures from Hospital Information System 210 (e.g., from Radiology Information System 210C) via a communication network, based on the Health Level 7 (HL7) standard communication protocol; and / or may receive data related to radiation dose during medical imaging procedures from Hospital Information System 210 (e.g., from Radiology Analysis System 210D) via a communication network, based on API calls (e.g., API calls initiated by AI Shuttle System 102 to Radiology Analysis System 210D).

[0070] In some non-limiting embodiments, the EMR system 212 may include one or more devices capable of communicating via a communication network (e.g., communication network 114) with the 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. In some non-limiting embodiments, the EMR system 212 may include one or more devices for receiving, managing, storing, and / or transmitting electronic medical records, which include medical record data associated with a patient's medical history, such as demographics, medical history, medications and allergies, immune status, laboratory test results, radiographic images, vital signs, personal statistics (e.g., age, weight, height, etc.), billing information, etc., associated with a specific instance of medical care.

[0071] In some non-limiting embodiments, the EHR system 213 may include one or more devices capable of communicating via a communication network (e.g., communication network 112) with the 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. In some non-limiting embodiments, the EHR system 213 may include one or more devices for receiving, managing, storing, and / or transmitting electronic health records, which include medical record data associated with a patient's medical records, such as demographics, medical history, medications and allergies, immune status, laboratory test results, radiographic images, vital signs, personal statistics (e.g., age, weight, height, etc.), billing information, etc., associated with various providers and / or locations of healthcare (e.g., offices, clinics, hospitals, etc.). Additionally or alternatively, the EHR system 213 may include a patient portal (e.g., a web-based interface) to allow patients to interact with their respective electronic medical records. In some non-limiting embodiments, EMR system 212 may be a data source for EHR system 213.

[0072] In some non-limiting embodiments, the digital pathology system 214 may include a communication network (e.g., Figure 1 The communication network 112 communicates with one or more devices, including the 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. In some non-limiting embodiments, the medical imaging system 216 may include one or more devices for receiving, managing, transmitting, and / or interpreting pathological information, including data (e.g., image data, slide data, etc.) analyzed by microscopes, scanners, and / or other similar devices.

[0073] In some non-limiting embodiments, the medical imaging system 216 may include one or more devices capable of communicating via a communication network (e.g., communication network 112) with the 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. In some non-limiting embodiments, the medical imaging system 216 may include one or more scanners, such as computed tomography (CT) scanners and / or magnetic resonance imaging (MRI) scanners, capable of communicating via a communication network and performing medical imaging procedures involving the use of radioactive contrast agent materials.

[0074] In some non-limiting embodiments, the laboratory information system 218 may include one or more devices capable of communicating with the AI ​​shuttle system 102, the fluid injection system 108, the workstation device 206, the hospital information system 210, the EMR system 212, the digital pathology system 214, and / or the medical imaging system 216 via a communication network (e.g., communication network 112). In some non-limiting embodiments, the medical imaging system 216 may include one or more devices for recording, managing, updating, and / or storing patient data and / or test data for clinical and / or anatomical 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.

[0075] In some non-limiting embodiments, the AI ​​shuttle system 102 may include multiple applications, and each of the multiple applications may be associated with an API associated with the corresponding 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.), which allows other systems and / or devices to interface with the AI ​​shuttle system 102 (e.g., communicate, establish communication interfaces, etc.) and / or allows the AI ​​shuttle system 102 to interface with other systems and / or devices (e.g., various subsystems of the hospital information system 210, such as the patient procedure tracking system 210A, the image archiving and communication system 210B, the radiology information system 210C, and / or the radiology analysis system 210D).

[0076] In some non-limiting embodiments, the 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 users to access information such as medical findings (e.g., a patient's medical findings).

[0077] like Figure 2 As further shown, workstation device 206 may include display unit 206A. In some non-limiting embodiments, display unit 206A may be able to display a user interface (e.g., a web-based user interface) provided by AI shuttle system 102. In some non-limiting embodiments, display unit 206A may include computing devices, such as smart display units, portable computers, such as tablets, laptops, etc. In some non-limiting embodiments, display unit 206A may include a touchscreen for receiving user input. In some non-limiting embodiments, display unit 206A may include display devices (e.g., monitors, screens, etc. for displaying visual information).

