Remote patient management systems and methods

Abstract

A system for managing heart disease, the system including a patient management platform for managing heart disease in patients, the patient management platform connected with one or more mobile and remote patient monitoring device(s) configured to remotely obtain patient physiological attributes outside of a clinic or hospital setting. The patient management platform is configured to receive patient data through electronic patient data systems (e g., an EMR) and compare the physiological attributes and patient data with at least one heart disease treatment protocol. Based on the analyzing, the platform determines one or more recommended action(s) to be taken according to at least one heart disease treatment protocol and generates a notification of the recommended action(s).

Description

REMOTE PATIENT MANAGEMENT SYSTEMS AND METHODSRELATED APPLICATIONS

[0001] This application is claims priority to U.S. Provisional Patent Application No. 63 / 672,417, filed July 17, 2024, the entire disclosure of which is hereby incorporated herein by reference.BACKGROUND

[0002] Many patients have conditions that rapidly fluctuate and may require frequent monitoring and treatment protocol changes over time including cancer, neurological disease, and COPD, for example. Heart failure is one such example and a leading cause of patient mortality and hospitalization, particularly for older individuals. Keeping such patients in a hospital setting, who may otherwise be ambulatory and capable of caring for themselves, can be substantially resource-consuming for the hospitals and inconvenient and costly for the patients.

[0003] Patients admitted into hospitals for heart attacks or heart failure are in particular danger of additional heart attacks, stroke, and / or excess fluid buildup in the lungs. Upon diagnosis, patients are often administered medications such as beta blockers or diuretics to manage these conditions. Patients who are not given the correct treatment or dosage are at risk of adverse events and readmission.

[0004] Blood pressure, abnormal heart rhythms, and weight changes, among other factors, may be indicators of the risk of additional adverse events. Blood tests, including for the presence of NT -pro BNP, are also important indicators of risk, particularly fluid overload relating to heart failure, and have been found to be useful in determining appropriate levels of medication administration and titration. Improved systems for managing the care of diseased patients are needed to improve patient outcomes and reduce readmission and costs.SUMMARY

[0005] Systems and methods for managing disease are described, which may include a patient management platform connected with one or more mobile and remote patientmonitoring devices. The mobile monitoring devices can include heart rate monitors, blood pressure monitors, blood-oxygen monitors, activity trackers, and other remote computing devices configured to collect and communicate patient data over a network to the platform.

[0006] The patient data may be consolidated and stored by the patient management platform, which is further configured to interface with patient data resources including an electronic medical record (EMR) or laboratory information system (LIS) database, for example, providing access to patient medical data and records including diagnosis, medications, and procedures, for example. The patient management platform is configured to analyze the remotely obtained data together with other patient data from the EMR or LIS. Other patient data can include, for example, recent measurements of NT-pro BNP and the current type and dosage of a beta blocker or other medication prescribed to a patient. Based on analyzing the data and comparing it to a medical guideline or treatment protocol, one or more recommended actions are determined and communicated to a patient and / or provider.

[0007] The recommended actions can be communicated for display on a mobile device of the patient, for example, or to a system connected with a medical provider’s office such as a stand-alone application, web interface, and / or add-on to an EMR interface. Recommended actions can include scheduling tests (e.g., for NT-pro BNP), engaging the mobile / remote patient monitors, entering additional information (e.g., patient observations, pain, weight, tiredness) prompted from an interface operating on a mobile patient device, and / or taking prescribed medications, for example. Recommended actions communicated to a provider may include scheduling the patient for a follow-up visit and / or increasing the prescribed dosage of a medication (e.g., beta-blocker, diuretic). A recommended dosage increase may be based on test measurement values crossing predetermined thresholds (e.g., maximum levels / changes of NT-pro BNP) or based on relationships among multiple test measurements and a patient’s recorded history.

[0008] In some embodiments, data across multiple patients is analyzed to determine the performance of medical guidelines, protocols, adherence, and / or communication with patients during the course of heart disease care. A comparison may be made between outcomes for patients who followed certain guidelines / protocols and those who did not, forexample. In some embodiments, outcomes include mortality rates and readmission rates. The comparison may be represented in a graphical chart format.

[0009] Summarizing and without excluding further possible embodiments, the following embodiments may be envisaged:

[0010] Embodiment 1 : A system for managing heart disease, the system comprising: a patient management platform for managing heart disease in patients, the patient management platform comprising one or more processors; one or more mobile and remote patient monitoring device(s) programmed and configured to remotely obtain patient physiological attributes outside of a clinic or hospital setting, the monitoring device(s) configured to transmit the obtained physiological attributes through a networking interface to the patient management platform; wherein the patient management platform is programmed and configured to: transmit and receive patient data to and from one or more electronic patient data systems; receive remote patient data representing the physiological patient attributes obtained by the remote patient monitoring device(s); analyze the remotely obtained patient physiological attributes and patient data, wherein the analyzing comprises comparing the physiological attributes and patient data with at least one heart disease treatment protocol; in response to the analyzing, determine one or more recommended action(s) to be taken according to at least one heart disease treatment protocol; based on the recommended action(s), cause a generation and transmission of a notification to a patient or provider device connected with the patient management platform.

[0011] Embodiment 2: The system of embodiment 1, wherein the one or more electronic patient data systems comprise one or more of an electronic medical records (EMR) system, a laboratory information system (LIS), an electronic pharmacy management system, a picture archiving and communication system (PACS), a digital pathology (DP) system, and a radiology information system (RIS).

[0012] Embodiment 3: The system of any of embodiments 1-2, wherein the patient data received from the electronic patient data systems comprises a prescription of a beta blocker and test results pertaining to measuring or estimating a presence of NT -ProBNP beyond a predetermined threshold, wherein the recommended action(s) comprises at least one of increasing or maintaining the amount of administered beta blocker.

[0013] Embodiment 4: The system of any of embodiments 1-3, wherein the patient data received from the electronic patient data systems comprises test results or an absence of test results pertaining to measuring NT-ProBNP, and the recommended action(s) comprises recommending a test for measuring NT-ProBNP.

[0014] Embodiment 5: The system of any of embodiments 1-4, wherein analyzing the remotely obtained patient data comprises estimating a level of NT-ProBNP of the patient, and the recommended action(s) is based on the estimated level of NT-ProBNP.

[0015] Embodiment 6: The system of any of embodiments 1-5, wherein the one or more remote patient monitoring device(s) comprises one or more of a heart rate monitor, heart rhythm monitor, activity tracker, sleep monitor, blood pressure monitor, blood- oxygen level monitor, and / or mobile telecommunications device.

[0016] Embodiment 7: The system of any of embodiments 1-6, wherein the one or more mobile and remote patient monitoring device(s) are connected through an Internet of Things (loT) interface configured to communicate the remote patient data.

[0017] Embodiment 8: The system of any of embodiments 1-7, wherein, based on the recommended action(s), generating a treatment or care plan for the patient and causing the treatment or care plan to be displayed on the patient or provider device.

[0018] Embodiment 9: The system of any of embodiments 1-8, wherein the treatment or care plan comprises recommended and prioritized action(s) and wherein transmitting the treatment or care plan causes a higher of the prioritized action(s) to be respectively highlighted on the patient or provider device.

