AI-enabled access to healthcare services

Abstract

A system collects first data related to a user. The first data includes historical health data and a sensor data set corresponding to a set of health monitoring parameters. The system applies a first artificial intelligence (AI) model to the first data to calculate an index reflecting a deviation of the user's health state relative to a reference value. Based on the index, the system generates first inferred data including a label or tag related to a cause of the deviation. Based on the first inferred data, the system determines a first requirement for the user to visit a first healthcare center. Based on the first data and the first inferred data, the system further determines a first user-related data set related to the first requirement. The system then transfers the first user-related data set to an electronic healthcare system associated with the first healthcare center.

Description

Technical Field

[0001] 〔Cross-Reference to Related Applications / Incorporation by Reference〕 This application claims the benefit of priority of U.S. Patent Application No. 17 / 462,285, filed with the United States Patent and Trademark Office on August 31, 2021. Each of the above applications is hereby incorporated by reference in its entirety.

[0002] Various embodiments of the present disclosure relate to artificial intelligence (AI)-based healthcare services. Specifically, various embodiments of the present disclosure relate to systems and methods for artificial intelligence-enabled access to healthcare services.

Background Art

[0003] Advances in the field of medical science have led to the development of various healthcare and medical services. Such services can assist patients who require emergency or non-emergency medical support or intervention. Healthcare and medical services can be provided to patients through medical facilities such as hospitals and clinics. Usually, patients may visit one or more healthcare centers during the process of medical examination or treatment. Each time a patient visits a healthcare center, they may have limited comprehension to explain their health condition, symptoms, or other relevant information to medical staff. In some cases, patients may be referred to another healthcare center for further treatment. The other healthcare center to which the patient is referred may not have all the necessary information about the patient that the previous healthcare center had. Throughout the examination period, a lot of information about the patient may be lost. The reason for the loss of information may be that patient care is almost fragmented, and most healthcare services operate through an infrastructure (including information technology (IT) infrastructure) that can be based on a closed platform. The loss of information may affect the provision of appropriate and timely medical treatment or intervention to patients.

Summary of the Invention

[0004] Those skilled in the art will be able to see the limitations and disadvantages of conventional methods by comparing the described system with some aspects of the disclosure shown with reference to the drawings in the remainder of this application. [Means for solving the problem]

[0005] The present invention provides a system and method for artificial intelligence (AI) enabled access to healthcare services, as illustrated and / or described substantially in relation to at least one figure and more fully provided in the claims.

[0006] These and other features and advantages of the disclosure can be understood by considering the following detailed description of the disclosure with reference to the accompanying drawings, which indicate the same elements throughout by the same reference numerals. [Brief explanation of the drawing]

[0007] [Figure 1] This diagram shows an exemplary network environment for AI-enabled access to healthcare services according to embodiments of the present disclosure. [Figure 2] This figure shows an exemplary scenario for AI-enabled access to healthcare services according to embodiments of the present disclosure. [Figure 3] This is a sequence diagram illustrating a set of actions for establishing an encrypted session for transferring user-related data according to an embodiment of the present disclosure. [Figure 4A] Figure 4B shows a sequence diagram illustrating, together with other figures, a method for enabling access to the services of the first healthcare center according to an embodiment of the present disclosure. [Figure 4B] Figure 4A shows a sequence diagram illustrating, together with other figures, a method for enabling access to the services of a first healthcare center according to an embodiment of the present disclosure. [Figure 5A]Figure 5B shows a sequence diagram illustrating, together with other figures, a method for enabling access to the services of a second healthcare center according to an embodiment of the present disclosure. [Figure 5B] Figure 5A shows a sequence diagram illustrating, together with other figures, a method for enabling access to the services of a second healthcare center according to an embodiment of the present disclosure. [Figure 6A] Figure 6B shows a sequence diagram illustrating a method for scheduling an emergency response (ER) service according to an embodiment of the present disclosure. [Figure 6B] Figure 6A shows a sequence diagram illustrating a method for scheduling an emergency response (ER) service according to an embodiment of the present disclosure. [Figure 7A] Figure 7B shows a sequence diagram illustrating, together with other figures, a method for determining recommendations from one or more healthcare centers according to embodiments of the present disclosure. [Figure 7B] This sequence diagram, along with Figure 7A, shows a method for determining one or more healthcare center recommendations according to an embodiment of the present disclosure. [Figure 8] This is a sequence diagram illustrating a method for a VR (virtual reality) based medical consultation session according to an embodiment of the present disclosure. [Figure 9] This figure shows a master-slave configuration of multiple AI models according to an embodiment of the present disclosure. [Figure 10A] Figure 10B shows a sequence diagram illustrating the set of operations between multiple IA models according to an embodiment of the present disclosure. [Figure 10B] This sequence diagram, along with Figure 10A, shows the set of operations between multiple IA models according to the embodiments of this disclosure. [Figure 11] This figure shows an exemplary determination of a first requirement using a first AI model according to an embodiment of the present disclosure. [Figure 12] This is a block diagram of a system that enables artificial intelligence (AI)-based access to healthcare services according to embodiments of the present disclosure. [Figure 13]This flowchart shows an exemplary method for enabling artificial intelligence (AI) enabled access to healthcare services according to embodiments of the present disclosure. [Modes for carrying out the invention]

[0008] Systems and methods for artificial intelligence (AI) enabled access to healthcare services disclosed may include implementations described below. Exemplary aspects of this disclosure provide a system that can be configured to collect user-related data (such as a patient). The collected data may include historical health data and a sensor dataset corresponding to a health monitoring parameter set. The sensor dataset may be obtained from a set of sensors, such as a blood pressure sensor, a heart rate sensor, and a bioimpedance sensor. The set of sensors may be part of a user device (such as a smartphone) associated with the user, or separate from the user device.

[0009] At some point, the system can apply a first AI model to the collected data to calculate one or more indices that can reflect the deviation of the user's health status from a baseline. For example, the first index may have a value between 0 and 1 based on the confidence score of the first AI model's prediction of the health status deviation. In one scenario, the system can determine the deviation of a user's blood pressure measurement (e.g., a BP measurement of 147 / 92 millimeters of mercury (mmHg)) from a baseline blood pressure measurement (e.g., a baseline BP measurement of 120 / 80 mmHg). The first index corresponding to the determined deviation may be 0.92. In one embodiment, the first index may correspond to a confidence score or prediction score associated with the first AI model. A value close to 1 may indicate that the first index reflects a strong deviation of the monitored user's health status from the baseline.

[0010] The system can generate inference data that may include one or more labels or tags related to the cause of deviations in health status, based on one or more calculated indicators. For example, the inference data may include the label "hypertension" based on deviations in the user's blood pressure measurements.

[0011] Based on the generated inference data, the system can determine the requirements that might necessitate a user visiting a healthcare center (e.g., a clinic). For example, the determined requirement might be a medical consultation with a healthcare professional. Based on the collected data and inference data, the system can determine user-related datasets relevant to the first determined requirement. For example, user-related datasets may include the user's personal details (such as name, age, and gender), as well as recorded sensor data and historical health data (such as heart rate, blood pressure, and blood glucose measurements).

[0012] The system can be configured to transfer the determined user-related dataset to an electronic healthcare system associated with the healthcare center (such as a computer, mobile device, edge node associated with the healthcare center, or server). In one embodiment, the user-related dataset can be transferred to the electronic healthcare system before or at the time the user visits the healthcare center. In another embodiment, the transfer may be based on the determination that the user has departed for the healthcare center. The transferred user-related dataset may include all data points that may be necessary to enable healthcare professionals at the healthcare center (who may be, for example, doctors or nurses) to analyze the user's health status, diagnose any medical conditions, physically examine the user, or instruct or provide the user with tests, prognoses, medications, or interventions.

[0013] According to an embodiment, the system can be configured to collect medical data related to medical treatment received by a user at a healthcare center as part of the determined requirements. The system can update a first AI model based on the collected medical data. The system can be further configured to apply the first AI model to the collected medical data and the collected data to generate inference data. Based on the generated inference data, the system can determine requirements that the user may need to visit a healthcare center (such as a hospital) that can be different from a first healthcare center (such as a clinic). For example, the second requirement can be to be able to handle a scheduled surgery based on second inference data that can include a label of "kidney stone". In such a case, the system can determine a second user-related dataset based on the collected first data, the collected medical data, and the second inference data. The second user-related dataset can be related to the determined second requirement and can be required by a second electronic healthcare system (such as a computer, a mobile device, or a server) related to the second healthcare center. The system can transfer the determined second user-related dataset to the second electronic healthcare system. Therefore, by transferring the second user-related dataset to the second electronic healthcare system, a complete medical history of the user, which can include the diagnosis results of the previously visited clinic, can be provided to medical staff (such as a doctor or a nurse) at the second healthcare center.

[0014] According to an embodiment, the system can be configured to receive, by a user device, a request to share a data portion of the collected first data with a first AI model. Based on this request, the system can build an encrypted session between the first AI model and the user device. While the encrypted session is active, the system can transfer the data portion of the collected first data to the first AI model and store the transferred data portion in encrypted form in a data store. For example, this data store can be associated with the user device. Thus, the system can enable storage of the data portion of the collected first data in encrypted form, thereby protecting the privacy of the user.

[0015] The system of the present disclosure can collect all relevant data points including the user's medical history before the user requests a medical examination and throughout the medical examination period. Such data points can be utilized by medical staff at various healthcare centers (such as primary healthcare centers or secondary healthcare centers) to address all types of ongoing or future medical requirements of the user. The system interacts with a connected network of an electronic healthcare system (i.e., nodes of a distributed network) that can each be associated with a healthcare center. An AI model can be hosted on the system and the electronic medical system, and this AI model can analyze and exchange data regarding the user and the medical services provided to the user.

[0016] The collected data points about the user can be shared with maximum privacy through encrypted sessions, allowing any healthcare professional associated with any of the various healthcare centers to access holistic information about the user's health. The system of this disclosure can provide a user-centered (i.e., patient-centered) end-to-end solution that monitors various health parameters of the user and uses an AI model to determine the user's requirements for medical consultation. This solution can also determine the data that the user needs to share with a healthcare center or an AI model associated with the healthcare center before they have a consultation or receive a service at the healthcare center. On the user side, the (single or multiple) AI model functions to help the user access various healthcare services. On the service provider side, such models (single or multiple) function to help various healthcare professionals assess the user's health and better understand the user's requirements. The (single or multiple) AI model can exchange data (including collected data, learned information, and learned neural parameter values) at each step of the consultation process. In a decentralized network, the system and network nodes can maintain holistic information about users' health status, medical records, prescriptions, medical tests, and treatments. This not only improves users' access to various medical services but also enables healthcare centers to provide users with high-quality medical support.

[0017] Figure 1 is a diagram of an exemplary network environment for AI-enabled access to healthcare services according to an embodiment of the present disclosure. Figure 1 shows a diagram of the network environment 100. The network environment 100 may include a system 102, a first AI model 104, and a user device 106 associated with a user 108. The network environment 100 may further include a first electronic healthcare system 110 and a second AI model 112 associated with the first electronic healthcare system 110. The first electronic healthcare system 110 may be associated with a first healthcare center 114. The network environment 100 may further include a second electronic healthcare system 116 and a third AI model 118 associated with the second electronic healthcare system 116. The second electronic healthcare system 116 may be associated with a second healthcare center 120. Furthermore, the network environment 100 may include a third electronic healthcare system 122 and a fourth AI model 124 associated with the third electronic healthcare system 122. A third electronic healthcare system 122 may be associated with an emergency response (ER) service 126. The network environment 100 may further include a server 128 capable of storing the first data 130.

[0018] The network environment 100 may include a series of sensors 132 associated with the user device 106, and a communication network 134. System 102, the first electronic healthcare system 110, the second electronic healthcare system 116, the third electronic healthcare system 122, and the server 128 can communicate with each other via the communication network 134.

[0019] System 102 may include suitable logic, circuits, interfaces, and / or code that can be configured to apply one or more AI models to monitor the health status of user 108 and determine indicators that reflect deviations in health status. During monitoring, System 102 may determine one or more requirements that may necessitate user 108 visiting a healthcare center (such as the first healthcare center 114). System 102 may further determine user-related datasets related to such requirements and transfer the determined user-related datasets to one or more electronic healthcare systems (such as the first electronic healthcare system 110). Examples of implementations of System 102 include, but are not limited to, cloud servers (public, private, or hybrid cloud servers), distributed computer servers or clusters of servers, software-as-a-service (SaaS) application servers, edge computer systems including networks of distributed computers / edge nodes, mainframe systems, workstations, personal computers, or mobile devices.

[0020] In one embodiment, system 102 may include a front-end subsystem and a back-end subsystem. The front-end subsystem can be deployed on-premises or at the location of different entities, such as healthcare centers. In one embodiment, the front-end subsystem may be a client-side application accessible on a user device, such as user device 106. The front-end subsystem may be configured to display a UI that includes user interface (UI) elements that allow user 108 and healthcare professionals to provide input and view health information about user 108. The back-end subsystem may include a server-side application that can perform instructions regarding the application of (one or more) AI models, or other actions related to the requirements of user 108 and / or the healthcare center.