[0078] In some non-limiting embodiments, the AI ​​shuttle system 102 can transmit data associated with images received from the medical imaging system 216 to the fluid injection system 108 via a communication network. For example, the AI ​​shuttle system 102 can transmit data associated with images received from the medical imaging system 216 to the fluid injection system 218 via a communication network based on API calls from the fluid injection system 216. In some non-limiting embodiments, the AI ​​shuttle system 102 can transmit data associated with a fluid injection procedure (e.g., data associated with the volume, flow rate, and / or time of injecting contrast agent material into the patient) received from the fluid injection system 108 to the medical imaging system 216 via a communication network. For example, the AI ​​shuttle system 102 can transmit data associated with a fluid injection procedure received from the fluid injection system 108 to the medical imaging system 216 via a communication network based on API calls from the medical imaging system 216 (e.g., API calls for the Imaging System Interface (ISI), API calls for the ISI2 interface, API calls for connecting to the CT interface, etc.). In some non-limiting embodiments, the medical imaging system 216 may perform medical imaging procedures on a patient based on data (including injection protocols) associated with a fluid injection procedure. In some non-limiting embodiments, the AI ​​shuttle system 102 may receive data associated with the operation of the medical imaging system 216 via a communication network based on API calls (e.g., API calls from the AI ​​shuttle system 102 to the medical imaging system 216).

[0079] In some non-limiting embodiments, the AI ​​shuttle system 102 may provide a communication interface between the hospital information system 210 and the fluid injection system 108, enabling the fluid injection system 108 to receive data based on API calls from the fluid injection system 108 to the AI ​​shuttle system 102. In some non-limiting embodiments, the AI ​​shuttle system 102 may transmit data associated with informatics received from the hospital information system 210 to the fluid injection system 108 via a communication network (e.g., communication network 112). For example, the AI ​​shuttle system 102 may transmit data associated with informatics received from the hospital information system 210 to the fluid injection system 108 via a communication network based on API calls from the fluid injection system 108.

[0080] Now for reference Figure 3 , Figure 3This 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] like Figure 3 As shown, device 300 may include bus 302, processor 304, memory 306, storage unit 308, input unit 310, output unit 312, and communication interface 314. Bus 302 may include components that allow communication between 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), graphics processing unit (GPU), accelerated processing unit (APU), etc.), microprocessor, digital signal processor (DSP), and / or any processing unit that can be programmed to perform functions (e.g., a field-programmable gate array (FPGA), application-specific integrated circuit (ASIC), etc.). 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 storage, optical storage, 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 hard disk (e.g., magnetic disk, optical disk, magneto-optical disk, solid-state disk, etc.), compact disc (CD), digital versatile disc (DVD), floppy disk, cassette tape, magnetic tape, and / or another type of computer-readable medium and corresponding drives.

[0083] Input component 310 may include components that allow device 300 to receive information, such as via user input (e.g., touchscreen display, keyboard, keypad, mouse, button, switch, microphone, etc.). Additionally or alternatively, input component 310 may include sensors for sensing information (e.g., Global Positioning System (GPS) components, accelerometers, gyroscopes, actuators, etc.). Output component 312 may include components that provide output information from device 300 (e.g., display, speaker, one or more light-emitting diodes (LEDs), etc.).

[0084] Communication interface 314 may include transceiver-like components (e.g., a transceiver, a separate receiver, and a transmitter), enabling 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 allow device 300 to receive information from 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 interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, or a Wi-Fi interface. ® Interfaces, cellular network interfaces, etc.

[0085] Device 300 can perform one or more processes described herein. Device 300 can perform these processes based on software instructions stored in a computer-readable medium (such as memory 306 and / or storage unit 308) executed by processor 304. 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 within a single physical storage device or memory space distributed across multiple physical storage devices.

[0086] Software instructions may be read into memory 306 and / or storage unit 308 via communication interface 314 from another computer-readable medium or from another device. When executed, the software instructions stored in memory 306 and / or storage unit 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. Therefore, the embodiments described herein are not limited to any particular combination of hardware circuitry and software.