[0019] Embodiment 10: The system of any of embodiments 1-9, wherein the patient management platform is programmed and configured to: obtain patient outcome data relating to the recommended action(s) pertaining to multiple patients; determine a performance relationship between the outcome data and recommended action(s) pertaining to multiple patients; generate and cause a display of a report comprising the determined performance relationship.

[0020] Embodiment 11 : The system of embodiment 10, wherein the performance relationship comprises at least one of mortality or readmission rates of patients based on at least the recommended action(s) and / or the heart disease treatment protocol.

[0021] Embodiment 12: A computer-implemented method for managing heart disease, the method comprising: using one or more remote patient monitoring device(s) to obtain physiological attributes from a patient outside of a clinic or hospital setting, the monitoring device(s) configured to transmit the obtained physiological attributes through a networking interface to a patient management platform; receiving patient data from one or more electronic patient data systems; analyzing the remotely obtained patient physiological attributes and patient data, wherein the analyzing comprises comparing the physiological attributes and patient data with at least one heart disease treatment protocol; in response to the analyzing, determining one or more recommended action(s) to be taken according to at least one heart disease treatment protocol; based on the recommended action(s), causing a generation and transmission of a notification to a patient or provider device connected with the patient management platform.

[0022] Embodiment 13: The method of embodiment 12, wherein the one or more electronic patient data systems comprise one or more of an electronic medical records (EMR) system, a laboratory information system (LIS), an electronic pharmacy management system, a picture archiving and communication system (PACS), a digital pathology (DP) system, or a radiology information system (RIS).

[0023] Embodiment 14: The method of any of embodiments 12-13, wherein the patient data received from the one or more electronic patient data systems comprises a prescription of a beta blocker and test results pertaining to measuring or estimating an increase of NT- ProBNP beyond a predetermined threshold, wherein the recommended action(s) comprises at least one of increasing or maintaining the amount of administered beta blocker.

[0024] Embodiment 15: The method of any of embodiments 12-14, wherein the patient data received from the one or more electronic patient data systems comprises test results or an absence of test results pertaining to measuring NT -ProBNP, and the recommended action(s) comprises recommending a test for measuring NT-ProBNP.

[0025] Embodiment 16: The method of any of embodiments 12-15, wherein analyzing the remotely obtained patient data comprises estimating a level of NT-ProBNP of the patient, and the recommended action(s) is based on the estimated level of NT-ProBNP.

[0026] Embodiment 17: The method of any of embodiments 12-16, wherein the one or more remote patient monitoring device(s) comprises one or more of a heart rate monitor,heart rhythm monitor, activity tracker, blood pressure monitor, sleep monitor, blood- oxygen level monitor, and / or mobile telecommunications device.

[0027] Embodiment 18: The method of any of embodiments 12-17, wherein the one or more remote patient monitoring device(s) are connected through an Internet of Things (loT) interface configured to communicate the remote patient data.

[0028] Embodiment 19: The method of any of embodiments 12-18, wherein, based on the recommended action(s), generating a treatment or care plan for the patient and causing the treatment or care plan to be displayed on the patient or provider device.

[0029] Embodiment 20: The method of any of embodiments 12-19, wherein the treatment or care plan comprises recommended and prioritized action(s) and wherein transmitting the treatment or care plan causes a higher of the prioritized action(s) to be respectively highlighted on the display of the patient or provider device.

[0030] Embodiment 21 : The method of any of embodiments 12-20, further comprising: obtaining patient outcome data relating to the recommended action(s) pertaining to multiple patients; determining a performance relationship between the outcome data and recommended action(s) pertaining to multiple patients; causing a display of a report comprising the determined performance relationship.

[0031] Embodiment 22: The method of embodiment 21, wherein the performance relationship comprises at least one of mortality or readmission rates of patients based on at least the recommended action(s) and / or the heart disease treatment protocol.

[0032] Embodiment 23 : A remote patient monitoring and management system, the system comprising: a patient management platform for monitoring and managing patient clinical care, the patient management platform comprising one or more processors; one or more electronic patient data systems connected with the patient management platform; one or more mobile and remote patient monitoring device(s) programmed and configured to remotely obtain patient physiological attributes outside of a clinic or hospital setting, the monitoring device(s) configured to transmit the obtained physiological attributes through a networking interface to the patient management platform; wherein the patient management platform is programmed and configured to: transmit and receive patient data to and from the one or more electronic patient data systems; receive remote patient data representing the physiological patient attributes obtained by the remote patient monitoring device(s);analyze the remotely obtained patient physiological attributes and patient data, wherein the analyzing comprises comparing the physiological attributes and patient data with at least one clinical treatment protocol; in response to the analyzing, determine one or more recommended action(s) to be taken according to at least one clinical treatment protocol; based on the recommended action(s), generate a treatment or care plan for the patient and cause the generation and transmission of notifications to a patient or provider device connected with the patient management platform, the notifications including the treatment or care plan for the patient.

[0033] Embodiment 24: The system of embodiment 23, wherein the one or more electronic patient data systems comprise one or more of an electronic medical records (EMR) system, a laboratory information system (LIS), an electronic pharmacy management system, a picture archiving and communication system (PACS), a digital pathology (DP) system, or a radiology information system (RIS).

[0034] Embodiment 25: The system of any of embodiments 23-24, wherein the patient data received from the electronic patient data systems comprises an administration of a therapy and test results pertaining to measuring or estimating a presence of a biomarker, wherein the recommended action(s) comprises at least one of increasing or maintaining the amount of administered therapy.

[0035] Embodiment 26: The system of any of embodiments 23-25, wherein the patient data received from the electronic patient data systems comprises test results or an absence of test results pertaining to measuring the biomarker, and the recommended action(s) comprises recommending a test for detecting the biomarker.

[0036] Embodiment 27: The system of any of embodiments 23-26, wherein analyzing the remotely obtained patient data comprises estimating a presence of the biomarker based on the remotely obtained physiological attributes, and the recommended action(s) is based on the estimated presence of the biomarker.

[0037] Embodiment 28: The system of any of embodiments 23-27, wherein the one or more remote patient monitoring device(s) comprises one or more of a heart rate monitor, heart rhythm monitor, activity tracker, sleep monitor, blood pressure monitor, blood- oxygen level monitor, and / or mobile telecommunications device.

[0038] Embodiment 29: The system of any of embodiments 23-28, wherein the one or more mobile and remote patient monitoring device(s) are connected through an Internet of Things (loT) interface configured to communicate the remote patient data.

[0039] Embodiment 30: The system of any of embodiments 23-29, wherein the treatment or care plan comprises recommended and prioritized action(s) and wherein transmitting the treatment or care plan causes a higher of the prioritized action(s) to be respectively highlighted on the patient or provider device.

[0040] Embodiment 31 : The system of any of embodiments 23-30, wherein the patient management platform is programmed and configured to: obtain patient outcome data relating to the recommended action(s) pertaining to multiple patients; determine a performance relationship between the outcome data and recommended action(s) pertaining to multiple patients; generate and cause the display of a report comprising the determined performance relationship.

[0041] Embodiment 32: The system of embodiment 31, wherein the performance relationship comprises at least one of mortality or readmission rates of patients based on at least the recommended action(s) and / or the clinical treatment protocol.

[0042] Embodiment 33: The system of any of the previously listed embodiments referring to a system, further comprising one of a driver or a service that converts data from the one or more devices into a compatible form within the platform and / or a provider network.