[0021] The first AI model 104 can be a machine learning model or a deep learning model that can be trained to identify relationships between inputs (such as morphological features of health parameter measurements, such as blood glucose and pulse rate measurements) and outputs (such as labels or scores, which may be indicators or inferences related to health status). The first AI model 104 can be further trained to output symptoms related to user 108 based on first data 130 collected by system 102. The first AI model 104 can be hosted on system 102 or on user device 106.

[0022] The first AI model 104 can be defined by the network topology and parameters such as the number of weights, cost function, input size, and number of layers. During the development of the first AI model 104, its parameters can be adjusted after each training epoch. During training, the weights can be updated to move towards the global minima of the cost function of the first AI model 104. After training for multiple epochs on features in the training dataset, the first AI model 104 can be trained to output prediction / classification / regression results for the input set. In the case of classification, the results may show the class label of each input in the input set (e.g., input features extracted from new / undiscovered instances).

[0023] The first AI model 104 may include electronic data that can be implemented, for example, as a software component of an application executable on system 102. The first AI model 104 may rely on libraries, external scripts, or other logic / instructions for execution by a processing unit such as the processor of system 102. The first AI model 104 may include code and routines configured to enable a computer device such as system 102 to perform one or more operations, such as calculating one or more first indicators that can reflect the deviation of user 108's health status from a baseline. In addition to or instead of the above, the first AI model 104 may also be implemented using hardware including a processor, a microprocessor (e.g., one or more operations that are performed or controlled), a field-programmable gate array (FPGA), or an application-specific integrated circuit (ASIC). Alternatively, in some embodiments, the ML model may be implemented using a combination of hardware and software.

[0024] In some embodiments, the first AI model 104 may be a neural network model. The neural network model may be a computational network or a system of artificial neurons or nodes that can be arranged in multiple layers. The multiple layers of the neural network model may include an input layer, one or more hidden layers, and an output layer. Each of the multiple layers may include one or more nodes (or, for example, artificial neurons represented by circles). The outputs of all nodes in the input layer may be coupled to at least one node in the (one or multiple) hidden layers. Similarly, the input of each hidden layer may be coupled to the output of at least one node in the other layers of the neural network model. The output of each hidden layer may be coupled to the input of at least one node in the other layers of the neural network model. The (one or multiple) nodes in the final layer may receive input from at least one hidden layer and output a result. The number of layers and the number of nodes in each layer may be determined from the hyperparameters of the neural network model. Such hyperparameters may be set before, during, or after training the neural network model with respect to the training dataset.

[0025] Each node in a neural network model can correspond to a mathematical function (e.g., a sigmoid function or a rectified linear unit) with a set of parameters that can be adjusted during network training. These parameters may include, for example, weight parameters and regularization parameters. Each node can use the mathematical function to compute an output based on one or more inputs from nodes in other (single or multiple) layers of the neural network model (e.g., previous (single or multiple) layers). All or some nodes in a neural network model can correspond to the same or different mathematical functions.

[0026] In training a neural network model, one or more parameters of each node in the neural network model can be updated based on whether the output of the final layer for a given input (from the training dataset) matches the correct result based on the neural network model's loss function. This process can be repeated for the same or different inputs until the minimum value of the loss function is achieved and the training error is minimized. Several training methods are known in this field, including gradient descent, stochastic gradient descent, batch gradient descent, gradient boosting, and metaheuristic methods.

[0027] Examples of neural network models include, but are not limited to, deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), CNN-recurrent neural networks (CNN-RNNs), R-CNN, Fast R-CNN, Faster R-CNN, artificial neural networks (ANNs), (You Only Look Once) YOLO networks, long-shortening memory (LSTM) network-based RNNs, CNN+ANN, LSTM+ANN, gated recurrent unit (GRU)-based RNNs, fully connected neural networks, Connectionist Temporal Classification (CTC)-based RNNs, deep Bayes neural networks, and / or combinations of these networks. In some embodiments, the learning engine may include numerical methods using a dataflow graph. In some embodiments, the neural network model may be based on a hybrid architecture of multiple deep neural networks (DNNs).

[0028] User device 106 may include suitable logic, circuitry, interfaces, and / or code that can be configured to receive a sensor dataset corresponding to a health monitoring parameter set associated with user 108. The sensor dataset may be received from a set of sensors 132 associated with user device 106. User device 106 may include a display device that can display data such as recommendations from a healthcare center and medical professionals to user 108. Examples of user device 106 include, but are not limited to, wearable health devices (such as fitness bands), smartphones, cellular phones, mobile phones, personal computers, workstations, kiosk devices that can be associated with a set of sensors 132, or consumer electronic (CE) devices that can interact with or be associated with a set of sensors 132.

[0029] The series of sensors 132 may include suitable logic, circuitry, and / or interfaces that can be configured to monitor a set of health monitoring parameters related to the user 108. The series of sensors 132 can generate a sensor dataset corresponding to the monitored set of health monitoring parameters. The set of health monitoring parameters may include, for example, pulse rate measurements, blood pressure measurements, body temperature measurements, blood glucose measurements, one or more images of affected areas of the user 108's body, oxygen level measurements, pedometer measurements, and respiratory pattern measurements. In some embodiments, the series of sensors 132 can be communicatively coupled to a user device 106. One or more sensors from the series of sensors 132 can be integrated into the user device 106 or worn by the user 108. Examples of the series of sensors 132 include, but are not limited to, photovoltaic (PPG) sensors, temperature sensors, blood pressure sensors, ambient oxygen partial pressure (ppO2) sensors, imaging sensors, microphones, artificial intelligence robot (AIBO) sensors, step detection sensors, step counter sensors, glucose monitoring sensors, accelerometers, gyroscopes, and Global Positioning System (GPS) sensors.

[0030] The first electronic healthcare system 110 may include suitable logic, circuits, interfaces and / or code that can be configured to receive a set of user-related data relating to one or more requirements (medical / health) of user 108. The first electronic healthcare system 110 may be further configured to control a display device associated with the first healthcare center 114 to display a set of presentation data to healthcare professionals associated with the first healthcare center 114.

[0031] In some embodiments, the first electronic healthcare system 110 can host one or more AI models and can be connected to multiple computers or display devices in the first healthcare center 114. The first electronic healthcare system 110 can be a node in a distributed computing system which may also include system 102, multiple computers or display devices as nodes. Examples of implementations of the first electronic healthcare system 110 include, but are not limited to, cloud servers (public, private, or hybrid cloud servers), distributed computing servers or server clusters, software-as-a-service (SaaS) application servers, edge computing systems including networks of distributed computers / edge nodes, mainframe systems, workstations, personal computers, or mobile devices.

[0032] The second electronic healthcare system 116 and the third electronic healthcare system 122 may be identical or similar to the first electronic healthcare system 110, as described in Figure 1, for example. Therefore, for the sake of brevity, a description of the second electronic healthcare system 116 and the third electronic healthcare system 122 will be omitted from this disclosure.

[0033] The second AI model 112 may be associated with the first electronic healthcare system 110. In some embodiments, the second AI model 112 may be hosted on the first electronic healthcare system 110 and configured to interact with the first AI model 104 or the system 102 hosting the first AI model 104 to receive information such as a first user-related dataset associated with user 108. The second AI model 112 may also receive results or labels / tags (such as symptoms of a medical condition) that can be generated by the first AI model 104 and / or other AI models such as the third AI model 118. Based on the received results or labels / tags, the second AI model 112 may generate information that helps healthcare professionals such as nurses and doctors understand and diagnose problems affecting user 108's health. For example, this information may include suggestions for performing relevant medical tests for user 108.

[0034] The second AI model 112, the third AI model 118, and the fourth AI model 124 can be architecturally identical or similar to the first AI model 104. Therefore, for brevity, descriptions of the third AI model 118 and the fourth AI model 124 are omitted from the disclosure. The functions of the above models may be the same or different from each other. These differences may be based on the training data on which each such model is trained. For example, the first AI model 104 can monitor the health status of user 108, while the second AI model 112 or the third AI model 118 can provide information, including insights or suggestions, to healthcare professionals in a healthcare center.

[0035] Healthcare centers such as the first healthcare center 114 and the second healthcare center 120 may correspond to entities such as hospitals, clinics, medical testing laboratories, or healthcare touchpoints. In some embodiments, the first healthcare center 114 may be a primary healthcare center, such as a clinic, to which user 108 may visit for a primary check-up. The second healthcare center 120 may be a secondary healthcare center, such as a hospital, to which user 108 may visit for any type of medical intervention, such as surgical procedures. In some embodiments, the first healthcare center 114 and the second healthcare center 120 may be primary healthcare centers. In some other embodiments, the first healthcare center 114 and the second healthcare center 120 may be secondary healthcare centers.

[0036] ER service 126 can accommodate mobile medical assistance and transport services, such as ambulance services with or without a first responder. Examples of ER service 126 include, but are not limited to, basic ambulances (e.g., ambulances equipped with first aid and basic life support systems), advanced ambulances (e.g., ambulances equipped with advanced life support and intensive care systems), mortuary ambulances, and ambulances (capable of airlifting patients in remote areas, critically ill patients, injured patients, or deceased patients). Each vehicle associated with ER service 126 may include a mobile data terminal that allows user 108 to upload health status data while receiving ER service 126.

[0037] Server 128 may include suitable logic, circuitry and interfaces, and / or code that can be configured to store user-related data (such as the first data 130) in a secure health database, such as a HIPAA-compliant database. Server 128 may be associated with System 102 or User Device 106. Server 128 may be implemented as a cloud server and may perform operations through web applications, cloud applications, HTTP requests, repository operations, and file transfers. Other implementation examples of Server 128 include, but are not limited to, a database server, file server, web server, media server, application server, mainframe server, or cloud computing server.

[0038] In at least one embodiment, the server 128 can be implemented as multiple distributed cloud-based resources by using several techniques well known to those skilled in the art. Those skilled in the art will understand that the scope of this disclosure may not be limited to implementations of the server 128 and system 102 as two independent entities. In some embodiments, the functionality of the server 128 can be incorporated into system 102, either entirely or at least partially, without departing from the scope of this disclosure.

[0039] According to one embodiment, a first electronic healthcare system 110, a second electronic healthcare system 116, a third electronic healthcare system 122, a server 128, a user device 106, and a series of sensors 132 can be part of a system 102 as nodes that are connected to each other in a communicative manner.

[0040] During operation, the user device 106 can be configured to monitor health parameters such as the user 108's blood pressure and pulse rate, but is not limited to these parameters. In some embodiments, the user device 106 can continuously monitor the user 108's health parameters when the user 108 is at home or anywhere else. For example, if the user 108 has heart disease, the user device 106 can receive measurements related to the user 108's blood pressure, oxygen saturation, and pulse rate from a series of sensors 132 at regular time intervals (such as every minute or every hour).

[0041] System 102 can collect first data 130 related to user 108 from user device 106. The collected first data 130 may include historical health data and sensor datasets corresponding to health monitoring parameter sets. System 102 can collect historical health data and sensor datasets from user device 106 or server 128. The health monitoring parameter set may relate to at least one of the following: user 108's known health status, one or more medical interventions user 108 has received in the past, or one or more comorbidities related to user 108. In addition to or instead of this, the collected first data 130 may also include medical history, family history and social health (PFSH) data, and collaborative filtered data. The collaborative filtered data may also include health-related data points related to a defined population, a specific geographic set, a specific demographic, or a viral infection or spread within a defined population. In addition to or instead of the above, the collected first data 130 may also include the user 108's appointment schedule for a series of medical or health interventions at one or more healthcare centers, including the first healthcare center 114. For example, the series of medical interventions may include surgical procedures such as replacing a pacemaker or implantable cardioverter-defibrillator (ICD). Further details regarding the collection of the first data 130 are shown, for example, in Figure 4A.

[0042] According to one embodiment, system 102 can receive a request from user device 106 to share a data portion of the collected first data 130 with the first AI model 104. Based on this request, system 102 can establish an encrypted session between the first AI model 104 and the user device 106. While the encrypted session is active, system 102 can transfer the data portion of the collected first data 130 to the first AI model 104. System 102 can store the transferred data portion in an encrypted form in a data store. Further details of the encrypted session are shown, for example, in Figure 3.

[0043] System 102 can apply the first AI model 104 to the collected first data 130 to calculate one or more first indicators that can reflect the deviation of user 108's health status from a baseline. For example, the first indicator may include a value between 0 and 1 as a confidence measure of the first AI model 104 in determining the deviation of user 108's health status. Further details on applying the first AI model 104 to calculate one or more first indicators are shown, for example, in Figure 4A.