[0087] Provided Figure 3 The number and arrangement of components shown are for illustrative purposes only. In some non-limiting embodiments, device 300 may include components related to... Figure 3The components shown may be additional components, fewer components, different components, or components with different arrangements compared to those shown. 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] Now for reference Figure 4 , Figure 4 This is a flowchart of a non-limiting embodiment of a machine learning analysis process 400 for providing healthcare information. In some non-limiting embodiments, one or more steps of process 400 are performed by AI shuttle system 102 (e.g., entirely, partially, etc.). In some non-limiting embodiments, one or more steps of process 400 are performed by another device or group of devices separate from or including AI shuttle system 102 (e.g., entirely, partially, etc.), such as a data source (e.g., data source 104), user devices (e.g., user device 106, workstation device 206), fluid injection systems (e.g., fluid injection system 108, one or more devices such as fluid injection system 108), and / or hospital information systems (e.g., hospital information system 110, one or more devices such as hospital information system 110, hospital information system 210, one or more subsystems such as hospital information system 210, etc.).

[0089] like Figure 4 As shown, in step 402, process 400 may include receiving patient-related healthcare data from multiple data sources. For example, AI shuttle system 102 may receive patient-related healthcare data from data source 104. In some non-limiting embodiments, data source 104 may include at least one of the following: electronic medical record (EMR) system (e.g., EMR system 212), patient procedure tracking system (e.g., patient procedure tracking system 210A), hospital information system (HIS) (e.g., hospital information system 210), radiology information system (RIS) (e.g., radiology information system 210C), radiology analysis system (RAS) (e.g., radiology analysis system 210D), laboratory information system (LIS) (e.g., laboratory information system 218), digital pathology system (DPS) (e.g., digital pathology system 214), picture archiving and communication system (PACS) (e.g., picture archiving and communication system 210B), or any combination thereof.

[0090] In some non-limiting embodiments, patient-associated healthcare data may include medical record data associated with the patient's medical history, protected health information associated with the patient, patient-associated demographic information, identification data associated with the patient's identifier, and data associated with patient examination procedures (e.g., fluid infusion procedures and / or medical imaging procedures performed on the patient), such as data associated with contrast agent fluid provided during a fluid infusion procedure, the specifications of the catheter used during the fluid infusion procedure, and the fluid infusion protocol used for the fluid infusion procedure. In some non-limiting embodiments, the AI ​​shuttle system 102 may store patient-associated healthcare data in a data structure (e.g., a database). For example, the AI ​​shuttle system 102 may store patient-associated healthcare data in a data structure having a unique identifier for the patient.

[0091] In some non-limiting embodiments, the AI ​​shuttle system 102 can retrieve patient-associated healthcare data from at least one data source based on a patient-associated identifier (e.g., based on polling and / or pulling software operations). For example, the AI ​​shuttle system 102 can transmit an identifier associated with a patient (e.g., a patient undergoing an examination procedure) (e.g., a unique patient identifier, such as a unique patient identifier from a patient's medical record) to a data source 104, such as a hospital information system, and the AI ​​shuttle system 102 can receive patient-associated healthcare data from that data source 104. In some non-limiting embodiments, the data source 104 can receive the patient-associated identifier, retrieve patient-associated healthcare data based on the identifier, and transmit the patient-associated healthcare data to the AI ​​shuttle system 102. In some non-limiting embodiments, the identifier can be associated with the patient's patient record (e.g., a patient's patient record stored in the patient procedure tracking system 210A of the hospital information system 210).

[0092] like Figure 4 As shown, at step 404, process 400 may include generating a patient profile based on 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 healthcare data associated with the patient. In some non-limiting embodiments, AI shuttle system 102 may generate a patient profile based on demographic information and / or protected health information associated with the patient.

[0093] like Figure 4As shown, at step 406, process 400 may include using a machine learning model to determine a patient's medical findings. For example, AI shuttle system 102 may use a machine learning model to determine a patient's medical findings based on patient-related healthcare data. In such an example, AI shuttle system 102 may provide patient-related healthcare data as input to the machine learning model, and the machine learning model may provide the patient's medical findings as output. In some non-limiting embodiments, the medical findings may include a proposed diagnosis for the patient and / or recommendations to perform one or more tests (e.g., examination procedures, such as imaging procedures) 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 patient-associated healthcare data as input, and the machine learning model may be configured to provide predictions of a proposed diagnosis for the patient and / or predictions of recommendations for performing one or more tests on the patient as output.