[0043] Embodiment 34: The system of any of the previously listed embodiments referring to a system, wherein biomarkers can be estimated based on one or more of the physiological attributes that do not require a blood test and the physiological attributes can be monitored with the monitoring devices.

[0044] Embodiment 35: The system of any of the previously listed embodiments referring to a system, wherein the patient management platform is further configured to utilize machine learning to (i) correlate the patient physiological attributes with biomarkers obtained from other patients while being remotely monitored and then (ii) manage heart disease care for a patient of interest.

[0045] Embodiment 36: The system of any of the previously listed embodiments referring to a system, further comprising a large language model (LLM) based interfaceconfigured for patients and / or providers to enter information about a patient or submit queries regarding patient status or recommended treatment and / or diagnostic protocols.

[0046] Embodiment 37: The system of the previous embodiment, wherein the LLM is configured to translate or structure submitted information according to a predetermined schema, structure and / or terminology.

[0047] Embodiment 38: The system of any of embodiments 36-37, wherein the LLM is configured to generate answers or recommendations in response to questions and a review of current patient data, other patient historical data, and / or third-party sources.

[0048] Embodiment 39: The method of any of the previously listed embodiments referring to a method, further comprising using one of a driver or a service to convert data from the one or more devices into a compatible form within the platform and / or a provider network.

[0049] Embodiment 40: The method of any of the previously listed embodiments referring to a method, further comprising estimating biomarkers based on one or more of the physiological attributes that do not require a blood test and monitoring the physiological attributes with the monitoring devices.

[0050] Embodiment 41 : The method of any of the previously listed embodiments referring to a method, further comprising utilizing machine learning to (i) correlate the patient physiological attributes with biomarkers obtained from other patients while being remotely monitored and then (ii) manage heart disease care for a patient of interest.

[0051] Embodiment 42: The method of any of the previously listed embodiments referring to a method, further comprising providing a large language model (LLM) based interface configured for patients and / or providers to enter information about a patient or submit queries regarding patient status or recommended treatment and / or diagnostic protocols.

[0052] Embodiment 43: The method of the previously listed embodiment, wherein the LLM translates or structures submitted information according to a predetermined schema, structure and / or terminology.

[0053] Embodiment 44: The method referred to in any of embodiments 42-43, wherein the LLM generates answers or recommendations in response to questions and a review of current patient data, other patient historical data, and / or third-party sources.BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The above-mentioned aspects of exemplary embodiments will become more apparent and will be better understood by reference to the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0055] FIG. 1 is an illustrative diagram of a system for managing heart disease according to some embodiments;

[0056] FIGS. 2A and 2B illustrate user interfaces for summarizing the status of heart disease patients according to some embodiments;

[0057] FIG. 3 illustrates a user interface highlighting particular patient attributes and system notifications according to some embodiments;

[0058] FIG. 4A illustrates a user interface displaying notifications of events or recommended actions according to some embodiments;

[0059] FIG. 4B illustrates a user interface displaying a patient care plan according to some embodiments;

[0060] FIG. 4C illustrates a user interface for prompting patient information according to some embodiments;

[0061] FIG. 4D illustrates a user interface for summarizing a patient history and recommended actions according to some embodiments;

[0062] FIG. 5 illustrates a user interface for summarizing patient information according to some embodiments;

[0063] FIG. 6 illustrates a user interface for summarizing a patient history and recommended actions according to some embodiments;

[0064] FIGS. 7A and 7B illustrate user interfaces interface for displaying performance metrics of the system according to some embodiments;

[0065] FIG. 8 is a process flow for remotely managing a heart disease patient according to some embodiments;

[0066] FIG. 9 is a process flow for remotely managing a heart disease patient utilizing a protocol according to some embodiments; and

[0067] FIG. 10 illustrates an example computer system that may be utilized to implement techniques disclosed herein.DESCRIPTION

[0068] The embodiments described below are not intended to be exhaustive or to limit the invention to the precise forms disclosed in the following detailed description. Rather, the embodiments are chosen and described so that others skilled in the art may appreciate and understand the principles and practices of this disclosure.

[0069] Disclosed herein are systems and methods for heart disease management utilizing remote patient monitoring. A patient management platform is configured to monitor and manage heart disease in patients, the patient management platform having one or more mobile and remote patient monitoring device(s) programmed and configured to remotely obtain patient physiological attributes outside of a clinic or hospital setting, the monitoring device(s) configured to transmit the obtained physiological attributes through a networking interface to the patient management platform.

[0070] The patient management platform is programmed and configured to transmit and receive patient data to and from one or more electronic patient data systems, receive remote patient data representing the physiological patient attributes obtained by the remote patient monitoring device(s), consolidate and analyze the remotely obtained patient data, wherein the analyzing includes comparing the remotely obtained patient data with patient data received through electronic patient data systems and with at least one heart disease treatment protocol. Electronic patient data systems may include, for example, an electronic medical records (EMR) system, a laboratory information system (LIS), an electronic pharmacy management system, a picture archiving and communication system (PACS), a digital pathology (DP) system, or a radiology information system (RIS).

[0071] In response to analyzing the remotely received data and data received through the electronic patient data systems, determining one or more recommended action(s) to be taken according to at least one heart disease treatment protocol. Based on the recommended action(s), causing the generation and transmission of a notification to one or more applications operating within a healthcare provider network or remote patient setting.

[0072] FIG. 1 is an illustrative diagram of a system for managing heart disease according to some embodiments. The system includes a heart disease management platform 100 that is programmed and configured to manage software applications, remotedevices, user interfaces, and analytics that operate on and / or with a remote patient setting 150 outside of a clinic or hospital setting (e.g., patient home, workplace) and a healthcare provider network 145 (e.g., hospitals / clinics).

[0073] The remote patient setting 150 is configured with patient monitoring devices 160, 165, 170, and 175 that can measure physiological attributes of a patient pertaining to heart disease. These devices may include, for example, a heart rate monitor 160 (e.g., a watch), a blood pressure device 165, a blood-oxygen reader 170, and a mobile communications device 175 (e.g., a cellular phone, tablet, laptop) to monitor and collect physiological data about a patient. Mobile communications device 175 may be used by a patient / user to receive healthcare information, communicate with a provider (e.g., within provider network 145), enter information about the patient, and configure the other devices within the remote patient setting 150. The remote patient setting may include a patient’s home, workplace, or anywhere a patient may move with the devices.

[0074] In some embodiments, particular remote devices 177 can be selectively added or removed for remote patient monitoring use, such as for particular patients and / or protocols. A user / provider of the system may add a particular device to monitor a patient, for example, such as to monitor their heart rate variability, weight, and / or blood pressure, for example. In some embodiments, a user / provider or system may add notifications (e.g., a text message or pop-up generated from an application) or reminders for the patient to wear / use the particular device such as at particular times (e.g., at bedtime) or if active use is not detected. In some embodiments, a driver or service is provided / programmed that converts data from the devices into a structured / compatible form within platform 100 and / or provider network 145 (e.g., Fast Healthcare Interoperability Resources (FHIR)).