[0044] System 102 can generate first inference data that may include one or more labels or tags related to the cause of the deviation in health status, based on one or more first indicators that have been calculated. For example, the first inference data may include labels such as "hypertension," "high blood sugar," and "worsening injury." Further details of the generation of the first inference data are shown in Figure 4A, for example.

[0045] Based on the generated first inference data, system 102 can determine a first requirement that may necessitate a visit by user 108 to the first healthcare center 114. This first requirement could, for example, correspond to a medical emergency, a scheduled medical examination, a scheduled surgical procedure, or a medical consultation. Further details regarding the determination of the first requirement are shown, for example, in Figure 4A.

[0046] System 102 can determine a first user-related dataset that may be relevant to a determined first requirement, based on the collected first data 130 and first inference data. For example, the first requirement may be a "medical emergency." The first user-related dataset may include detailed contact information (such as the user 108's phone number and address), personal details (such as the user 108's name, age, and gender), and details related to the cause of the medical emergency (such as a heart attack or paralysis). Such details may include, for example, sensor data, biomarkers, logs of changes in health status over a period of time (such as the past 5 days), and past cases of similar health status and medical interventions. Further details on the determination of the first user-related dataset are shown in Figure 4B, for example.

[0047] System 102 can transfer the determined first user-related dataset to the first electronic healthcare system 110 associated with the first healthcare center 114. The transfer can be performed before user 108 decides to visit the first healthcare center 114, after user 108 makes a reservation at the first healthcare center 114, or while user 108 is on their way to the first healthcare center 114. Further details regarding the transfer of the determined first user-related dataset are shown, for example, in Figure 4B.

[0048] In some embodiments, the first AI model 104 can output symptoms related to the user's 108 current health condition based on the received data portion of the collected first data 130. The first AI model 104 can interact with the second AI model 112 and transfer the symptoms to the second AI model 112. The second AI model 112 can then suggest relevant tests related to the symptoms to one or more healthcare professionals at the first healthcare center 114.

[0049] According to one embodiment, system 102 can generate a presentation dataset based on applying a second AI model 112 to a transferred first user-related dataset. The presentation dataset may include data points that a healthcare professional, such as a physician or nurse associated with a first healthcare center 114, may need to assess the current health status of user 108 and address the determined first requirements. System 102 can control a display device (e.g., a display device associated with a first electronic healthcare system 110) to display the generated presentation dataset to a healthcare professional. Further details of presentation dataset generation are shown, for example, in Figures 2 and 4B.

[0050] According to one embodiment, system 102 can detect the presence of user 108 at a first healthcare center 114. Based on the detection, system 102 can collect medical data related to the medical treatment (or intervention) received by user 108 at the first healthcare center 114 as part of a determined first requirement. System 102 can update the first AI model 104 based on the collected medical data. Details of updating the first AI model 104 are further shown, for example, in Figure 4B.

[0051] According to one embodiment, system 102 can generate second inference data by applying a first AI model 104 to the collected medical data and the collected first data 130. Based on the generated second inference data, system 102 can determine a second requirement that may necessitate user 108 visiting a second healthcare center 120, which may be different from the first healthcare center 114. For example, the second requirement may be a surgical procedure scheduled at a secondary healthcare center. Based on the collected first data 130, the collected medical data, and the second inference data, system 102 can determine a second user-related dataset that may be relevant to the determined second requirement and may be required by a second electronic healthcare system 116 associated with the second healthcare center 120. For example, the second user-related dataset may include personal details, detailed contact information, details of user 108's health status, and details of medical treatments / interventions received at the first healthcare center 114.

[0052] System 102 can transfer the determined second user-related dataset to the second electronic healthcare system 116. This transfer can be performed before user 108 decides to visit the second healthcare center 120, after user 108 has made an appointment at the second healthcare center 120, or while user 108 is en route to the second healthcare center 120. The second user-related dataset can be used to present data such as insights into user 108's health status, as well as details about the series of events that led user 108 to visit the first healthcare center 114 and the second healthcare center 120. Such data can help healthcare professionals at the second healthcare center 120 provide user 108 with appropriate medical treatment. Further details about the medical treatments available to user 108 at the second healthcare center 120 are shown, for example, in Figures 5A and 5B.

[0053] In one embodiment, system 102 can determine a second healthcare center 120 based on the user's 108 current location and a determination that the determined first requirement corresponds to a medical emergency. Initially, system 102 can schedule an emergency room service 126, such as an ambulance service, to transport user 108 to the second healthcare center 120 (which may be a second healthcare center suitable for a medical emergency). Based on the scheduled emergency room service 126, system 102 can transfer the first user-related dataset to a second electronic healthcare system 116 associated with the second healthcare center 120. In some embodiments, system 102 can transfer the first user-related dataset to a third electronic healthcare system 122 associated with a third healthcare center, which may be different from the first healthcare center 114 and the second healthcare center 120. In one embodiment, system 102 can send alert notifications to one or more devices (such as devices associated with family and relatives) that are registered to receive alert notifications, based on a determination that the first requirement corresponds to a medical emergency. Further details regarding the schedule for ER service 126 are shown, for example, in Figures 6A and 6B.

[0054] In some scenarios, user 108 may require medical assistance while out or traveling. In such scenarios, system 102 can determine user 108's current location and, using a first AI model 104, can further determine one or more recommendations that may include one or more healthcare centers (or touchpoints) related to user 108's first requirement. System 102 can control user device 106 to display the determined one or more recommendations. One or more healthcare centers may be located within a threshold distance from user 108's current location.

[0055] According to one embodiment, system 102 can receive a first input via user device 106. The first input may include a first selection of a first healthcare center 114 from among one or more healthcare centers, and a second selection of a schedule for making an appointment at the first healthcare center 114. Based on the received first input, system 102 can schedule a visit by user 108 to the first healthcare center 114. Based on the selected schedule, a first user-related dataset can be transferred to the first electronic healthcare system 110. Further details regarding the determination and selection of recommendations are shown, for example, in Figure 7.

[0056] According to one embodiment, system 102 may send a request to the first electronic healthcare system 110 to authorize a virtual reality (VR)-based consultation session for user 108, based on a determination that user 108's current location may differ from the location of the first healthcare center 114. System 102 may receive authorization from the first electronic healthcare system 110 for the transmitted request. Such authorization may be granted by a healthcare professional, the administrator of the first healthcare center 114, or software tracking the availability of all healthcare professionals for VR-based consultations. Based on the received authorization, system 102 may establish a VR-based consultation session between the user device 106 and a wearable electronic device worn by a healthcare professional at the first healthcare center 114. While the VR-based consultation session is active, a determined first user-related dataset is transferred to the wearable electronic device, which can then render such data along with user 108's video / audio / 3D model feed. Further details of the VR-based consultation session are shown, for example, in Figure 8.

[0057] Figure 2 shows an exemplary scenario for AI-enabled access to healthcare services according to embodiments of the present disclosure. The description of Figure 2 is made in relation to the elements of Figure 1. Figure 2 shows an exemplary scenario 200. Scenario 200 may include the home 202 of user 108, a data store 204 associated with system 102, user equipment 106 (not shown in Figure 2), and a server 128 (not shown in Figure 2). Note that scenario 200 in Figure 2 is for illustrative purposes only and should not be construed as limiting the scope of the present disclosure.

[0058] In an exemplary scenario 200, the user device 106 can continuously monitor user 108's health parameters at user 108's home 202. In some embodiments, the user device 106 can also monitor user 108's health parameters when user 108 is outside of home 202. For example, the user device 106 could be a fitness band worn by user 108 on their wrist. The fitness band can monitor health parameters such as user 108's pulse rate and the number of steps taken by user 108. Furthermore, user 108 or an attendant can record health parameters (such as blood pressure, oxygen level, and blood glucose levels) and manually input the recorded health parameters into the user device 106. Alternatively, user 108 can wear one or more sensors that can collect data on health parameters such as blood pressure, oxygen level, and blood glucose levels and digitally upload this data to system 102. In one embodiment, user 108 can capture one or more images of a disease such as a skin infection or injury of user 108 via an imaging device (such as a camera on user device 106 of user 108).

[0059] System 102 can collect first data 130 related to user 108. The first data 130 may include historical health data and sensor datasets corresponding to a set of health monitoring parameters. Such parameters may include pulse rate, step count, blood pressure measurements, oxygen level measurements, blood glucose measurements, and one or more images of user 108's external conditions (such as skin infections or injuries). The first data 130 can be stored in a data store 204 associated with user device 106, system 102, or server 128.

[0060] System 102 can apply a first AI model 104 to the first data 130 collected from user 108 to calculate one or more first indices that can reflect the deviation of user 108's health status from a baseline. For example, the calculated first indice may be 0.95. The first data 130 may include blood glucose measurements over a month. The average of the blood glucose measurements may be 200, which may be a deviation from a baseline (such as a value of 140). In this scenario, the blood glucose measurement may suggest that user 108's blood glucose level may be higher than the baseline. The calculated first indice can indicate the degree of deviation of the blood glucose measurement from the baseline. In one embodiment, the calculated first indice may be a confidence score related to a prediction of the first AI model 104, which may indicate the degree of deviation of user 108's health status from a baseline for a health status-related parameter.

[0061] System 102 can generate first inference data that may include one or more labels or tags related to the cause of the deviation, based on one or more first indicators that have been calculated. For example, the first inference data may include the label "high blood sugar" and the cause of the deviation "diabetes".

[0062] Based on the generated first inference data, system 102 can determine a first requirement that may necessitate user 108 visiting the first healthcare center 114. For example, the first requirement could be "a diabetes consultation."

[0063] System 102 can determine a first user-related dataset related to the determined first requirement based on the collected first data 130 and first inference data. In some embodiments, not all data in the collected first data 130 and first inference data may be related to the determined first requirement. For example, oxygen level measurements in the collected first data 130 may be irrelevant to the first requirement, such as a medical consultation regarding diabetes. System 102 can determine a first user-related dataset that may include information related to the diagnosis and treatment of diabetes, such as user 108's blood glucose measurements, user 108's recorded steps, and the diet plan followed by user 108. The first user-related dataset may further include personal information such as user 108's name, detailed contact information, and address.

[0064] Subsequently, system 102 can transfer the determined first user-related dataset to the first electronic healthcare system 110 associated with the first healthcare center 114. In some embodiments, the first user-related dataset can be transferred to the first electronic healthcare system 110 before user 108 arrives at the first healthcare center 114. In some other embodiments, the first user-related dataset can be transferred to the first electronic healthcare system 110 when user 108 arrives at the first healthcare center 114 and the presence of user 108 at the first healthcare center 114 is detected.

[0065] According to one embodiment, the first AI model 104 can suggest symptoms 206 that can be linked to the user's 108 current health condition as diabetes. For example, the first AI model 104 may suggest symptoms 206 such as frequent urination, fatigue, nausea, and hyperglycemia. In some cases, if the user 108 is unable to describe symptoms related to their condition, the symptoms 206 suggested by the first AI model 104 can be used for the diagnosis and / or treatment of the user 108.

[0066] In some embodiments, the first AI model 104 may be part of a user device 106 associated with a user 108. When the user 108 arrives at the first healthcare center 114, the first AI model 104 can interact with a second AI model 112 hosted on the first electronic healthcare system 110 of the first healthcare center 114. Based on this interaction, the first AI model 104 can share suggested symptoms 206 with the second AI model 112. The second AI model 112 can suggest relevant tests 208 based on the suggested symptoms 206 of the user 108. For example, the relevant tests 208 may include fasting blood glucose testing, A1C testing, and random blood glucose testing.

[0067] A second AI model 112 associated with the first electronic healthcare system 110 can generate a presentation dataset 210. The presentation dataset 210 may include, based on the preferences of the healthcare professional 212, a first user-related dataset (transferred by the first AI model 104), suggested symptoms 206, and related tests 208 in a structured format. The presentation dataset 210 may include, for example, the user 108's past medical records and a graphic representation of health parameters (included in the first user-related dataset), suggested symptoms 206, and related tests 208.

[0068] A healthcare professional 212, such as a nurse or doctor, can use the presented dataset 210 to assess the user's current health status and provide appropriate medical treatment or intervention. For example, medical treatment may include a diagnosis of a symptom 206. Based on the diagnosis, the healthcare professional 212 can prescribe medication and suggest medical tests to the user 108. The healthcare professional 212 can refer to relevant tests 208 and instruct the user 108 to undergo medical tests.

[0069] In some embodiments, user 108 can undergo medical examinations as instructed at a first healthcare center 114. Based on the medical assistance provided to user 108 at the first healthcare center 114, medical data 214 related to user 108 can be generated. The medical data 214 may include user 108's medical examination reports, diagnoses, and prescriptions given by healthcare professionals 212. The medical data 214 can be recorded in a first electronic healthcare system 110.