[0095] In some non-limiting embodiments, the AI ​​shuttle system 102 may store a patient's medical findings along with the patient's patient file. For example, the AI ​​shuttle system 102 may store a patient's medical findings along with the patient's patient file based on the determination of the patient's medical findings.

[0096] like Figure 4 As shown, in step 408, process 400 may include providing data associated with the patient's medical findings. For example, AI shuttle system 102 may provide data associated with the patient's medical findings to a device associated with the patient for display. In some non-limiting embodiments, AI shuttle system 102 may provide data associated with the patient's medical findings to user devices (e.g., user device 106, workstation device 206, etc.), fluid injection systems (e.g., fluid injection system 108), hospital information systems (e.g., hospital information system 110, hospital information system 210, etc.) 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, the AI ​​shuttle system 102 may provide data associated with a patient's medical findings to a patient-associated device based on a received request for the medical findings. For example, the AI ​​shuttle system 102 may receive a request for medical findings that include a unique patient identifier associated with the patient. The AI ​​shuttle system 102 may retrieve the medical findings from a data structure based on the unique patient identifier associated with the patient, and the AI ​​shuttle system 102 may transmit the medical findings to the system or device that provided the request for the medical findings.

[0098] In some non-limiting embodiments, the AI ​​shuttle system 102 may provide a web-accessible link to a patient profile to allow display of patient information (such as the patient's medical findings) on a device (e.g., user device 106, display unit 206A of workstation device 206, etc.) and / or allow receiving patient information from the device via the web-accessible link. In some non-limiting embodiments, the AI ​​shuttle system 102 may provide a user interface with a web-accessible link, and the AI ​​shuttle system 102 may receive the selection of a patient identifier via the user interface. In some non-limiting embodiments, the AI ​​shuttle system 102 may receive the identifier selection based on user input. For example, a user may select an identifier associated with the patient profile (e.g., a patient identifier including a unique identifier) ​​by selecting (e.g., touching, clicking, pressing, etc.) an identifier in the user interface.

[0099] Now for reference Figure 5 , Figure 5 This is a flowchart of a non-limiting embodiment of a process 500 for generating patient profiles for a patient. In some non-limiting embodiments, one or more steps of process 500 are performed by AI shuttle system 102 (e.g., entirely, partially, etc.). In some non-limiting embodiments, one or more steps of process 500 are performed by another device or group of devices separate from or including AI shuttle system 102 (e.g., entirely, partially, etc.), such as a data source (e.g., data source 104), user devices (e.g., user device 106, workstation device 206), fluid injection systems (e.g., fluid injection system 108, one or more devices such as fluid injection system 108), and / or hospital information systems (e.g., hospital information system 110, one or more devices such as hospital information system 110, hospital information system 210, one or more subsystems such as hospital information system 210, etc.).

[0100] like Figure 5As shown, in step 502, process 500 may include receiving patient-related healthcare data from multiple data sources. For example, AI shuttle system 102 may receive patient-related data from data source 104 in the same or similar manner as described above in step 402 of process 400. In some non-limiting embodiments, AI shuttle system 102 may receive patient-related healthcare data from a first data source 104 of data sources 104 asynchronously (e.g., not simultaneously with the second data source 104) compared to the second data source 104. In some non-limiting embodiments, AI shuttle system 102 may receive patient-related healthcare data from all data sources 104 synchronously.

[0101] like Figure 5 As shown, in step 504, process 500 may include processing patient-related healthcare data. For example, AI shuttle system 102 may process patient-related healthcare data based on received healthcare data. In some non-limiting embodiments, patient-related healthcare data may include healthcare data associated with each of a plurality of individual events concerning the patient's medical care over a period of time. Each of the plurality of individual events may relate to a specific instance of the patient's medical care 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 patient-associated healthcare data, the AI ​​shuttle system 102 may determine that the healthcare data received from the data source 104 is associated with the correct patient profile and / or that the healthcare data is complete over a period of time. For example, the AI ​​shuttle system 102 may compare the patient's unique patient identifier with a patient identifier included in the patient-associated healthcare data. If the AI ​​shuttle system 102 determines that the patient's unique patient identifier corresponds to a patient identifier included in the healthcare data, then the AI ​​shuttle system 102 may determine that the healthcare data is associated with the correct patient profile. If the AI ​​shuttle system 102 determines that the patient's unique patient identifier does not correspond to a patient identifier included in the healthcare data, then the 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 can determine whether a patient's healthcare data is complete within a given period based on the patient's unique patient identifier. For instance, AI shuttle system 102 can compare the patient's unique patient identifier with patient identifiers included in the healthcare data associated with the patient (e.g., multiple records included in the healthcare data associated with the patient) and determine whether the healthcare data is complete within the time period. If AI shuttle system 102 determines that the patient's unique patient identifier corresponds to all healthcare data associated with the patient within that time period (e.g., all of multiple records), then AI shuttle system 102 can determine that the patient's healthcare data is complete within that time period. If AI shuttle system 102 determines that the patient's unique patient identifier does not correspond to all healthcare data associated with the patient within that time period, or that healthcare data associated with the patient is missing for time intervals within that time period, then AI shuttle system 102 can determine that the patient's healthcare data is incomplete within that time period.