[0075] Remote user interfaces or “apps” 180 are configured to be operated by remote users such as by providers within healthcare provider network 145, patients, and / or other users. These interfaces are configured to, for example, enable providers to review patient history / data, communicate with patients, and manage care, such as illustrated in FIGS. 2A- 2B, 3, and 5-7. In some embodiments, the interfaces are generated by applications available from the platform 100 and may be installed on devices at remote locations (e.g., within healthcare provider network 145 or remote patient setting 150) and / or by utilizing a remote interface platform such as a web browser. In some embodiments,alerts / notifications may be generated to provide information about healthcare actions or treatments needing imminent attention and / or in response to detecting likely adverse conditions or data surrounding a patient.

[0076] Remote user interfaces and / or apps 180 include those installed on patient devices (e.g., a mobile device 175). These interfaces or apps are configured to, for example, enable patients to review treatment plans, enter data, and communicate with providers, such as illustrated in FIGS. 4A-4C and 6. Reminders, recommendations, and / or alerts may be generated including those for patients and / or providers regarding administering or changing medications and / or dosages, engaging in testing, monitoring, and follow-up provider visits, for example, as further described herein.

[0077] The system operates through and over a wide area network 140 such as the internet and may utilize cloud computing services 120 for processing large amounts of patient data. Platform 100 includes communications services 105 that facilitate communications between the platform 100, devices operating in the remote patient setting 150, and providers within healthcare provider network 145. These services may include networking protocols, application programming interface(s) (APIs), and messaging services, for example.

[0078] In some embodiments, standardized protocols are used for communication with healthcare data systems (e.g., Fast Healthcare Interoperability Resources (FHIR)), including electronic medical record (EMR) databases, laboratory information systems (LIS), and / or other electronic patient data systems. Healthcare provider network 145 is connected to EMR 135 via provider network 145 and contains medical records for patients managed with heart disease management platform 100. Protocols such as FHIR may be used to communicate medical records, including imaging data, pathology reports, prescription data, laboratory tests, etc.

[0079] A mobile device management service 125 is configured to remotely configure remote devices 160, 165, 170, and 175 to collect heart disease related physiological information about a patient and store that information for use with the platform 100. Physiological information about a patient may include, for example, heart rate, blood pressure, and blood-oxygen levels used to assess patient conditions and guide heart disease care such as further described herein. The devices may communicate using a cellular orother internet / network connection (e.g., wireless network). Utilizing Internet of Things (loT) protocols such as loT HTTP protocol 152 and MQTT protocol 154 can widen an overall communications network by connecting the loT devices directly with each other (e.g., Bluetooth, Li-Fi) and thereby forming a mesh subnetwork between each other.

[0080] In some embodiments, medical data is obtained from patients remotely utilizing questionnaire feedback (e.g., observations of pain, breathing difficulty, tiredness, weight change, mental acuity, emotion, etc ). Questionnaires can be integrated / updated into the system from third-party sources (e.g., Kansas City Cardiomyopathy Questionnaire (KCCQ)-12) or manually entered / edited by users within a provider network. In some embodiments, the questionnaire is administered utilizing a messaging platform (e.g., SMS on a mobile telephone) or automated calling system and feedback may be returned by input through a dial-pad and / or microphone, for example.

[0081] Medical data processing services 130 are programmed to receive or transmit medical data from or to electronic patient data systems (e.g., EMR 135, LIS) using a form compatible with the respective system or platform (e.g., using FHIR). Once received by the platform 100, the data is integrated in a form compatible for further processing and / or storage within the system (e.g., by analytics services 110). An (application programming interface) API may be configured to permit the heart disease management system to interface with the external or third party tools. In some embodiments, data from a patient is anonymized prior to transmission to a third party analytical tool.

[0082] Analytics services 110 utilize data received from devices within the remote patient setting 150 and EMR 135 to guide clinical decisions / notifications (e.g., as illustrated in FIGS. 2A-2B, 3, and 5-6) and provide useful analytical information such as regarding the performance of certain protocols (e.g., as illustrated in FIGS. 7A-7B). For example, analytics services 110 may analyze patient data and / or a guideline / protocol to determine that a change in medication, dosage, treatment type, and / or test is recommended for a patient based on the analysis. Performance evaluations may analyze data from multiple patients to determine the effectiveness of a particular protocol, for example.

[0083] FIGS. 2 A and 2B illustrate a user interface for summarizing the status of heart disease patients according to some embodiments. The interface of FIGS. 2A-2B is configured to present data to a healthcare provider such as on a computer terminalconnected within healthcare provider network 145. A column 200 represents a list of heart disease patients along with patient identifying information (e.g., name, birthdate, patient ID). In some embodiments, a heart disease protocol under which each patient is enrolled is identified (e.g., Congestive Heart Failure (CHF) protocol as provided by the American College of Cardiology and / or a protocol defined / managed by a provider user in the system).

[0084] A column 205 lists remote monitoring devices (e.g., devices 160, 165, and 170 of FIG. 1) active for respective patients along with status information about the devices (e.g., connection status, battery level). Columns 210, 215, 220, and 225 list most recent measurements provided by the devices and / or other sources (e.g., weight, blood pressure, heart rate, oxygenation).

[0085] Columns 240, 245, 250, and 260 display additional medical information / data regarding heart disease care for the patient. Column 240 represents medical questionnaires completed by the patient as part of the patient’s care plan or protocol. Column 245 represents laboratory measurements including, for example, levels of NT -proBNP, eGFR, and potassium levels. Column 250 lists current medications and dosages prescribed (e.g., beta blocker, diuretic) to respective patients and column 260 lists individual assigned healthcare providers and their clinical role / expertise.

[0086] As further described herein, notifications or alerts are generated based on identifying that measured patient attributes are out of a predetermined range or threshold and may include recommendations for certain actions based on the measured attributes (e.g., laboratory measurements), other patient medical history / data, and / or protocol(s) / guideline(s). At 230, the interface identifies that there are active notifications, which may be further reviewed in detail by selecting the identification of active alerts. In some embodiments, alerts or notifications are more specifically identified within respective columns of patient data (e.g., within column 250 identifying a recommended medication change or column 240 identifying that a questionnaire requires completion).

[0087] FIG. 3 illustrates a user interface 300 highlighting particular patient attributes and system notifications according to some embodiments. Platform 100 is configured to analyze patient data such as that received from remote devices 160, 165, 170, and 175 and EMR 135 (or other sources) and compare such data to ranges within clinical protocols orguidelines (e.g., using medical data processing services 130). Analysis that represents a patient should receive attention (e.g., risk of adverse event increases, change in medication / dosage recommended, follow-up visit recommended) according to a guideline causes related measurements within interface to be highlighted for a provider. For example, an increase in weight above a certain guideline threshold is highlighted at 310 and a notification at 315 provides additional information about the event. An increased blood pressure is highlighted at 320 and a notification indicating the patient’s blood pressure had been lower is generated at 325.

[0088] FIGS. 4A-4C illustrate user interfaces for patients such as within the remote patient setting 150 of FIG. 1. They may be operated using a computing device and a web browser and / or installed application provided by platform 100. FIG. 4A illustrates a user interface displaying notifications of events or recommended actions according to some embodiments. Action items or alerts identified for a patient are shown at 410, and may be associated with a protocol managed by the heart disease management platform (e.g., platform 100). For example, a reminder to take a particular medication and obtain a blood pressure reading (e.g., from remotely monitored blood pressure device 165) is shown. A list of remotely monitored devices is shown along with status information about the devices.