[0070] The second AI model 112 can interact with the first AI model 104 to share medical data 214 with the first AI model 104. System 102 can update the recorded first data 130 with medical data 214 obtained from the first healthcare center 114. At this point, the data store 204 can contain the first data 130 and medical data 214 related to user 108. User 108's examination can be completed based on the medical treatments received by user 108. Even after the examination is complete, system 102 can continuously monitor user 108's health parameters through the user device 106. System 102 can maintain holistic information about user 108's health status, medical records, prescriptions, medical tests, and treatments, enabling the healthcare center to provide user 108 with high-quality medical support.

[0071] Figure 3 is a sequence diagram showing a set of operations for establishing an encrypted session for transferring user-related data according to an embodiment of the present disclosure. The description of Figure 3 will be made in relation to the elements of Figures 1 and 2. Figure 3 shows a sequence diagram 300 showing a series of operations 302-310. These operations can be performed by various elements of the network environment 100, such as system 102, user device 106, and first AI model 104, but are not limited to the following.

[0072] In 302, a request can be received to share a data portion of the collected first data 130 with the first AI model 104. In one embodiment, the system 102 can be configured to receive a request from the user device 106 to share a data portion of the collected first data 130 with the first AI model 104. The data portion of the collected first data 130 can be shared in a manner that protects the privacy of user 108. The data portion of the collected first data 130 can be selected, for example, based on input from user 108. Any portion of the first data 130 that user 108 does not wish to share can be excluded from the data portion of the collected first data 130. For example, the data portion of the collected first data 130 may not include detailed contact information such as an address, or any medical history that user 108 may wish to keep private.

[0073] In 304, an encryption session can be initiated between the user device 106 and the first AI model 104. In one embodiment, the system 102 can be configured to initiate an encryption session between the user device 106 and the first AI model 104. The system 102 can encrypt the data portion of the collected first data 130 by using a secret key. The secret key can be used, for example, on the user device 106. Once the encryption session is initiated, the secret key can be shared with the first AI model 104. After receiving the secret key, the first AI model 104 can access the data portion of the collected first data 130.

[0074] In 306, the data portion of the collected first data 130 can be transferred to the first AI model 104. In one embodiment, the system 102 can be configured to transfer the data portion of the collected first data 130 to the first AI model 104. As described in 304, the data portion can be encrypted and transferred through an encrypted session between the user device 106 and the first AI model 104. The first AI model 104 can use the shared data portion of the collected first data 130 to calculate one or more first indicators that can reflect the deviation of the user 108's health status from a baseline.

[0075] According to one embodiment, system 102 can establish an encrypted session between a first AI model 104 and a second AI model 112 associated with a first electronic healthcare system 110. Through this encrypted session, the shared data portion of the collected first data 130 and suggested symptoms 206 can be transferred to the second AI model 112.

[0076] In 308, the transferred data portion can be stored in a data store in an encrypted form. In one embodiment, system 102 can be configured to store the transferred data portion in a data store 204 in an encrypted form. System 102 can store the transferred data portion in accordance with the standards of the Health Insurance Portability and Accountability Act (HIPPA). As a result, system 102 can ensure that user 108's identity and other personally identifiable information (PII) cannot be accessed after the consultation. Thus, system 102 can enable complete protection of the privacy of the shared portion of the first data 130 related to user 108.

[0077] In 310, the encryption session can be terminated. In one embodiment, the system 102 can be configured to terminate the encryption session. After the termination of the encryption session, the system 102 can prevent the data portion of the first data 130 from being transferred to the first AI model 104. Any unauthorized entity or device can be prevented from accessing the first data 130.

[0078] Figures 4A and 4B are sequence diagrams illustrating together a method for enabling access to the services of a first healthcare center according to embodiments of the present disclosure. The description of Figures 4A and 4B will be made in relation to the elements of Figures 1, 2, and 3. Figures 4A and 4B show a sequence diagram 400 illustrating a series of operations 402-426. These operations can be performed by various elements of the network environment 100, including, but are not limited to, system 102, user device 106, and the first electronic healthcare system 110.

[0079] In 402, the health status of user 108 can be monitored. In one embodiment, user device 106 can be configured to monitor the health status of user 108. In one embodiment, the health status can be monitored by configuring a series of sensors 132 associated with user device 106 to periodically collect sensor data sets corresponding to a set of health monitoring parameters of user 108 (such as blood pressure and pulse rate measurements). User device 106 can receive sensor data sets from the series of sensors 132 at predetermined fixed time intervals, such as every minute or every hour.

[0080] In one embodiment, a user device 106, such as a user 108's smartphone, can be configured to collect sensor data sets, including, but not limited to, a series of built-in sensors such as an accelerometer, a PPG sensor, an imaging sensor, a step detection sensor, a step counter sensor, and a microphone.

[0081] In 404, first data 130 related to user 108 can be collected. In one embodiment, system 102 can be configured to collect first data 130 related to user 108. The collected first data 130 may include historical health data and sensor datasets corresponding to a health monitoring parameter set. Historical health data may include, for example, user 108's medical history and recent medical interventions received by user 108. The health monitoring parameter set may be related to at least one of user 108's known health conditions, one or more medical interventions received by user 108 in the past, or one or more comorbidities associated with user 108. For example, the health monitoring parameter set may include information on known health conditions such as asthma, past medical prescriptions, medical examination reports such as X-ray and computed tomography (CT) scans, and images of injuries sustained.

[0082] According to one embodiment, the first data 130 may further include medical history, family history, and lifestyle (PFSH) data. Medical history data may include data relating to one or more of user 108's past illnesses, surgical procedures, medications, or allergies. Family history data may include data relating to one or more of the genetic disorders or diseases that one or more of user 108's family members have suffered from. Lifestyle data may include data relating to one or more of user 108's past and present activities (such as work and marital status). The first data 130 may further include collaboratively filtered data that may include health-related data points relating to a defined population, a specific geographical set, a specific demographic, or a viral infection or spread within a defined population. For example, collaboratively filtered data may include data on a specific bacterial infection that is common in user 108's geographical location. In another example, mosquitoes may be prevalent in user 108's geographical location. Collaboratively filtered data may include information on diseases that may be caused by a specific species of mosquito (such as malaria, chikungunya, or dengue fever).

[0083] According to one embodiment, the first data 130 may further include an appointment schedule for a series of medical or health interventions at one or more healthcare centers, including the first healthcare center 114. For example, user 108 may have acute diabetes. The first data 130 may include information regarding an appointment schedule for routine diabetes checkups at a healthcare center such as the first healthcare center 114.

[0084] In 406, the first AI model 104 can be applied to the collected first data 130 to calculate one or more first indicators. In one embodiment, the system 102 can be configured to apply the first AI model 104 to the collected first data 130 to calculate one or more first indicators that can reflect the deviation of user 108's health status from a reference value. For example, if user 108 is a heart disease patient, the calculated one or more first indicators can reflect the deviation of user 108's blood glucose measurement or cholesterol level. If the blood glucose measurement is "350" and the reference value for blood glucose measurement is "140", the deviation can be determined as "210", i.e., 350-140. In the above example, based on the reflected deviation of the blood glucose measurement, the first indicator can be calculated as 0.97. If the value of the first indicator is close to 1 (such as 0.97), such a value can indicate that the blood glucose measurement (i.e., "350") deviates significantly from the blood glucose reference value (i.e., "140").

[0085] In one embodiment, the first index may correspond to the confidence score or predictive score of the first AI model 104. Specifically, such a value may indicate the confidence of the first AI model 104 in predicting the deviation of blood glucose levels from normal / standard blood glucose levels.

[0086] In 408, first inference data can be generated based on one or more calculated first indicators. In one embodiment, the system 102 can be configured to generate first inference data based on one or more calculated first indicators. The first inference data may include one or more labels or tags related to the cause of the deviation. For example, the first inference data may include the label or tag "high blood sugar" as the cause of a deviation in health status (diabetes).

[0087] In 410, a first requirement can be determined that may necessitate a visit to the first healthcare center 114 by user 108. In one embodiment, the system 102 can be configured to determine a first requirement that may necessitate a visit to the first healthcare center 114 by user 108. The system 102 can determine the first requirement based on the generated first inference data. In some embodiments, the first requirement may correspond to, but is not limited to, a medical emergency, a scheduled health checkup (such as a scheduled appointment), a necessary medical consultation, a scheduled surgical intervention, or an immediate surgical intervention. In an exemplary scenario, the system 102 may determine that the cause of the deviation is diabetes. In such a case, the first requirement may be a medical consultation with an endocrinologist or specialist for the treatment of “diabetes”.

[0088] In 412, an appointment at the first healthcare center 114 can be scheduled for user 108 based on the first requirement. In one embodiment, the system 102 can be configured to schedule an appointment at the first healthcare center 114 for user 108 based on the first requirement. The system 102 can receive confirmation of the appointment schedule via user 108's user device 106.

[0089] In some embodiments, the system 102 can select a first healthcare center 114 based on the user 108's preferences, the user 108's current location, and / or a first requirement. The system 102 can control the user 108's user device 106 to schedule an appointment at the first healthcare center 114. The system 102 can then communicate with the first electronic healthcare system 110 of the first healthcare center 114 to schedule the appointment.

[0090] In 414, a first user-related dataset related to the determined first requirement can be determined. System 102 can be configured to determine the first user-related dataset based on the collected first data and the first inference data. The first user-related dataset may include information about user 108 that may be relevant to the diagnosis and medical treatment of user 108. For example, the first user-related dataset may include user 108's blood glucose measurements (e.g., blood glucose measurements over a week), recorded weight of user 108, and data on any past or current health conditions such as food allergies, genetic diseases, or hereditary disorders. The first user-related dataset may further include personal information such as user 108's name, detailed contact information, and address.

[0091] In 416, the determined first user-related dataset can be transferred to the first electronic healthcare system 110 associated with the first healthcare center 114. In one embodiment, the system 102 can be configured to transfer the first user-related dataset to the first electronic healthcare system 110. In one or more embodiments, the first user-related dataset can be transferred to the first electronic healthcare system 110 before the user 108 visits the first healthcare center 114. For example, the first user-related dataset can be transferred when an appointment is scheduled. In some embodiments, the first user-related dataset can be transferred to the first electronic healthcare system 110 when the user 108 visits the first healthcare center 114. In some medical emergencies, the first user-related dataset can be transferred before or during the user's visit to the first healthcare center 114.

[0092] In some embodiments, system 102 may transfer a first user-related dataset based on synchronization between a first AI model 104 and a second AI model 112 associated with a first electronic healthcare system 110. Synchronization between the first AI model 104 and the second AI model 112 may include transferring the first user-related dataset and the weights of various nodes of the first AI model 104 to the second AI model 112. The first AI model 104 and the second AI model 112 can be retrained based on the first user-related dataset and / or the first data 130.

[0093] In 418, a presentation dataset 210 can be generated. According to one embodiment, the system 102 can be configured to generate the presentation dataset 210 by applying a second AI model 112 to a transferred first user-related dataset. The presentation dataset 210 can be generated based on the preferences of healthcare professionals 212 associated with a first healthcare center 114. The presentation dataset 210 may include data points that healthcare professionals 212 associated with the first healthcare center 114 may need to assess the current health status of user 108 and address the determined first requirements.

[0094] For example, a healthcare professional 212, such as a physician, may require a first user-related dataset in a structured format. For instance, the presentation dataset 210 may include past medical records sorted by the date of the medical record. The presentation dataset 210 may also include a graphical representation of the user's symptoms, as well as health monitoring parameters such as blood glucose and blood pressure readings.

[0095] In 420, the generated presentation dataset 210 can be transferred to the first electronic healthcare system 110. In one embodiment, the system 102 can be configured to transfer the generated presentation dataset 210 to the first electronic healthcare system 110.

[0096] In another embodiment, system 102 can transfer the generated presentation dataset 210 from the first AI model 104 to the second AI model 112. For example, system 102 can initiate an encrypted session between the first AI model 104 and the second AI model 112 to synchronize the first AI model 104 with the second AI model 112. Synchronization between the first AI model 104 and the second AI model 112 may include transferring the presentation dataset 210 and the weights of various nodes in the first AI model 104 to the second AI model 112. The first AI model 104 and the second AI model 112 can then be retrained based on the presentation dataset 210.

[0097] In one embodiment, the second AI model 112 may be a conversational AI hosted on the first electronic healthcare system 110 and may be associated with the first healthcare center 114. For example, the second AI model 112 may be a chatbot accessible to a healthcare professional 212. In such an example, the second AI model 112 may be the primary AI model and the first AI model 104 may be the secondary AI model. The healthcare professional 212 only needs to type, select, or voice queries regarding the health status of user 108. In response, the conversational AI may generate responses to questions that include a portion of the presented dataset 210 in a specific format. In some examples, the conversational AI may receive audio input related to user 108's medical interests. In response, the conversational AI may provide the healthcare professional 212 with a structured copy of the audio input.

[0098] In 422, a display device associated with the first healthcare center 114 can be controlled to display the generated presentation dataset 210. In one embodiment, the system 102 can be configured to control a display device associated with the first healthcare center 114 to display the generated presentation dataset 210. For example, the display device may be a display monitor on which a healthcare worker 212 can view the presentation dataset 210. In another example, the display device may be associated with a user device (such as a smartphone) associated with the healthcare worker 212.