[0104] In some non-limiting embodiments, when processing patient-related healthcare data, the AI ​​shuttle system 102 may collect healthcare data from all data sources 104 for each of a plurality of patients and store the healthcare data associated with each of the plurality of patients separately in a data structure (e.g., a database). In some non-limiting embodiments, when processing patient-related healthcare data, the AI ​​shuttle system 102 may detect whether the healthcare data in each data source 104 contains errors relative to healthcare data in one or more other data sources 104. In some non-limiting embodiments, the AI ​​shuttle system 102 may correct detected errors based on the detected errors. Additionally or alternatively, the AI ​​shuttle system 102 may provide notification about detected errors (e.g., a notification message to an operator). In some non-limiting embodiments, the AI ​​shuttle system 102 may provide prompts (e.g., prompts in a user interface) to accept or reject proposed error correction.

[0105] In some non-limiting embodiments, when processing patient-related healthcare data, the AI ​​shuttle system 102 can sequentially arrange all of the patient's healthcare data (e.g., for a patient profile). For example, the AI ​​shuttle system 102 can receive patient-related healthcare data from all data sources 104 based on the patient's unique patient identifier, and the AI ​​shuttle system 102 can arrange all of the patient's healthcare data in a time-based sequence to provide a patient health history profile. In some non-limiting embodiments, when processing patient-related healthcare data, the AI ​​shuttle system 102 can arrange the patient's healthcare data into subsequences based on the characteristics of the medical care associated with the patient's healthcare data. For example, regarding the medical care associated with the patient's healthcare data, the AI ​​shuttle system 102 can arrange the patient's healthcare data into subsequences based on treatment type, type of medication used and / or type of medical device, location of the patient's medical problem, etc. In some non-limiting embodiments, the AI ​​shuttle system 102 can sequentially arrange all of the patient's healthcare data based on determining that the patient's healthcare data is complete within a time period.

[0106] like Figure 5 As shown, in step 506, process 500 may include generating a patient profile. For example, AI shuttle system 102 may generate a patient profile based on processing healthcare data associated with the patient. In some non-limiting embodiments, AI shuttle system 102 may generate a patient profile as a patient health history profile for the patient, which includes a longitudinal dataset of healthcare data (e.g., from all data sources 104) and / or a cross-sectional dataset of healthcare data. In some non-limiting embodiments, the longitudinal dataset of healthcare data may include healthcare data collected from data source 104 (e.g., data source 104 from multiple different systems, such as different hospital systems) over time periods (e.g., specified time periods, predetermined time periods, automatically selected time periods, etc.) of multiple 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 sources 104 of data sources 104 over time instances (e.g., specified time instances, predetermined time instances, automatically selected time instances, etc.) of 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] Now for reference Figures 6A-6C , Figures 6A-6CThis is a diagram of a non-limiting embodiment or aspect of an implementation of 600 related to a process (e.g., process 600) for providing healthcare information. In some non-limiting embodiments or aspects, one or more steps of the process may be performed (e.g., entirely, partially, etc.) by an AI shuttle system 102 (e.g., by one or more devices of the AI ​​shuttle system 102). In some non-limiting embodiments or aspects, one or more steps of the process may be performed (e.g., entirely, partially, etc.) by another device or group of devices separate from or including the 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, one or more devices such as fluid injection system 108), and / or a hospital information system (e.g., hospital information system 110, one or more devices such as hospital information system 110, hospital information system 210, one or more subsystems such as hospital information system 210, etc.).