[0089] FIG. 4B illustrates a user interface displaying a patient care plan according to some embodiments. At 420, a week-to-week schedule is shown outlining general activities for the care plan of a patient. These activities may include scheduled doctor visits, laboratory tests, and medication changes that will occur under a current plan. In some embodiments, a user can select a particular week and, in response, the interface will provide greater and more specific detail about events for the selected week (e.g., dates, times, and locations of doctor visits or laboratory tests).

[0090] FIG. 4C illustrates a user interface for prompting patient information according to some embodiments. At 430, inputs are provided to enter information about the status of a patient. For example, information about how a patient feels (e.g., pain levels, breathing comfort) are obtained and can be stored as part of a patient’s medical record and / or within the system and may be used in connection with protocols or guidelines such as further described herein.

[0091] FIG. 5 illustrates a user interface for summarizing patient information according to some embodiments. At 500, a column of categories of patient medical information is listed with the information followed at 510 over a time period 505. The categories of patient information pertain to heart disease and what could be useful to a clinician to assess changes over time. For example, changes in certain physiological attributes (e.g., presence of NT -proBNP and blood pressure) can be useful to gauge the impact of medication and progress or decline of a patient’s heart failure and / or risk of heart attack or stroke. In some embodiments, the data is analyzed (e.g., utilizing analytics services 110) by the system to alert a provider of an elevated risk of an adverse outcome and / or for recommending certain clinical decisions. A notification (not shown) may be provided within the interface to highlight the risk or recommendation with the particular data shown that relates to the notification.

[0092] In some embodiments, certain biomarkers (e.g., of NT-proBNP) may be estimated based on physiological measurements / attributes that do not require a blood test (e.g., heart rate / rhythm / variability trends / sleep trends / other activity trends) and can be monitored utilizing the remote monitoring devices described herein. In some embodiments, machine learning is utilized to correlate these physiological measurements with such biomarkers (e.g., such as when they were measured using blood tests with other patients while being remotely monitored), and then utilized to manage heart disease care for a patient.

[0093] FIG. 6 illustrates a user interface 600 for summarizing a patient history and recommended actions according to some embodiments. Interface 600 illustrates a summary history of a patient’s heart disease care and recommendations 610 for a patient, such as based on a protocol / guideline, during a particular (e.g., current) week in the care plan. The recommended care plan includes the current treatment regimen (e.g., administering medication(s) and dosages) and suggested therapie(s) going forward including whether to cease, maintain, or change the administered therapy (e.g., medication(s) and dosage) during the respective week.

[0094] In some embodiments, interface 600 is configured to operate on a provider’s device (e.g., a hand-held mobile device such as a mobile telephone) so that a provider (e.g., within provider network 145) can readily review a patient’s current treatment plan andrecommendations. At 630, a user may also quickly navigate to reviewing a patient’s laboratory results or other important information about a patient’s heart disease care (e.g., by sliding or swiping between screens).

[0095] FIGS. 7A and 7B illustrate user interfaces for displaying performance metrics of the system according to some embodiments. Tabs 700 in the interface provide an option for displaying historical performance of treatment protocols, such as those implemented with the systems described herein (e.g., FIG. 1). A particular treatment protocol / guideline may be selected or reviewed for further detail by receiving user input at 710. A performance relationship between outcome data and protocols or recommended actions for multiple patients is determined. Historical performance and outcome data may include readmission rate, mortality rate, survival period, and other recorded clinical history. An option for review of the timeline of historical performance may be selected at 740. At 720, recorded history may be selected reviewing records of patient and / or provider adherence to a protocol, for example. Other displays from tabs 700 may include revenue data and analysis based on the recorded use of the system and / or protocols. In some embodiments, analysis of use and effectiveness of user engagement with the patient and / or provider interfaces (e.g., FIGS. 2-6) is displayed. At 730, separate protocols in which a patient is enrolled may be selected for display of their respective performance metrics.

[0096] FIG. 8 is a process flow for remotely managing a heart disease patient according to some embodiments. At block 800 a heart disease patient is enrolled in a heart disease care protocol through a system in accordance with embodiments herein (e.g., system of FIG. 1). The patient may have had a heart failure event or been diagnosed with heart disease (e g., heart attack, atrial fibrillation, stroke, hypertension). The protocol may be a standardized heart disease treatment protocol or guideline for the treatment of heart failure, including heart attack or congestive heart failure, such as published by the American College of Cardiology. In some embodiments, a protocol can be added, modified, and / or configured by users within a provider network (e.g., provider network 145).

[0097] The protocol or guideline may include recommendations for medication type, dosage, testing, surgery, and / or readmission based on the monitored status and / or medical history of a patient. For example, the type and dosage of a beta blocker or diuretic may berecommended based on measured levels of NT -ProBNP, blood pressure, and / or other indicators of heart failure decompensation and / or risk of heart attack or stroke.

[0098] At block 810, the patient is assigned with one or more remote monitoring devices (e.g., remote devices 160, 165, 170, and 175). The devices are configured to obtain data about the patient such as, for example, blood pressure, heart rate, heart rhythm, blood oxygenation, and / or other heart disease related indicators while the patient is in a remote patient setting (e.g., home, work). The devices may be configured to operate through wireless networking systems (e.g., connected with the internet) within a remote patient setting (e.g., home, work).

[0099] At block 820, a patient is discharged from a hospital or clinic after a heart disease event (e.g., heart attack or diagnosis of heart disease). The patient is prescribed medication(s) (e.g., beta blocker(s) and / or diuretic(s)) and provided instructions for working with the heart disease management system (e.g., user interfaces, remote devices ) for monitoring their condition and adhering to a care protocol. Based on analyzing the patient data (from the remote devices and / or patient records), biomarkers are measured (where possible) to determine if changes in medication (beta blocker, diuretic) or dosage may be needed according to a protocol or guideline.

[0100] At block 830, patient data is received by the heart disease management system (e g., at platform 100 of FIG. 1) such as through the remote monitoring devices and / or other patient data sources (e.g., EMR database 135). In some embodiments, patient data is provided by a patient or caregiver with the assistance or prompting by a computer interface (e.g., as illustrated in FIGS. 4A-4C). The obtained patient data is analyzed by the system (e.g., analytics services 110) such as by comparing (processed) data to guidelines / protocols and / or predetermined thresholds indicating a (new) diagnosis and / or determining certain clinical actions are recommended (e.g., a change in medication or dosage based on measured indicators such as blood pressure and / or level of NT-ProBNP).

[0101] At block 840, based on the results of the analysis at block 830, the determined diagnosis or recommendations are communicated to a provider and / or patient (e.g., as illustrated in FIGS. 2-6). In some embodiments, alerts, reminders, and / or other indicators are generated based on determining that attention or action(s) should be given heightenedor higher priority based on the analysis and / or protocol (e.g., by highlighting certain patient data and / or recommendations as illustrated in FIGS. 2B and 3).

[0102] At block 850, analytical components of the system (e.g., analytics services 110) are (re)configured based on performance feedback or measures of system operation (e.g., patient outcomes, patient / provider feedback). The (re)configuring may include applying machine learning techniques to data obtained by the system (e.g., collected patient records / hi story). A machine learning system may include a neural network, for example, designed to identify optimal treatment or clinical practices for improving patient outcomes (e.g., survival and / or readmission rates). The machine learning models or analysis may utilize external or third party tools or data such as from other patients or systems (e.g., clinical data from clinical trials / hospitals) and the models or results may evolve / change over time as more or different data becomes available.