[0099] In 424, medical data 214 related to medical treatment received by user 108 at the first healthcare center 114 can be collected. In one embodiment, system 102 can be configured to collect medical data related to medical treatment received by user 108 at the first healthcare center 114. System 102 can be configured to detect the presence of user 108 at the first healthcare center 114. Based on the detection of user 108's presence, system 102 can collect medical data 214 related to medical treatment received by user 108 at the first healthcare center 114 as part of a determined first requirement. According to one embodiment, system 102 can collect medical data 214 related to medical treatment from a first electronic healthcare system 110 associated with the first healthcare center 114. The medical data 214 may include, for example, test reports, diagnoses, and prescriptions given by healthcare professionals 212.

[0100] In 426, the first AI model 104 can be updated based on the collected medical data 214. In one embodiment, the system 102 can be configured to update the first AI model 104 based on the collected medical data 214. According to one embodiment, the first AI model 104 can be synchronized with the second AI model 112 in order to update the first AI model 104. In some embodiments, the system 102 can initiate an encrypted session between the first AI model 104 and the second AI model 112 to enable synchronization between the first AI model 104 and the second AI model 112. Synchronization between the first AI model 104 and the second AI model 112 may include transferring the collected medical data 214 and the weights of various nodes of the second AI model 112 to the first AI model 104. The first AI model 104 and the second AI model 112 can be retrained based on the collected medical data 214. In one or more embodiments, the second AI model 112 can be a primary AI model, and the first AI model 104 can be a secondary AI model.

[0101] Figures 5A and 5B are sequence diagrams illustrating a method for enabling access to the services of a second healthcare center according to an embodiment of the present disclosure. The description of Figures 5A and 5B will be made in relation to the elements of Figures 1, 2, 3, 4A, and 4B. Figures 5A and 5B show a sequence diagram 500 illustrating a series of operations 502-518. These operations can be performed by various elements of the network environment 100, including, but are not limited to, system 102, user device 106, and second electronic healthcare system 116.

[0102] In 502, the health status of user 108 can be monitored. In one embodiment, user device 106 can be configured to monitor the health status of user 108. In an exemplary scenario, user 108 may arrive at home 202 after receiving medical treatment (based on the first requirement) from a first healthcare center 114. User device 106 can continuously monitor the health status of user 108. Monitoring of user 108's health status is illustrated, for example, in 402 of Figure 4A.

[0103] At 504, first data 130 related to user 108 can be collected. In one embodiment, the system 102 can be configured to collect first data 130 which may include historical health data and a sensor dataset corresponding to a health monitoring parameter set. The collection of first data 130 by the system 102 is illustrated, for example, at 404 in Figure 4A.

[0104] In 506, the first AI model 104 can be applied to the collected medical data 214 and the collected first data 130. According to one embodiment, the system 102 can be configured to apply the first AI model 104 to the collected medical data 214 and the collected first data 130 to generate second inference data. In an exemplary scenario, user 108 may be injured. User 108 can acquire images of the injured part of user 108's body via an imaging device associated with user device 106. The collected first data 130 may include images of such injured part of user 108's body. The generated second inference data may include the label “untreated injury” based on the images of the injury in the first data 130.

[0105] In 508, a second requirement can be determined that may necessitate a visit to a second healthcare center 120 by user 108. In one embodiment, the system 102 can be configured to determine, based on the generated second inference data, a second requirement that may necessitate a visit to a second healthcare center 120 by user 108. The second healthcare center 120 may be different from the first healthcare center 114. For example, the second healthcare center 120 may be a second healthcare center such as a hospital that can specialize in surgical procedures. The second requirement can correspond to a requirement for surgical intervention based on the generated second inference data.

[0106] In 510, a second user-related dataset relating to the second requirement can be determined. In one embodiment, the system 102 can be configured to determine a second user-related dataset relating to the determined second requirement based on the collected first data 130, the collected medical data 214, and the second inference data.

[0107] A second user-related dataset may be required by a second electronic healthcare system 116 associated with a second healthcare center 120. For example, the second user-related dataset may include information about user 108 that is relevant to the assessment of user 108's health status and the treatment of injuries. If user 108 is diabetic, the second user-related dataset may include images of injuries, blood glucose measurements, and blood pressure measurements.

[0108] In 512, the second user-related dataset can be transferred to the second electronic healthcare system 116. In one embodiment, system 102 can be configured to transfer the second user-related dataset to the second electronic healthcare system 116. The second user-related dataset can be transferred to the second electronic healthcare system 116 before user 108 visits the second electronic healthcare system 116. In one implementation, the second user-related dataset can be transferred while user 108 is on their way to the second healthcare center 120, or at the time user 108 visits the second healthcare center 120.

[0109] In one embodiment, system 102 can transfer a second user-related dataset based on synchronization between a first AI model 104 and a third AI model 118 associated with a second electronic healthcare system 116. Synchronization between the first AI model 104 and the third AI model 118 may include transferring the second user-related dataset and the weights of various nodes of the first AI model 104 to the third AI model 118. The first AI model 104 and the third AI model 118 can be retrained based on the second user-related dataset.

[0110] In 514, a presentation dataset 210 can be generated. In one embodiment, the system 102 can be configured to generate the presentation dataset 210. According to one embodiment, the presentation dataset 210 may include a second user-related dataset in a structured form desired by a healthcare professional (who may be a physician or nurse associated with the second healthcare center 120). In another embodiment, the presentation dataset 210 may include suggested symptoms 206 and related tests 208.

[0111] In 516, the generated presentation dataset 210 can be transmitted to the second electronic healthcare system 116. In one embodiment, system 102 can be configured to transmit the generated presentation dataset 210 to the second electronic healthcare system 116. In some embodiments, system 102 can transfer the generated presentation dataset 210 based on synchronization between the first AI model 104 and the third AI model 118. For example, system 102 can initiate an encrypted session between the first AI model 104 and the third AI model 118 to synchronize the first AI model 104 with the third AI model 118. Synchronization between the first AI model 104 and the third AI model 118 may include transferring the presentation dataset 210 and the weights of various nodes of the first AI model 104 to the third AI model 118. In some cases, the first AI model 104 and the third AI model 118 can be retrained with respect to the presentation dataset 210.

[0112] In 518, a display device associated with the second healthcare center 120 can be controlled to display the generated presentation dataset 210. In one embodiment, the system 102 can be configured to control a display device associated with the second healthcare center 120. For example, the display device may be a display monitor on which healthcare professionals can view the presentation dataset 210. In one embodiment, the system 102 may enable the first AI model 104 to be updated based on medical treatment received by user 108 at the second healthcare center 120 through an encrypted session between the first AI model 104 and the third AI model 118. In one or more embodiments, the third AI model 118 may be a primary AI model and the first AI model 104 may be a secondary AI model.

[0113] Traditionally, when user 108 visits a second healthcare center 120 after visiting a first healthcare center 114, the second healthcare center 120 may not have access to complete medical information related to user 108's treatment and health status determined at the first healthcare center 114. This could affect the accuracy of the diagnosis and the effectiveness of the treatment that the second healthcare center 120 can provide to user 108. On the other hand, the presented dataset 210 can include medical data 214 from the first healthcare center 114 that user 108 has visited in the past. Therefore, healthcare professionals at the second healthcare center 120 can provide user 108 with an accurate diagnosis and treatment. For example, healthcare professionals at the second healthcare center 120 can use the diagnosis and treatment of "diabetes" performed by healthcare professionals 212 at the first healthcare center 114 to surgically treat user 108's physical injuries. Because the bodies of diabetic patients, such as User 108, may take longer to heal injuries compared to non-diabetic patients, healthcare professionals can make more accurate diagnoses based on User 108's past medical history.

[0114] Figures 6A and 6B are sequence diagrams illustrating a method for scheduling an emergency response (ER) service according to an embodiment of the present disclosure. The description of Figures 6A and 6B is made in relation to the elements of Figures 1, 2, 3, 4A, 4B, 5A, and 5B. Figures 6A and 6B show a sequence diagram 600 illustrating a series of operations 602-616. These operations can be performed by various elements of the network environment 100, including, but are not limited to, system 102, user device 106, and second electronic healthcare system 116.

[0115] In 602, a first requirement for responding to a medical emergency can be determined. In one embodiment, the system 102 can be configured to determine a first requirement for responding to a medical emergency. For example, the first inference data may indicate a heart attack based on parameters such as the user's electrocardiogram (ECG) signal pattern and pulse rate. In the case of a heart attack, the system 102 can determine that the first requirement is an emergency surgical procedure.

[0116] In 604, a notification can be sent to confirm the determined medical emergency. In one embodiment, the system 102 can be configured to send a notification to the user device 106 to confirm the determined medical emergency. Examples of notifications include, but are not limited to, a pop-up alert, a text message, an alarm, or a call to the user device 106 related to user 108.

[0117] In 606, confirmation of a medical emergency can be received from the user device 106. In one embodiment, the system 102 can be configured to receive confirmation of a medical emergency from the user device 106. If the medical emergency is determined to be inaccurate or erroneous, the system 102 can receive input from the user device 106 to dismiss the medical emergency. For example, user 108 may only be experiencing a mild pain in their heart that does not require immediate medical assistance. This can help user 108 avoid paying unnecessary costs associated with emergency services, such as ambulance fees.

[0118] In 608, the current location of user 108 can be determined. In one embodiment, system 102 can be configured to determine the current location of user 108 based on confirmation of a medical emergency. System 102 can determine the current location of user 108 using a sensor dataset which may include location data received from a location sensor (such as a satellite-based location receiver). In one embodiment, confirmation received from user device 106 may include information related to the current location of user 108. For example, confirmation may include the latitude and longitude coordinates or GPS coordinates of user device 106 related to user 108. The current location of user 108 may be user 108's home 202, or any other location different from home 202. For example, user 108 may be out of town on vacation, or at a market, a friend's house, or an office.

[0119] In 610, a second healthcare center 120 can be determined based on the user 108's current location and the determination that the first requirement corresponds to a medical emergency. In one embodiment, the system 102 can be configured to determine the second healthcare center 120 based on the current location and the medical emergency.

[0120] System 102 can determine the second healthcare center 120 based on the services it provides, as well as other factors such as the distance between the home 202 and the second healthcare center 120, and the availability of (one or multiple) healthcare professionals at the second healthcare center 120 to meet the user 108's requirements. For example, a medical emergency such as a heart attack may require immediate medical treatment, such as surgery. System 102 can determine the closest healthcare center to the user 108's home 202 that can specialize in cardiac treatment, including emergency cardiac procedures.

[0121] In 612, an ER service 126 can be scheduled. In one embodiment, system 102 can schedule an ER service 126 (such as an ambulance) based on confirmation of a received medical emergency. In one embodiment, system 102 can wait for a predetermined period of time starting from the time a notification is sent to user device 106. If no user response or input is received within the predetermined period, system 102 can schedule an ER service 126. This response can be taken to provide immediate care to user 108 in case user 108 is unresponsive. In some cases, user 108 may be unresponsive, unconscious, injured, or paralyzed. In such cases, system 102 can schedule an ER service 126 after the initial predetermined period (a few minutes).

[0122] In 614, alert notifications can be sent to one or more devices registered to receive alert notifications. In one embodiment, system 102 can be configured to send alert notifications to one or more devices that can be registered to receive alert notifications. Sending an alert notification may be based on the determination that the first requirement corresponds to a medical emergency. For example, one or more devices may relate to one or more family members, relatives, friends, or acquaintances (such as neighbors) of user 108. One or more devices may be mobile phones related to user 108's family, relatives, friends, or acquaintances. Examples of alert notifications include, but are not limited to, pop-up notifications, text messages, alarms, or calls to one or more devices. Thus, system 102 can provide user 108's family, relatives, friends, or acquaintances with timely alert notifications regarding user 108's medical emergency. As a result, user 108 can receive prompt assistance while experiencing a medical emergency.

[0123] In 616, the first user-related dataset can be transferred to the second electronic healthcare system 116. In one embodiment, the system 102 can be configured to transfer the first user-related dataset to the second electronic healthcare system 116 associated with the second healthcare center 120 based on a scheduled ER service 126. The first user-related dataset can be transferred to the second electronic healthcare system 116 while the ambulance is en route to the second healthcare center 120. In some embodiments, the first AI model 104 can be synchronized with a third AI model 118 to transfer the first user-related dataset. In such cases, the third AI model 118 can be the primary AI model and the first AI model 104 can be the secondary AI model. As a result, the second healthcare center 120 can provide medical treatment to user 108 as soon as user 108 arrives at the second healthcare center 120. The first user-related dataset may include relevant information (related to user 108), such as user 108's pulse rate measurements and user 108's personal details. In some embodiments, the first user-related dataset may also include real-time or near-real-time information regarding user 108's vital signs.