[0108] like Figure 6A As indicated by reference numeral 605 in the accompanying drawings, the AI ​​shuttle system 102 can receive patient-related healthcare data from one or more data sources 104 via a communication network 112. Figure 6B As indicated by reference numeral 610 in the accompanying drawings, the AI ​​shuttle system 102 can generate patient profiles for patients. For example, the AI ​​shuttle system 102 can generate patient profiles based on received healthcare data associated with the patient. Figure 6B As further illustrated by reference numeral 615 in the accompanying drawings, the AI ​​shuttle system 102 can determine the patient's medical findings using a machine learning model based on patient-related healthcare data. Figure 6C As shown by reference numerals 620 and 625 in the accompanying drawings, the AI ​​shuttle system 102 can provide data related to the patient's medical findings to user devices (e.g., user device 106, workstation 206, and / or similar devices) via communication network 112, and provide web-accessible links to the patient's profile.

[0109] Although the systems, methods, and computer program products described above have been described in detail for illustrative purposes based on embodiments currently considered to be the most practical and preferred, it should be understood that such details are for that purpose only, and this disclosure is not limited to the described embodiments or aspects, but is intended to cover modifications and equivalent arrangements within the spirit and scope of the appended claims. For example, it should be understood that this disclosure contemplates that, to the extent possible, at least one feature of any embodiment or aspect may be combined with at least one feature of any other embodiment.

Claims

1. Systems for providing machine learning analytics to deliver healthcare information, including: At least one processor is programmed or configured to: Receive patient-related healthcare data from multiple data sources, wherein the multiple data sources include at least one of the following: Electronic Medical Record (EMR) system Electronic Health Record (EHR) system Patient procedure tracking system Hospital Information System (HIS) Radiographic Information System (RIS). Radiometric Analysis System (RAS) Laboratory Information System (LIS) Digital Pathology System (DPS) Picture Archiving and Communication System (PACS) or any combination thereof; A patient health history record is generated based on the healthcare data associated with the patient. The medical findings of the patient are determined using a machine learning model based on the patient’s health history file, wherein the medical findings include at least one of a proposed diagnosis for the patient or a recommendation to perform one or more tests on the patient. Data related to the patient's medical findings is provided to patient-related devices for display.

2. The system according to claim 1, wherein, When generating the patient's health history file based on healthcare data related to the patient, the at least one processor is programmed or configured to: Receive healthcare data related to the patient; Determine the longitudinal healthcare dataset of the patients; The patient's health history profile is generated based on the longitudinal healthcare dataset.

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's health history record to allow display of patient information on the device and to allow receiving of the patient information from the device via the web-accessible link.

4. The system according to claims 1-3, wherein, When generating a patient's health history record, the at least one processor is programmed or configured to: Receive demographic information related to the patient; and The patient's health history file is generated based on the demographic information associated with the patient.

5. The system according to any one of claims 1-4, wherein, When generating the patient's health history file, the at least one processor is programmed or configured to: Receive protected health information related to the patient; and Based on the protected health information associated with the patient, a patient health history file is generated.

6. The system according to any one of claims 1-5, wherein, When the healthcare data related to the patient is received, the at least one processor is programmed or configured to: Based on a unique patient identifier associated with the patient, the healthcare data related to the patient is retrieved from at least one of the plurality of data sources.

7. The system according to any one of claims 1-6, wherein, When providing data related to the patient's medical findings, the at least one processor is programmed or configured to: Based on a received request for medical findings including a unique patient identifier associated with the patient, the data associated with the medical findings is provided to the device associated with the patient.

8. The system according to any one of claims 1-7, wherein, The proposed diagnosis includes one of the proposed differential diagnosis and the proposed final diagnosis.

9. Methods for providing machine learning analytics for healthcare information, including: Receive patient-related healthcare data from multiple data sources, wherein the multiple data sources include at least one of the following: Electronic Medical Record (EMR) system Electronic Health Record (EHR) system Patient procedure tracking system Hospital Information System (HIS) Radiographic Information System (RIS). Radiometric Analysis System (RAS) Laboratory Information System (LIS) Digital Pathology System (DPS) Picture Archiving and Communication System (PACS) or any combination thereof; A patient health history record is generated based on the healthcare data associated with the patient. The medical findings of the patient are determined using a machine learning model based on the patient’s health history file, wherein the medical findings include at least one of a proposed diagnosis for the patient or a recommendation to perform one or more tests on the patient. Data related to the patient's medical findings is provided to patient-related devices for display.