[0103] In some embodiments, optimal methods for obtaining patient data or improving patient adherence to protocols are identified (e.g., identifying optimal remote monitoring devices, techniques, or interfaces for obtaining patient data). In some embodiments, performance data is stored and made available for presentation / review by users (e.g., as illustrated in FIGS. 7A-7B). In some embodiments, analysis of patient data is used to identify an optimal protocol or guideline for a patient and identified as such to a provider.

[0104] In some embodiments, a large language model (LLM) based interface (e.g., chatbot) is provided for patients and / or providers to enter information about a patient or submit queries regarding patient status or recommended treatment / diagnostic protocols. The model may be configured to translate / structure submitted information or query about a patient according to a predetermined schema, structure, and / or terminology. The model may be configured / trained to generate answers or recommendations in response to questions and a review of current patient data, other patient historical data, and / or third- party sources (e.g., protocols, literature, clinical studies). The model may be continuously trained based on additional / updated information regarding treatment outcomes and / or provider feedback.

[0105] FIG. 9 is a process flow for remotely managing a heart disease patient utilizing a protocol according to some embodiments. At block 910, a heart disease patient has been enrolled in remote heart disease care management protocol with a system in accordancewith embodiments herein (e.g., as illustrated in FIG. 1). As represented at 900, as part of remotely monitored and managed care, the patient is monitored utilizing remote monitoring devices (e.g., devices 160, 165, 170, and 175).

[0106] While data from the devices and / or medical records of the patient are monitored and analyzed, a determination is made at block 920 as to whether particular (additional) biomarkers should be monitored for the patient. For example, if a certain biomarker is not being monitored, analysis of patient data (e.g., blood pressure, weight, heart arrhythmia indicators) is used to trigger a recommendation to a provider / user at block 925 that additional testing or biomarkers should be measured (e.g., testing levels of NT -ProBNP). In some embodiments, the provider is alerted to measurements / biomarkers identified based on patient data (including combinations of measured indicators) exceeding particular thresholds (e.g., as illustrated in FIG. 3).

[0107] At block 930, a determination is made as to whether a particular biomarker is detected and / or exceeds a predetermined threshold (e.g., a maximum increase of a measured level of NT -ProBNP over time). At block 935, in response to determining that the threshold is exceeded, a recommendation is generated indicating that a titration of a medication should be paused while the patient / biomarker is further monitored. The recommendation may be automatically transmitted / alerted to a provider / prescriber / caregiver and to the patient through an interface such as further described herein (e.g., as illustrated in FIG. 6). If analysis of patient data indicates that a planned titration should proceed, the patient / provider is notified accordingly.

[0108] At block 915, a remotely monitored patient is not presently managed under a particular heart disease care protocol and / or there is an absence of test results identified as important to a patient’s care (e.g., NT -ProBNP, potassium). Based on analyzed patient data, it is determined whether the patient should be so enrolled / managed / monitored / tested. For example, based on measured levels of NT-ProBNP, atrial fibrillation, and / or blood pressure, for example, a recommendation is generated indicating that the patient should be enrolled / managed under a particular protocol (e.g., including being prescribed medication type and assigned additional monitoring for changes in patient data).

[0109] FIG. 10 illustrates an example computer system that may be utilized to implement techniques disclosed herein. Any of the computer systems mentioned herein,such as for hosting the systems and implementing the processes described for managing heart disease, may utilize any suitable number of subsystems. Examples of such subsystems are shown in FIG. 10 in computer system 10. In some embodiments, a computer system includes a single computer apparatus, where the subsystems can be the components of the computer apparatus. In other embodiments, a computer system can include multiple computer apparatuses, each being a subsystem, with internal components. A computer system can include desktop and laptop computers, tablets, mobile phones, telecommunication devices or other mobile devices. In some embodiments, a cloud infrastructure (e.g., Amazon Web Services), a graphical processing unit (GPU), etc., can be used to implement the disclosed techniques.

[0110] The subsystems shown in FIG. 10 are interconnected via a system bus 75. Additional subsystems such as a printer 74, keyboard 78, storage device(s) 79, monitor 76, which is coupled to display adapter 82, and others are shown. Peripherals and input / output (VO) devices, which couple to I / O controller 71, can be connected to the computer system by any number of means known in the art such as input / output (I / O) port 77 (e.g., USB, FireWire®). For example, I / O port 77 or external interface 81 (e.g., Ethernet, Wi-Fi, etc.) can be used to connect computer system 10 to a wide area network such as the Internet, a mouse input device, or a scanner. The interconnection via system bus 75 allows the central processor 73 to communicate with each subsystem and to control the execution of a plurality of instructions from system memory 72 or the storage device(s) 79 (e.g., a fixed disk, such as a hard drive, or optical disk), as well as the exchange of information between subsystems. The system memory 72 and / or the storage device(s) 79 may embody a computer readable medium. Another subsystem is a data collection device 85, such as a camera, microphone, accelerometer, and the like. Any of the data mentioned herein can be output from one component to another component and can be output to the user.

[0111] A computer system can include a plurality of the same components or subsystems, e.g., connected together by external interface 81 or by an internal interface. In some embodiments, computer systems, subsystem, or apparatuses can communicate over a network. In such instances, one computer can be considered a client and another computer a server, where each can be part of a same computer system. A client and a server can each include multiple systems, subsystems, or components.

[0112] Aspects of embodiments can be implemented in the form of control logic using hardware (e.g., an application specific integrated circuit or field programmable gate array) and / or using computer software with a generally programmable processor in a modular or integrated manner. As used herein, a processor includes a single-core processor, multi-core processor on a same integrated chip, or multiple processing units on a single circuit board or networked. Based on the disclosure and teachings provided herein, a person of ordinary skill in the art will know and appreciate other ways and / or methods to implement embodiments of the present disclosure using hardware and a combination of hardware and software.

[0113] Machine learning models utilized herein may include one or more of a Naive Bayes (NB) model, a logistic regression (LR) model, a random forest (RF) model, a support vector machine (SVM) model, an artificial neural network model, a multilayer perceptron (MLP) model, a convolutional neural network (CNN), a Large Language model (LLM), and / or other machine learning or deep leaning models, etc. The machine learning models can be updated / trained using a supervised learning technique, an unsupervised learning technique, etc.

[0114] Any of the software components or functions described in this application may be implemented as software code to be executed by a processor using any suitable computer language such as, for example, Java, C, C++, C#, Objective-C, Swift, or scripting language such as Perl or Python using, for example, conventional or object- oriented techniques. The software code may be stored as a series of instructions or commands on a computer readable medium for storage and / or transmission. A suitable non-transitory computer readable medium can include random access memory (RAM), a read only memory (ROM), a magnetic medium such as a hard-drive or a floppy disk, or an optical medium such as a compact disk (CD) or DVD (digital versatile disk), flash memory, and the like. The computer readable medium may be any combination of such storage or transmission devices.