[0124] Figures 7A and 7B are sequence diagrams illustrating a method for determining one or more recommendations for a healthcare center according to embodiments of the present disclosure. The description of Figures 7A and 7B is made in relation to the elements of Figures 1, 2, 3, 4A, 4B, 5A, 5B, 6A, and 6B. Figures 7A and 7B show a sequence diagram 700 illustrating a series of operations 702-716. These operations can be performed by various elements of the network environment 100, including, but are not limited to, system 102, user device 106, and first electronic healthcare system 110.

[0125] In 702, a first requirement can be determined that may necessitate user 108 visiting a healthcare center. In one embodiment, system 102 can be configured to determine a first requirement that may necessitate user 108 visiting a healthcare center. For example, user 108 may be experiencing fatigue and headaches. One or more first indicators (determined by the first AI model 104) may indicate user 108 having an irregular pulse and a drop in blood pressure. In such a case, based on the first inference data, the first requirement can be determined as an immediate medical intervention for user 108.

[0126] In 704, the current location of user 108 can be determined. In one embodiment, the system 102 can be configured to determine the current location of user 108 based on a sensor dataset (included in the collected first data 130). The determination of user 108's current location is illustrated, for example, in Figure 6. In an exemplary scenario, user 108's current location may differ from the location of home 202. For example, user 108 may be traveling or located in a different country, state, or region.

[0127] In 706, one or more recommendations can be determined. Such recommendations may include one or more healthcare centers related to the first requirement determined. One or more healthcare centers may be located within a threshold distance from the user 108's current location. For example, one or more healthcare centers may be located within a threshold distance of 500 meters from the user 108's current location.

[0128] According to one embodiment, the system 102 can be configured to determine one or more recommendations by using a first AI model 104. Factors that may influence the determination of one or more recommendations include, for example, the user's preferences regarding healthcare centers, the user's preferences regarding healthcare professionals, the user's visit history to a particular healthcare center, the cost of consultations and treatments at one or more healthcare centers, and the user's current location.

[0129] In 708, one or more determined recommendations can be transmitted to the user device 106. In one embodiment, the system 102 can be configured to transmit one or more determined recommendations to the user device 106.

[0130] In 710, the user device 106 can be controlled to display one or more of the determined recommendations. In one embodiment, the system 102 can be configured to control the user device 106 to display one or more of the determined recommendations. The one or more of the determined recommendations can be displayed, for example, on the display screen of the user's smartphone.

[0131] In 712, a first input can be received from the user device 106. In one embodiment, the system 102 can be configured to receive the first input via the user device 106. The first input may include a first selection of a first healthcare center 114 from among one or more healthcare centers. The first input may further include a second selection of a schedule for booking the first healthcare center 114. In one embodiment, the system 102 can control the user device 106 to display different slots available for booking the selected first healthcare center 114. The user 108 can simply select a slot available at the selected first healthcare center 114 through the first input. The selected slot is included in the selected schedule.

[0132] In 714, a visit to the first healthcare center 114 can be scheduled based on the first input received. In one embodiment, the system 102 can be configured to schedule a visit to the first healthcare center 114 for user 108.

[0133] In 716, a first user-related dataset can be transferred to the first electronic healthcare system 110 based on scheduled visits. In one embodiment, the system 102 can be configured to transfer the first user-related dataset to the first electronic healthcare system 110. The first user-related dataset may include a time series of data on a health monitoring parameter set and related information such as personal details of user 108.

[0134] Figure 8 is a sequence diagram illustrating a method for a virtual reality (VR)-based medical consultation session according to an embodiment of the present disclosure. The description of Figure 8 is made in relation to the elements of Figures 1, 2, 3, 4A, 4B, 5A, 5B, 6A, 6B, 7A, and 7B. Figure 8 shows a sequence diagram 800 illustrating a series of operations 804-810. These operations can be performed by various elements of the network environment 100, including, but are not limited to, system 102, user device 106, first electronic healthcare system 110, and wearable electronic device 802.

[0135] In 804, a request to authorize a virtual reality (VR)-based consultation session can be sent to the first electronic healthcare system 110. According to one embodiment, system 102 can be configured to send a request to the first electronic healthcare system 110 to authorize a VR-based consultation session. The request may be sent based on a determination that the current location of user 108 is different from the location of the first healthcare center 114. For example, user 108 may be at home 202 and need a VR-based consultation due to a leg injury. In another example, user 108 may be an elderly person for whom it is inconvenient to visit the first healthcare center 114. Such an elderly person may need a VR-based medical consultation.

[0136] In 806, permission can be received for a transmitted request. According to one embodiment, system 102 can be configured to receive permission from the first electronic healthcare system 110 for a transmitted request. Permission from the first electronic healthcare system 110 for a VR-based consultation session may be necessary to prevent unauthorized or malicious entities or devices from connecting to the VR-based consultation session.

[0137] In 808, a VR-based medical consultation session can be established between the user device 106 and the wearable electronic device 802. In one embodiment, the system 102 can be configured to establish a VR-based consultation session between the user device 106 and the wearable electronic device 802 based on received authorization. In one embodiment, a healthcare professional 212 at the first healthcare center 114 can wear and operate the wearable electronic device 802. In one embodiment, the user device 106 may include one or more imaging devices (such as cameras) that can be used for the VR-based consultation.

[0138] In 810, the first user-related dataset can be transferred to the wearable electronic device 802. According to one embodiment, the system 102 can be configured to transfer a determined first user-related dataset to the wearable electronic device 802 when a VR-based medical consultation session is active. The first user-related dataset may include relevant information such as images of the user 108's injury, data on health monitoring parameters, and personal details of the user 108.

[0139] The wearable electronic device 802 can be configured to receive a first user-related dataset and a video feed or 3D avatar of user 108. While the session is active, the wearable electronic device 802 can display the first user-related dataset via UI elements or widgets. User 108 can also interact with the medical professional 212 through a video feed (or 3D avatar) that the wearable electronic device 802 can display, or indicate at least a part of the body that requires medical treatment. For example, the wearable electronic device 802 can display user 108's injured leg. Examples of the wearable electronic device 802 include, but are not limited to, smart glasses, virtual reality (VR) based head-mounted devices, and augmented reality (AR) based head-mounted devices. In some embodiments, system 102 can establish a video consultation session as a VR-based consultation session between user device 106 and the first electronic healthcare system 110. The wearable electronic device 802 can be connected to a first electronic healthcare system 110 to provide medical treatment to the user 108.

[0140] Figure 9 shows a master-slave configuration of multiple AI models according to an embodiment of the present disclosure. The description of Figure 9 will be made in reference to Figures 1, 2, 3, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B, and 8. Figure 9 shows Figure 900. Figure 900 may include a first AI model 104. The first AI model 104 may be associated with a user device 106. Figure 900 may further include a second AI model 112 associated with a first healthcare center 114, a third AI model 118 associated with a second healthcare center 120, and a fourth AI model 124 associated with an ER service 126.

[0141] The first AI model 104, the second AI model 112, the third AI model 118, and the fourth AI model 124 can be implemented in a master-slave configuration. For example, the first AI model 104 can be a master AI model that can be configured to control or organize the operations of slave AI models such as the second AI model 112, the third AI model 118, and the fourth AI model 124 to perform their operations.

[0142] In an exemplary scenario, the first AI model 104 can transfer a first user-related dataset to the second AI model 112 based on the presence of a user 108 in the first healthcare center 114. The first AI model 104 can control the second AI model 112 to generate a presentation dataset 210. The first AI model 104 can further control the second AI model 112 to receive medical data 214 from the second AI model 112. In one embodiment, the first AI model 104 can control the second AI model 112 to function as a chatbot accessible to a healthcare professional 212.

[0143] The first AI model 104 can transfer a second user-related dataset to the third AI model 118 based on the presence of user 108 at the second healthcare center 120. The first AI model 104 can control the third AI model 118 to generate a presentation dataset 210 for healthcare professionals associated with the second healthcare center 120. The first AI model 104 can further control the third AI model 118 to receive medical data 214 from the third AI model 118.

[0144] In an exemplary scenario, system 102 can determine a first requirement for responding to a medical emergency. Details of determining the first requirement for responding to a medical emergency are further illustrated, for example, in Figure 6A, 604. Based on the determined first requirement for responding to a medical emergency, the first AI model 104 can transmit a first user-related dataset to a fourth AI model 124 associated with the ER service 126. The first AI model 104 can control the fourth AI model 124 to generate a presentation dataset 210 before the user 108 arrives at the second healthcare center 120, so that healthcare workers have the necessary presentation dataset 210.

[0145] Figures 10A and 10B are sequence diagrams showing a combined set of operations between multiple AI models according to embodiments of the present disclosure. The description of Figures 10A and 10B is made in relation to the elements of Figures 1, 2, 3, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B, 8, and 9. Figures 10A and 10B show sequence diagram 1000, which shows a series of operations 1002-1024. These operations can be performed by various entities, including, but are not limited to, a first AI model 104, a second AI model 112, a third AI model 118, and a fourth AI model 124.

[0146] In 1002, the first AI model 104 can be applied to the collected first data 130 to calculate one or more first indicators. Such indicators can reflect the deviation of the user's 108 health status from a baseline. Further details on the calculation of one or more first indicators are shown, for example, in 406 of Figure 4A.

[0147] In 1004, the first user-related dataset can be transferred to the second AI model 112. According to one embodiment, the first AI model 104 can transfer the first user-related dataset to the second AI model 112 based on synchronization between the first AI model 104 and the second AI model 112. Further details of the transfer of the first user-related dataset are shown, for example, in 416 of Figure 4B.

[0148] In 1006, the presentation dataset 210 can be generated. According to one embodiment, the second AI model 112 can be configured to generate the presentation dataset 210. For example, the first AI model 104 can control the second AI model 112 to generate the presentation dataset 210. Further details on the generation of the presentation dataset 210 are shown, for example, in 418 of Figure 4B.

[0149] In 1008, the first AI model 104 can be updated. According to one embodiment, the second AI model 112 can transmit medical data 214 to the first AI model 104. For example, the second AI model 112 can transmit medical data 214 to the first AI model 104 based on synchronization between the first AI model 104 and the second AI model 112. Further details of updating the first AI model 104 are shown, for example, in 426 of Figure 4B.

[0150] In 1010, second inference data can be generated. According to one embodiment, the system 102 can be configured to generate second inference data by applying the first AI model 104 to the collected medical data 214 and the collected first data 130. Further details of the generation of the second inference data are shown, for example, in 506 of Figure 5A.

[0151] In step 1012, a second user-related dataset can be transferred to the third AI model 118. The second user-related dataset may include relevant data from the first data 130 and medical data 214 received by the first AI model 104. The second user-related dataset may be required by the second electronic healthcare system 116 associated with the second healthcare center 120. Further details regarding the transfer of the second user-related dataset are shown, for example, in Figure 5A, 510.

[0152] In 1014, the presentation dataset 210 can be generated. According to one embodiment, the third AI model 118 can be configured to generate the presentation dataset 210. For example, the first AI model 104 can control the third AI model 118 to generate the presentation dataset 210. Details of the generation of the presentation dataset 210 by the third AI model 118 are further shown, for example, in 514 of Figure 5B.

[0153] In 1016, the first AI model 104 can be updated. According to one embodiment, the third AI model 118 can transmit medical data 214 to the first AI model 104 based on synchronization between the first AI model 104 and the third AI model 118. The medical data 214 can be generated based on diagnoses or treatments performed by healthcare professionals at the second healthcare center 120. This update can correspond to a process of training the first AI model 104 with respect to the medical data 214.

[0154] In 1018, a first requirement for responding to a medical emergency can be determined. According to one embodiment, the first AI model 104 can be configured to determine a first requirement for responding to a medical emergency. For example, in the case of a paralytic seizure, the first AI model 104 can determine that the first requirement is a medical emergency.

[0155] In 1020, a second healthcare center 120 can be determined. According to one embodiment, the first AI model 104 can be configured to determine the second healthcare center 120. For example, the first AI model 104 can determine the second healthcare center 120 based on the location of the user 108, as well as other factors such as the availability of (one or multiple) healthcare professionals at the second healthcare center 120 and the type of services offered at the second healthcare center 120. Further details of the determination of the second healthcare center 120 are shown, for example, in 610 of Figure 6A.

[0156] In 1022, ER services 126 can be scheduled. According to one embodiment, the first AI model 104 can be configured to schedule ER services 126 based on a determined medical emergency. Further details of scheduling ER services 126 are shown, for example, in 612 of Figure 6B.

[0157] In 1024, the first user-related dataset can be transferred to the second AI model 112 associated with the second electronic healthcare system 116. In one embodiment, the first AI model 104 can be configured to transfer the first user-related dataset to the second AI model 112 associated with the second electronic healthcare system 116 based on a scheduled ER service 126. For example, the first AI model 104 can transfer the first user-related dataset to the fourth AI model 124 based on synchronization between the first AI model 104 and the fourth AI model 124. Further details regarding the transfer of the first user-related dataset are shown, for example, in 616 of Figure 6B.