10. The method according to claim 9, wherein, The generation of a patient health history profile based on the patient's associated healthcare data includes: Receive healthcare data related to the patient; Determine the longitudinal healthcare dataset of the patients; The patient's health history profile is generated based on the longitudinal healthcare dataset.

11. The method of claim 10, further comprising: Provide a web-accessible link to the patient's health history record to allow display of patient information on the device and to allow receiving of the patient information from the device via the web-accessible link.

12. The method of claim 10 or 11, wherein generating the patient health history file comprises: Receive demographic information related to the patient; as well as The patient's health history file is generated based on the demographic information associated with the patient.

13. The method according to any one of claims 10-12, wherein generating the patient's health history file comprises: Receive protected health information related to the patient; as well as Based on the protected health information associated with the patient, a patient health history file is generated.

14. The method according to any one of claims 9 to 13, wherein receiving the healthcare data related to the patient comprises: Based on a unique patient identifier associated with the patient, the healthcare data related to the patient is retrieved from at least one of the plurality of data sources.

15. The method according to any one of claims 9 to 14, wherein, Data related to the patient's medical findings includes: Based on a received request for medical findings including a unique patient identifier associated with the patient, the data associated with the medical findings is provided to the device associated with the patient.

16. The method according to any one of claims 9 to 15, wherein, The proposed diagnosis includes one of the proposed differential diagnosis and the proposed final diagnosis.

17. A computer program product for providing machine learning analysis of healthcare information, the computer program product comprising at least one non-transitory computer-readable medium, the at least one non-transitory computer-readable medium containing one or more instructions, the one or more instructions causing the at least one processor, when executed by at least one processor, to: Receive patient-related healthcare data from multiple data sources, wherein the multiple data sources include at least one of the following: Electronic Medical Record (EMR) system Electronic Health Record (EHR) system Patient procedure tracking system Hospital Information System (HIS) Radiographic Information System (RIS). Radiometric Analysis System (RAS) Laboratory Information System (LIS) Digital Pathology System (DPS) Picture Archiving and Communication System (PACS) or any combination thereof; A patient health history record is generated based on the healthcare data associated with the patient. The medical findings of the patient are determined using a machine learning model based on the patient’s health history file, wherein the medical findings include at least one of a proposed diagnosis for the patient or a recommendation to perform one or more tests on the patient. Data related to the patient's medical findings is provided to patient-related devices 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 file based on the healthcare data associated with the patient cause the at least one processor to: Receive healthcare data related to the patient; Determine the longitudinal healthcare dataset of the patients; The patient's health history profile is generated based on the longitudinal healthcare dataset.

19. The computer program product according to claim 18, wherein, The one or more instructions further cause the at least one processor to: Provide a web-accessible link to the patient's health history record to allow display of patient information on the device and to allow receiving of the patient information from the device via the web-accessible link.

20. The computer program product according to claim 18 or 19, wherein, The one or more instructions that cause the at least one processor to generate the patient's health history file, cause the at least one processor to: Receive demographic information related to the patient; as well as The patient's health history file is generated based on the demographic information associated with the patient.

21. The computer program product according to any one of claims 18-20, wherein, The one or more instructions that cause the at least one processor to generate the patient's health history file, cause the at least one processor to: Receive protected health information related to the patient; as well as Based on the protected health information associated with the patient, a patient health history file is generated.

22. The computer program product according to any one of claims 17-21, wherein, The at least one processor receives one or more instructions related to the healthcare data associated with the patient, causing the at least one processor to: Based on a unique patient identifier associated with the patient, the healthcare data related to the patient is retrieved from at least one of the plurality of data sources.

23. The computer program product according to any one of claims 17-22, wherein, The at least one instruction that causes the at least one processor to provide the data related to the patient's medical findings causes the at least one processor to: Based on a received request for medical findings including a unique patient identifier associated with the patient, the data associated with the medical findings is provided to the device associated with the patient.

24. The computer program product according to any one of claims 17-23, wherein, The proposed diagnosis includes one of the proposed differential diagnosis and the proposed final diagnosis.

Images