[0115] Such programs may also be encoded and transmitted using carrier signals adapted for transmission via wired, optical, and / or wireless networks conforming to a variety of protocols, including the Internet. As such, a computer readable medium may be created using a data signal encoded with such programs. Computer readable mediaencoded with the program code may be packaged with a compatible device or provided separately from other devices (e.g., via Internet download). Any such computer readable medium may reside on or within a single computer product (e.g., a hard drive, a CD, or an entire computer system), and may be present on or within different computer products within a system or network. A computer system may include a monitor, printer, or other suitable display for providing any of the results mentioned herein to a user.

[0116] Any of the methods described herein may be totally or partially performed with a computer system including one or more processors, which can be configured to perform the steps. Thus, embodiments can be directed to computer systems configured to perform the steps of any of the methods described herein, potentially with different components performing a respective steps or a respective group of steps. Although presented as numbered steps, steps of methods herein can be performed at a same time or in a different order. Additionally, portions of these steps may be used with portions of other steps from other methods. Also, all or portions of a step may be optional. Additionally, any of the steps of any of the methods can be performed with modules, units, circuits, or other means for performing these steps.

[0117] The specific details of particular embodiments may be combined in any suitable manner without departing from the spirit and scope of embodiments of the disclosure. However, other embodiments of the disclosure may be directed to specific embodiments relating to each individual aspect, or specific combinations of these individual aspects.

[0118] The above description of example embodiments of the disclosure has been presented for the purposes of illustration and description. It is not intended to be exhaustive or to limit the disclosure to the precise form described, and many modifications and variations are possible in light of the teaching above.

[0119] A recitation of “a,” “an” or “the” is intended to mean “one or more” unless specifically indicated to the contrary. The use of “or” is intended to mean an “inclusive or,” and not an “exclusive or” unless specifically indicated to the contrary. Reference to a “first” component does not necessarily require that a second component be provided. Moreover reference to a “first” or a “second” component does not limit the referenced component to a particular location unless expressly stated.

[0120] All patents, patent applications, publications, and descriptions mentioned herein are incorporated by reference in their entirety for all purposes. None is admitted to be prior art.

[0121] While exemplary embodiments have been disclosed hereinabove, the present invention is not limited to the disclosed embodiments. Instead, this application is intended to cover any variations, uses, or adaptations of this disclosure using its general principles. Further, this application is intended to cover such departures from the present disclosure as come within known or customary practice in the art to which this invention pertains and which fall within the limits of the appended claims.

Claims

WHAT IS CLAIMED IS:

1. A system for managing heart disease, the system comprising: a patient management platform for managing heart disease in patients, the patient management platform comprising one or more processors; and one or more mobile and remote patient monitoring device(s) programmed and configured to remotely obtain patient physiological attributes outside of a clinic or hospital setting, the monitoring device(s) configured to transmit the obtained physiological attributes through a networking interface to the patient management platform; wherein the patient management platform is programmed and configured to: transmit and receive patient data to and from one or more electronic patient data systems; receive remote patient data representing the physiological patient attributes obtained by the remote patient monitoring device(s); analyze the remotely obtained patient physiological attributes and patient data, wherein the analyzing comprises comparing the physiological attributes and patient data with at least one heart disease treatment protocol; in response to the analyzing, determine one or more recommended action(s) to be taken according to at least one heart disease treatment protocol; and based on the recommended action(s), cause a generation and transmission of a notification to a patient or provider device connected with the patient management platform.

2. The system of claim 1, wherein the one or more electronic patient data systems comprise one or more of an electronic medical records (EMR) system, a laboratory information system (LIS), an electronic pharmacy management system, a picture archiving and communication system (PACS), a digital pathology (DP) system, and a radiology information system (RIS).

3. The system of any of claims 1-2, wherein the patient data received from the electronic patient data systems comprises a prescription of a beta blocker and test resultspertaining to measuring or estimating a presence of NT-ProBNP beyond a predetermined threshold, wherein the recommended action(s) comprises at least one of increasing or maintaining the amount of administered beta blocker.

4. The system of any of claims 1-3, wherein the patient data received from the electronic patient data systems comprises test results or an absence of test results pertaining to measuring NT-ProBNP, and the recommended action(s) comprises recommending a test for measuring NT-ProBNP.

5. The system of any of claims 1-4, wherein analyzing the remotely obtained patient data comprises estimating a level of NT-ProBNP of the patient, and the recommended action(s) is based on the estimated level of NT-ProBNP.

6. The system of any of claims 1-5, wherein the one or more remote patient monitoring device(s) comprises one or more of a heart rate monitor, heart rhythm monitor, activity tracker, sleep monitor, blood pressure monitor, blood-oxygen level monitor, and / or mobile telecommunications device.

7. The system of any of claims 1-6, wherein the one or more mobile and remote patient monitoring device(s) are connected through an Internet of Things (loT) interface configured to communicate the remote patient data.

8. The system of any of claims 1-7, wherein, based on the recommended action(s), generating a treatment or care plan for the patient and causing the treatment or care plan to be displayed on the patient or provider device.

9. The system of any of claims 1-8, wherein the treatment or care plan comprises recommended and prioritized action(s) and wherein transmitting the treatment or care plan causes a higher of the prioritized action(s) to be respectively highlighted on the patient or provider device.

10. The system of any of claims 1-9, wherein the patient management platform is programmed and configured to: obtain patient outcome data relating to the recommended action(s) pertaining to multiple patients; determine a performance relationship between the outcome data and recommended action(s) pertaining to multiple patients; and generate and cause a display of a report comprising the determined performance relationship.

11. The system of claim 10, wherein the performance relationship comprises at least one of mortality or readmission rates of patients based on at least the recommended action(s) and / or the heart disease treatment protocol.

12. The system of any of claims 1-11, further comprising one of a driver or a service that converts data from the one or more devices into a compatible form within the platform and / or a provider network.

13. The system of any of claims 1-12, wherein biomarkers can be estimated based on one or more of the physiological attributes that do not require a blood test and the physiological attributes can be monitored with the monitoring devices.

14. The system of any of claims 1-13, wherein the patient management platform is further configured to utilize machine learning to (i) correlate the patient physiological attributes with biomarkers obtained from other patients while being remotely monitored and then (ii) manage heart disease care for a patient of interest.

15. The system of any of claims 1-14, further comprising a large language model (LLM) based interface configured for patients and / or providers to enter information about a patient or submit queries regarding patient status or recommended treatment and / or diagnostic protocols.

16. The system of claim 15, wherein the LLM is configured to translate or structure submitted information according to a predetermined schema, structure and / or terminology.

17. The system of any of claims 15-16, wherein the LLM is configured to generate answers or recommendations in response to questions and a review of current patient data, other patient historical data, and / or third-party sources.

18. A computer-implemented method for managing heart disease, the method comprising: using one or more remote patient monitoring device(s) to obtain physiological attributes from a patient outside of a clinic or hospital setting, the monitoring device(s) configured to transmit the obtained physiological attributes through a networking interface to a patient management platform; receiving patient data from one or more electronic patient data systems; analyzing the remotely obtained patient physiological attributes and patient data, wherein the analyzing comprises comparing the physiological attributes and patient data with at least one heart disease treatment protocol; in response to the analyzing, determining one or more recommended action(s) to be taken according to at least one heart disease treatment protocol; and based on the recommended action(s), causing a generation and transmission of a notification to a patient or provider device connected with the patient management platform.

19. The method of claim 18, wherein the one or more electronic patient data systems comprise one or more of an electronic medical records (EMR) system, a laboratory information system (LIS), an electronic pharmacy management system, a picture archiving and communication system (PACS), a digital pathology (DP) system, or a radiology information system (RIS).