[0158] Figure 11 shows an exemplary determination of a first requirement using a first AI model according to an embodiment of the present disclosure. The description of Figure 11 is made in relation to the elements of Figures 1, 2, 3, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B, 8, 9, 10A, and 10B. Figure 11 shows Figure 1100, which may include a first AI model 104.

[0159] System 102 can apply a first AI model 104 to the first data 130 to calculate one or more first indicators 1102. In an exemplary scenario, the first data 130 may include the user 108's past health data. For example, user 108 may be an asthma patient. The first data 130 may further include a sensor dataset. For example, the sensor dataset may include recorded blood pressure measurements, including the current blood pressure measurement (e.g., 124 / 81 mmHg). The sensor dataset may include recorded pulse rate measurements (e.g., 84 bpm, which is the current pulse rate). The sensor dataset may further include user 108's weight and current fasting blood glucose measurement (e.g., 350). The first data 130 may further include PFSH data related to user 108. For example, the PFSH data may include that user 108 is lactose intolerant and that user 108's parents (e.g., father) have a history of diabetes. System 102 can input the first data 130 into the first AI model 104.

[0160] The first AI model 104 can calculate one or more first indicators 1102 based on the input first data 130. In an exemplary scenario, one or more first indicators 1102 may include a first indicator corresponding to "hyperglycemia" with a confidence score of "0.97". The first AI model 104 can determine a first indicator corresponding to "hyperglycemia" based on a fasting blood glucose measurement 350, which may be a significant deviation from the reference value of the fasting blood glucose measurement. One or more first indicators 1102 may include a second indicator corresponding to "hypertension" with a confidence score of "0.32". For example, the first AI model 104 can determine a second indicator corresponding to "hypertension" based on a blood pressure measurement of "124 / 81 mmHg", which may be a small deviation from the reference value of the blood pressure measurement "120 / 80 mmHg". Note that the confidence score for the second indicator ("hypertension") is "0.32", so the second indicator can be ignored.

[0161] System 102 can generate first inference data 1104 based on a first indicator corresponding to hyperglycemia. The first inference data 1104 may include one or more labels or tags related to the cause of the deviation in health status. For example, the first inference data 1104 may include the cause as "diabetes". System 102 can further determine a first requirement 1106 based on the first inference data 1104. In some embodiments, the first requirement 1106 may correspond to a medical consultation, a medical emergency, a scheduled medical examination, or a scheduled surgical procedure. Based on the determination that the first inference data 1104 is a non-emergency situation, System 102 may determine the first requirement 1106 as "medical consultation". Based on the user 108's preferences, System 102 can book a medical consultation at a healthcare center, such as a first healthcare center 114.

[0162] Figure 12 is a block diagram of a system enabling artificial intelligence (AI)-based access to healthcare services according to an embodiment of the present disclosure. The description of Figure 12 will be made in relation to the elements of Figures 1, 2, 3, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B, 8, 9, 10A, 10B, and 11. Figure 12 shows a block diagram 1200 of system 1202, which can be similar to system 102 in Figure 1. System 1202 may include a processor 1204, memory 1206, input / output (I / O) device 1208, a set of sensors 1210, and a network interface 1212. The set of sensors 1210 can be similar to the set of sensors 132 in Figure 1. Therefore, for brevity, the description of the set of sensors 1210 is omitted here.

[0163] The processor 1204 may include preferred logic, circuitry, and / or interfaces that can be configured to execute instruction sets stored in memory 1206. The processor 1204 may be configured to execute program instructions related to different operations performed by system 102. For example, part of an operation could include receiving collected first data 130, calculating one or more first indicators by applying a first AI model 104 to the collected first data 130, generating first inference data, and determining first requirements which may require a user to visit a first healthcare center 114. The processor 1204 may be further configured to determine a first user-related dataset and transfer the determined first user-related dataset to a first electronic healthcare system 110. The processor 1204 may be implemented based on several processor technologies well known in the art. Examples of processor technologies include, but are not limited to, central processing units (CPUs), x86-based processors, reduced instruction set computing (RISC) processors, application-specific integrated circuit (ASIC) processors, composite instruction set computing (CISC) processors, graphical processing units (GPUs), and other processors.

[0164] Memory 1206 may include preferred logic, circuitry, and interfaces that can be configured to store one or more instructions executed by the processor 1204. Memory 1206 may be configured to store collected first data 130, one or more first indicators, first inference data, first requirements, and a first user-related dataset. Memory 1206 may be further configured to store presentation dataset 210 and medical data 214. Memory 1206 may be further configured to store second inference data, second requirements, and a second user-related dataset. Examples of implementations of memory 1206 include, but are not limited to, random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), hard disk drive (HDD), solid-state drive (SSD), CPU cache, and / or secure digital (SD) card.

[0165] The I / O device 1208 may include suitable logic, circuitry, and interfaces that can be configured to receive input from user 108 and provide output based on the received input. For example, the input may respond to a request to share a data portion of collected first data 130 with a first AI model 104. The input may further respond to a first input including a first selection of a first healthcare center from one or more healthcare centers and a second selection of a schedule for an appointment at the first healthcare center 114. The output may include, for example, recommendations for one or more healthcare centers. The I / O device 1208, which may include various input and output devices, may be configured to communicate with the processor 1204. Examples of the I / O device 1208 include, but are not limited to, a touch screen, keyboard, mouse, joystick, microphone, display screen, and speaker.

[0166] The network interface 1212 may include suitable logic, circuits, interfaces, and / or code that can be configured to facilitate communication between the processor 1204, the first electronic healthcare system 110, the second electronic healthcare system 116, the third electronic healthcare system 122, and the server 128. The network interface 1212 may be implemented using various known techniques to support wired or wireless communication between the system 102 and the communication network 134. The network interface 1212 may include, but is not limited to, an antenna, a radio frequency (RF) transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a coder-decoder (CODEC) chipset, a subscriber ID module (SIM) card, or a local buffer circuit. The network interface 1212 may be configured to communicate wirelessly with networks such as the Internet, an intranet, or wireless networks such as cellular telephone networks, wireless local area networks (LANs), and metropolitan area networks (MANs). Wireless communication can be configured to use one or more of several communication standards, protocols, and technologies, such as Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), Wideband Code Division Multiple Access (W-CDMA), Long-Term Evolution (LTE), Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Bluetooth, Wireless Fidelity (WiFi) (such as IEEE 802.11a, IEEE 802.11b, IEEE 802.11g, or IEEE 802.11n), Voice over Internet Protocol (VoIP), Light Fidelity (Li-Fi), or Worldwide Interoperability for Microwave Access (Wi-MAX), Email Protocol, Instant Message, and Short Message Service (SMS).

[0167] The functions or operations performed by the system 102, as described in Figure 1, can be performed by the processor 1204. The operations performed by the processor 1204 are described in detail, for example, in Figures 2, 3, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B, 8, 9, 10A, 10B, and 11.

[0168] Figure 13 is a flowchart illustrating an exemplary method for artificial intelligence (AI) enabled access to healthcare services according to embodiments of the present disclosure. The description of Figure 13 is made in relation to the elements of Figures 1, 2, 3, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B, 8, 9, 10A, 10B, 11, and 12. Figure 13 shows flowchart 1300. The exemplary method of flowchart 1300 can be executed by either a computer system, for example, system 102 in Figure 1 or processor 1204 in Figure 12. The exemplary method of flowchart 1300 can be started at 1302 and proceeded to 1304.

[0169] In 1304, first data 130 related to user 108 can be collected. According to one embodiment, processor 1204 can be configured to collect first data 130 related to user 108. The collected first data 130 may include historical health data and sensor datasets corresponding to a health monitoring parameter set. Further details of the collection of the first data 130 are shown, for example, in Figure 2.

[0170] In 1306, the first AI model 104 can be applied to the collected first data 130 to calculate one or more first indicators that can reflect the deviation of the user 108's health status from a baseline. According to one embodiment, the processor 1204 can apply the first AI model 104 to the collected first data 130 to calculate one or more first indicators. One or more first indicators can reflect the deviation of the user 108's health status from a baseline. Details of the calculation of one or more first indicators are further shown, for example, in Figure 4A.

[0171] In 1308, based on one or more calculated first indicators, first inference data can be generated which may include one or more labels or tags related to the cause of the deviation. According to one embodiment, the processor 1204 can be configured to generate first inference data which may include one or more labels or tags related to the cause of the deviation, based on one or more calculated first indicators. Further details of the generation of the first inference data are shown, for example, in Figure 4A.

[0172] In 1310, based on the generated first inference data, a first requirement can be determined that may necessitate a user 108 visiting the first healthcare center 114. According to one embodiment, the processor 1204 can be configured to determine, based on the generated first inference data, a first requirement that may necessitate a user 108 visiting the first healthcare center 114. Further details of determining the first requirement are shown, for example, in Figure 4A.

[0173] In 1312, a first user-related dataset relating to the determined first requirement can be determined based on the collected first data 130 and the first inference data. According to one embodiment, the processor 1204 can be configured to determine a first user-related dataset relating to the determined first requirement based on the collected first data 130 and the first inference data. Further details on the determination of the first user-related dataset are shown, for example, in Figure 4B.

[0174] In 1314, the determined first user-related dataset can be transferred to the first electronic healthcare system 110 associated with the first healthcare center 114. According to one embodiment, the processor 1204 can be configured to transfer the determined first user-related dataset to the first electronic healthcare system 110 associated with the first healthcare center 114. Details of the transfer of the first user-related dataset are further shown, for example, in Figure 4B. Control can then proceed to termination.

[0175] While flowchart 1300 shows discrete operations such as 1304, 1306, 1308, 1310, 1312, and 1314, the disclosure is not limited in this way. Accordingly, in some embodiments, such discrete operations can be further divided into further operations, combined into fewer operations, or deleted without impairing the essence of the disclosed embodiments, depending on the particular implementation.

[0176] Various embodiments of this disclosure can provide a non-temporary computer-readable medium and / or storage medium storing computer-executable instructions that can be executed by machines and / or computers in a system (e.g., system 102). These instructions can cause machines and / or computers in the system (e.g., system 102) to perform actions that may include collecting first data 130 related to user 108. The collected first data 130 may include historical health data and sensor datasets corresponding to a health monitoring parameter set. The action may further include applying a first AI model 104 to the collected first data 130 to calculate one or more first indices that can reflect the deviation of user 108's health status from a baseline. The action may further include generating first inference data based on the calculated one or more first indices, which may include one or more class labels or tags related to the cause of the deviation. The action may further include determining first requirements that may necessitate user 108 visiting a first healthcare center 114 based on the generated first inference data. The operation may further include determining a first user-related dataset related to a determined first requirement, based on the collected first data and first inference data. The operation may further include transferring the determined first user-related dataset to a first electronic healthcare system 110 associated with a first healthcare center 114.

[0177] Exemplary aspects of this disclosure may include a system (such as system 102) which may include a processor (such as processor 1204). The processor 1204 may be configured to collect first data 130 related to user 108. The collected first data 130 may include historical health data and a sensor dataset corresponding to a health monitoring parameter set. The processor 1204 may be further configured to apply a first AI model 104 to the collected first data 130 to calculate one or more first indices which can reflect the deviation of user 108's health status from a baseline. The processor 1204 may be further configured to generate first inference data based on the calculated one or more first indices which may include one or more class labels or tags related to the cause of the deviation. The processor 1204 may be further configured to determine first requirements which may necessitate user 108 visiting a first healthcare center 114 based on the generated first inference data. The processor 1204 can be further configured to determine a first user-related dataset related to the determined first requirement, based on the collected first data and first inference data. The processor 1204 can be further configured to transfer the determined first user-related dataset to a first electronic healthcare system 110 related to a first healthcare center 114.

[0178] According to one embodiment, the collected first data 130 may further include medical history, family history and lifestyle (PFSH) data, as well as collaboratively filtered data which may include health-related data points associated with a defined population, a specific geographic set, a specific demographic, or viral infection or spread within a defined population.

[0179] According to one embodiment, the collected first data 130 may further include an appointment schedule for a series of medical or health interventions at one or more healthcare centers, including a first healthcare center 114. The first requirement can be determined based on the appointment schedule.

[0180] According to one embodiment, the health monitoring parameter set may be related to at least one of the following: the user 108's known health status, one or more medical interventions the user 108 has received in the past, or one or more comorbidities associated with the user 108.

[0181] According to one embodiment, the processor 1204 may be further configured to determine the current location of the user 108. The processor 1204 may be further configured to determine one or more recommendations, which may include one or more healthcare centers related to the determined first requirement, using a first AI model 104. The processor 1204 may be further configured to control the user device 106 associated with the user 108 to display the determined one or more recommendations. The one or more healthcare centers may be located within a threshold distance from the current location.

[0182] According to one embodiment, the processor 1204 may be further configured to receive a first input via a user device 106. The first input may include a first selection of a first healthcare center 114 from among one or more healthcare centers, and a second selection of a schedule for making an appointment at the first healthcare center 114. The processor 1204 may be further configured to schedule a visit to the first healthcare center 114 based on the received first input. The first user-related dataset may be transferred to the first electronic healthcare system 110 based on the schedule.

[0183] According to one embodiment, the processor 1204 may be further configured to send a request to the first electronic healthcare system 110 to authorize a virtual reality (VR)-based consultation session based on a determination that the user's 108 current location is different from the location of the first healthcare center 114. The processor 1204 may be further configured to receive authorization for the transmitted request and, based on the received authorization, establish a VR-based consultation session between the user device 106 and a wearable electronic device worn by a healthcare professional 212 at the first healthcare center 114. While the VR-based consultation session may be active, the determined first user-related dataset may be transferred to the wearable electronic device.

[0184] According to one embodiment, the processor 1204 can be further configured to generate a presentation dataset 210 by applying a second AI model 112 to a transferred first user-related dataset. The presentation dataset 210 may include data points that a healthcare professional 212 associated with the first healthcare center 114 may need to assess the current health status of the user 108 and address the determined first requirements. The processor 1204 can be further configured to control a display device associated with the first healthcare center 114 to display the generated presentation dataset 210.

[0185] According to one embodiment, the second AI model 112 may be an interactive AI hosted on the first electronic healthcare system 110 and may be associated with the first healthcare center 114.

[0186] According to one embodiment, the processor 1204 may be further configured to detect the presence of user 108 at a first healthcare center 114. Based on this detection, the processor 1204 may be further configured to collect medical data 214 related to medical treatments received by user 108 at the first healthcare center 114 as part of a determined first requirement, and to update the first AI model 104 based on the collected medical data 214.

[0187] According to one embodiment, the processor 1204 can be further configured to apply a first AI model 104 to the collected medical data 214 and the collected first data 130 to generate second inference data. Based on the generated second inference data, the processor 1204 can be further configured to determine a second requirement that may necessitate a user 108 visiting a second healthcare center 120 that is different from the first healthcare center 114. Based on the collected first data 130, the collected medical data 214, and the second inference data, the processor 1204 can be further configured to determine a second user-related dataset that may be relevant to the determined second requirement and may be required by a second electronic healthcare system 116 associated with the second healthcare center 120. The processor 1204 can be further configured to transfer the determined second user-related dataset to the second electronic healthcare system 116.

[0188] According to one embodiment, the processor 1204 may be further configured to determine a second healthcare center 120 based on the current location of the user 108 and a determination that the determined first requirement can respond to a medical emergency. The processor 1204 may be further configured to schedule an ER service 126. Based on the scheduled ER service 126, the processor 1204 may be further configured to transfer the first user-related dataset to a second electronic healthcare system 116 associated with the second healthcare center 120.

[0189] According to one embodiment, the processor 1204 may be further configured to send alert notifications to one or more devices registered to receive alert notifications, based on a determination that the first requirement can respond to a medical emergency.

[0190] According to one embodiment, the processor 1204 may be further configured to receive a request from the user device 106 to share a data portion of the collected first data 130 with the first AI model 104. Based on this request, the processor 1204 may be further configured to establish an encrypted session between the first AI model 104 and the user device 106. While the encrypted session may be active, the processor 1204 may be further configured to transfer a data portion of the collected first data 130 to the first AI model 104. The processor 1204 may be further configured to store the transferred data portion in an encrypted form in a data store.

[0191] This disclosure can be implemented in hardware or in a combination of hardware and software. This disclosure can be implemented centrally within at least one computer system or in a distributed manner, where different elements are distributed across multiple interconnected computer systems. Computer systems or other devices adapted to perform the methods described herein may be suitable. The hardware-software combination may be a general-purpose computer system including a computer program that, when loaded and executed, can control the computer system to perform the methods described herein. This disclosure can also be implemented in hardware, including a portion of an integrated circuit that also performs other functions.

[0192] This disclosure includes all features that enable the implementation of the methods described herein and can be incorporated into a computer program product that can perform these methods when loaded onto a computer system. In this context, a computer program means any expression in any language, code, or notation of an instruction set intended to be executed directly, or after either a) conversion to another language, code, or notation, or b) reproduction in a different content form, on a system having information processing capabilities.

[0193] While this disclosure has been described with reference to several embodiments, those skilled in the art will understand that various modifications can be made and equivalents can be substituted without departing from the scope of this disclosure. Furthermore, many modifications can be made without departing from the scope of this disclosure to suit specific circumstances or content to the teachings of this disclosure. Accordingly, this disclosure is not limited to the specific embodiments disclosed, but is intended to include all embodiments that fall within the scope of the appended claims.

Claims

1. A method performed by a computer, Collecting first data related to the user, which includes past health data and a sensor dataset corresponding to a health monitoring parameter set, Applying a first artificial intelligence (AI) model to the collected first data to calculate one or more first indicators that reflect the deviation of the user's health status from a reference value, Based on the calculated one or more first indicators, first inference data is generated that includes one or more labels or tags related to the cause of the deviation. Based on the generated first inference data, determine the first requirement which requires the user to visit the first healthcare center for the first requirement, Based on the collected first data and the first inference data, determine a first user-related dataset related to the determined first requirement, Transferring the determined first user-related dataset to the first electronic healthcare system associated with the first healthcare center, To detect the presence of the user in the first healthcare center, Based on the detection, collect medical data related to the medical treatment received by the user at the first healthcare center as part of the determined first requirement, and update the first AI model based on the collected medical data and the first inference data so that the first AI model outputs the collected medical data as the correct result. Includes, The first AI model includes a trained model that takes the first data as input and outputs one or more first indicators, and a trained model that takes the medical data and the first data as input and outputs second inference data. The method is characterized in that the first user-related dataset includes information about the user related to the user's diagnosis and medical treatment.

2. The first data collected is Medical history, family history, and lifestyle (PFSH) data, Cooperatively filtered data including a defined population, a specific geographical set, specific demographics, or health-related data points associated with viral infection or spread within the defined population, The method according to claim 1, further comprising:

3. The first data collected further includes appointment schedules for a series of medical or health interventions at one or more healthcare centers, including the first healthcare center. The first requirement is determined based on the reservation schedule. The method according to claim 1.

4. The health monitoring parameter set relates to at least one of the following: the user's known health status, one or more medical interventions the user has received in the past, or one or more comorbidities associated with the user. The method according to claim 1.

5. Determining the current location of the user, Determine one or more recommendations, including one or more healthcare centers related to the first requirement determined above, Controlling the user device associated with the user to display the determined one or more recommendations, The further includes, the one or more healthcare centers located within a threshold distance from the current location, The method according to claim 1.

6. Through the user device, A first selection of the first healthcare center from among the one or more healthcare centers, The second selection of a schedule for making an appointment at the first healthcare center mentioned above, Receiving a first input that includes, Based on the first input received, schedule a visit to the first healthcare center, The first user-related dataset is transferred to the first electronic healthcare system based on the schedule, The method according to claim 5.

7. Based on the determination that the user's current location is different from the location of the first healthcare center, a request is sent to the first electronic healthcare system to authorize a virtual reality (VR) based consultation session. To receive permission for the aforementioned request, Based on the permission received, the VR-based consultation session is established between the user device and the wearable electronic device worn by the medical professional at the first healthcare center. The further includes the transfer of the determined first user-related dataset to the wearable electronic device while the VR-based consultation session is active. The method according to claim 1.

8. By applying a second AI model to the transferred first user-related dataset, a presentation dataset is generated that includes data points necessary for healthcare professionals associated with the first healthcare center to assess the user's current health status and address the determined first requirements. Controlling the display device associated with the first healthcare center to display the generated presentation dataset, It further includes, The method according to claim 1, wherein the second AI model includes a trained model that takes the first user-related dataset as input and outputs the presented dataset.

9. The second AI model is an interactive AI associated with the first healthcare center, hosted on the first electronic healthcare system. The method according to claim 8.

10. The process involves applying the first AI model to the collected medical data and the first collected data to generate second inference data. Based on the generated second inference data, determine a second requirement which requires the user to visit a second healthcare center different from the first healthcare center for the second requirement, Based on the first data collected, the medical data collected, and the second inference data, determine a second user-related dataset related to the determined second requirement, which is required by the second electronic healthcare system related to the second healthcare center. Transferring the determined second user-related dataset to the second electronic healthcare system, The method according to claim 9, further comprising:

11. Based on the user's current location and the determination that the first requirement determined corresponds to a medical emergency, a second healthcare center is determined. Schedule emergency response (ER) services, Based on the scheduled ER service, the first user-related dataset is transferred to a second electronic healthcare system associated with the second healthcare center. The method according to claim 1, further comprising:

12. Based on the determination that the first requirement corresponds to a medical emergency, the system further includes sending the alert notification to one or more devices registered to receive alert notifications, The method according to claim 1.

13. The user device receives a request to share the data portion of the collected first data with the first AI model. Based on the above requirements, an encrypted session is established between the first AI model and the user device, While the encryption session is active, the data portion of the collected first data is transferred to the first AI model. The transferred data portion is stored in the data store in an encrypted form, The method according to claim 1, further comprising:

14. Collect first data related to the user, which includes past health data and a sensor dataset corresponding to a health monitoring parameter set. Applying a first artificial intelligence (AI) model to the collected first data, one or more first indicators are calculated that reflect the deviation of the user's health status from a reference value. Based on the calculated one or more first indicators, first inference data is generated that includes one or more labels or tags related to the cause of the deviation. Based on the generated first inference data, the first requirement is determined, which requires the user to visit the first healthcare center for the first requirement. Based on the collected first data and the first inference data, a first user-related dataset related to the determined first requirement is determined. The first user-related dataset determined above is transferred to the first electronic healthcare system associated with the first healthcare center. The presence of the user in the first healthcare center is detected, Based on the above detection, collect medical data related to the medical treatment received by the user at the first healthcare center as part of the determined first requirement, The first AI model is updated based on the collected medical data and the first inference data so that the first AI model outputs the collected medical data as the correct result. Equipped with a processor configured as follows: The first AI model includes a trained model that takes the first data as input and outputs one or more first indicators, and a trained model that takes the medical data and the first data as input and outputs second inference data. The system is characterized in that the first user-related dataset includes information about the user related to the user's diagnosis and medical treatment.

15. The first data collected further includes appointment schedules for a series of medical or health interventions at one or more healthcare centers, including the first healthcare center. The first requirement is determined based on the reservation schedule. The system according to claim 14.

16. The aforementioned processor, Determine the current location of the aforementioned user, Determine one or more recommendations, including one or more healthcare centers related to the first requirement determined above, that are located within a threshold distance from the current location. Control the user device associated with the user to display the one or more recommendations determined above. Through the user device, A first selection of the first healthcare center from among the one or more healthcare centers, The second selection of a schedule for making an appointment at the first healthcare center mentioned above, It receives a first input which includes, Based on the first input received, a visit to the first healthcare center is scheduled. The system is further configured such that the first user-related dataset is transferred to the first electronic healthcare system based on the schedule. The system according to claim 14.

17. The aforementioned processor, By applying the second AI model to the transferred first user-related dataset, a presentation dataset is generated that includes data points necessary for healthcare professionals associated with the first healthcare center to assess the user's current health status and address the determined first requirements. Controlling a display device associated with the first healthcare center to display the generated presentation dataset, The system according to claim 14, further configured as follows.

18. The second AI model is an interactive AI associated with the first healthcare center, hosted on the first electronic healthcare system. The system according to claim 17.

19. A non-temporary computer-readable storage medium configured to store instructions, wherein, when an instruction is executed by a computer in the system, the computer in the system... Collecting first data related to the user, which includes past health data and a sensor dataset corresponding to a health monitoring parameter set, Applying a first artificial intelligence (AI) model to the collected first data to calculate one or more first indicators that reflect the deviation of the user's health status from a reference value, Based on the calculated one or more first indicators, first inference data is generated that includes one or more labels or tags related to the cause of the deviation. Based on the generated first inference data, determine the first requirement which requires the user to visit the first healthcare center for the first requirement, Based on the collected first data and the first inference data, determine a first user-related dataset related to the determined first requirement, Transferring the determined first user-related dataset to the first electronic healthcare system associated with the first healthcare center, To detect the presence of the user in the first healthcare center, Based on the above detection, as part of the first requirement determined above, medical data related to the medical treatment received by the user at the first healthcare center is collected, The first AI model is updated based on the collected medical data and the first inference data so that the first AI model outputs the collected medical data as correct results. Perform an action that includes this, The first AI model includes a trained model that takes the first data as input and outputs one or more first indicators, and a trained model that takes the medical data and the first data as input and outputs second inference data. A non-temporary computer-readable storage medium characterized in that the first user-related dataset includes information about the user related to the user's diagnosis and medical treatment.

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