20. The method of any of claims 18-19, wherein the patient data received from the one or more electronic patient data systems comprises a prescription of a beta blocker and testresults pertaining to measuring or estimating an increase of NT-ProBNP beyond a predetermined threshold, wherein the recommended action(s) comprises at least one of increasing or maintaining the amount of administered beta blocker.

21. The method of any of claims 18-20, wherein the patient data received from the one or more electronic patient data systems comprises test results or an absence of test results pertaining to measuring NT-ProBNP, and the recommended action(s) comprises recommending a test for measuring NT-ProBNP.

22. The method of any of claims 18-21, wherein analyzing the remotely obtained patient data comprises estimating a level of NT-ProBNP of the patient, and the recommended action(s) is based on the estimated level of NT-ProBNP.

23. The method of any of claims 18-22, wherein the one or more remote patient monitoring device(s) comprises one or more of a heart rate monitor, heart rhythm monitor, activity tracker, blood pressure monitor, sleep monitor, blood-oxygen level monitor, and / or mobile telecommunications device.

24. The method of any of claims 18-23, wherein the one or more remote patient monitoring device(s) are connected through an Internet of Things (loT) interface configured to communicate the remote patient data.

25. The method of any of claims 18-24, wherein, based on the recommended action(s), generating a treatment or care plan for the patient and causing the treatment or care plan to be displayed on the patient or provider device.

26. The method of any of claims 18-25, wherein the treatment or care plan comprises recommended and prioritized action(s) and wherein transmitting the treatment or care plan causes a higher of the prioritized action(s) to be respectively highlighted on the display of the patient or provider device.

27. The method of any of claims 18-26, further comprising: obtaining patient outcome data relating to the recommended action(s) pertaining to multiple patients; determining a performance relationship between the outcome data and recommended action(s) pertaining to multiple patients; and causing a display of a report comprising the determined performance relationship.

28. The method of claim 27, wherein the performance relationship comprises at least one of mortality or readmission rates of patients based on at least the recommended action(s) and / or the heart disease treatment protocol.

29. The method of any of claims 18-28, further comprising using one of a driver or a service to convert data from the one or more devices into a compatible form within the platform and / or a provider network.

30. The method of any of claims 18-29, further comprising estimating biomarkers based on one or more of the physiological attributes that do not require a blood test and monitoring the physiological attributes with the monitoring devices.

31. The method of any of claims 18-30, further comprising utilizing machine learning to (i) correlate the patient physiological attributes with biomarkers obtained from other patients while being remotely monitored and then (ii) manage heart disease care for a patient of interest.

32. The method of any of claims 18-31, further comprising providing a large language model (LLM) based interface configured for patients and / or providers to enter information about a patient or submit queries regarding patient status or recommended treatment and / or diagnostic protocols.

33. The method of claim 32, wherein the LLM translates or structures submitted information according to a predetermined schema, structure and / or terminology.

34. The method of any of claims 32-33, wherein the LLM generates answers or recommendations in response to questions and a review of current patient data, other patient historical data, and / or third-party sources.

35. A remote patient monitoring and management system, the system comprising: a patient management platform for monitoring and managing patient clinical care, the patient management platform comprising one or more processors; one or more electronic patient data systems connected with the patient management platform; and one or more mobile and remote patient monitoring device(s) programmed and configured to remotely obtain patient physiological attributes outside of a clinic or hospital setting, the monitoring device(s) configured to transmit the obtained physiological attributes through a networking interface to the patient management platform; wherein the patient management platform is programmed and configured to: transmit and receive patient data to and from the one or more electronic patient data systems; receive remote patient data representing the physiological patient attributes obtained by the remote patient monitoring device(s); analyze the remotely obtained patient physiological attributes and patient data, wherein the analyzing comprises comparing the physiological attributes and patient data with at least one clinical treatment protocol; in response to the analyzing, determine one or more recommended action(s) to be taken according to at least one clinical treatment protocol; and based on the recommended action(s), generate a treatment or care plan for the patient and cause the generation and transmission of notifications to a patient or provider device connected with the patient management platform, the notifications including the treatment or care plan for the patient.

36. The system of claim 35, wherein the one or more electronic patient data systems comprise one or more of an electronic medical records (EMR) system, a laboratoryinformation system (LIS), an electronic pharmacy management system, a picture archiving and communication system (PACS), a digital pathology (DP) system, or a radiology information system (RIS).

37. The system of any of claims 35-36, wherein the patient data received from the electronic patient data systems comprises an administration of a therapy and test results pertaining to measuring or estimating a presence of a biomarker, wherein the recommended action(s) comprises at least one of increasing or maintaining the amount of administered therapy.

38. The system of any of claims 35-37, wherein the patient data received from the electronic patient data systems comprises test results or an absence of test results pertaining to measuring the biomarker, and the recommended action(s) comprises recommending a test for detecting the biomarker.

39. The system of any of claims 35-38, wherein analyzing the remotely obtained patient data comprises estimating a presence of the biomarker based on the remotely obtained physiological attributes, and the recommended action(s) is based on the estimated presence of the biomarker.

40. The system of any of claims 35-39, wherein the one or more remote patient monitoring device(s) comprises one or more of a heart rate monitor, heart rhythm monitor, activity tracker, sleep monitor, blood pressure monitor, blood-oxygen level monitor, and / or mobile telecommunications device.

41. The system of any of claims 35-40, wherein the one or more mobile and remote patient monitoring device(s) are connected through an Internet of Things (loT) interface configured to communicate the remote patient data.

42. The system of any of claims 35-41, wherein the treatment or care plan comprises recommended and prioritized action(s) and wherein transmitting the treatment or care plancauses a higher of the prioritized action(s) to be respectively highlighted on the patient or provider device.

43. The system of any of claims 35-42, wherein the patient management platform is programmed and configured to: obtain patient outcome data relating to the recommended action(s) pertaining to multiple patients; determine a performance relationship between the outcome data and recommended action(s) pertaining to multiple patients; and generate and cause the display of a report comprising the determined performance relationship.

44. The system of claim 43, wherein the performance relationship comprises at least one of mortality or readmission rates of patients based on at least the recommended action(s) and / or the clinical treatment protocol.

45. The system of any of claims 35-44, further comprising one of a driver or a service that converts data from the one or more devices into a compatible form within the platform and / or a provider network.

46. The system of any of claims 35-45, wherein biomarkers can be estimated based on one or more of the physiological attributes that do not require a blood test and the physiological attributes can be monitored with the monitoring devices.

47. The system of any of claims 35-46, wherein the patient management platform is further configured to utilize machine learning to (i) correlate the patient physiological attributes with biomarkers obtained from other patients while being remotely monitored and then (ii) manage heart disease care for a patient of interest.

48. The system of any of claims 35-47, further comprising a large language model (LLM) based interface configured for patients and / or providers to enter information abouta patient or submit queries regarding patient status or recommended treatment and / or diagnostic protocols.

49. The system of claim 48, wherein the LLM is configured to translate or structure submitted information according to a predetermined schema, structure and / or terminology.

50. The system of any of claims 48-49, wherein the LLM is configured to generate answers or recommendations in response to questions and a review of current patient data, other patient historical data, and / or third-party sources.

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