Online social health network

A computer implemented method comprising:

• providing a system for management of healthcare to support a social network, the system comprising a data analytics platform that includes:
• a patient controlled electronic health system comprising electronic health records (EHRs), the EHRs shared-with a plurality of health provider systems and external databases, each EHR having one or more patient records;
• a computer-based infrastructure comprising one or more processors programmed to update profiles of patient users and provider users;
• at least one database to centralize data, to collect and analyze patient data and health provider data, alone or in combination;
• one or more servers to store the patient data and the health provider data, alone or in combination, the server (a) establishing social connections to create patient communities and (b) utilizing predictive analytics to support decision-making;
• programming the server to:
• create the patient user profile through a first electronic device and having access to a patient user interface, and
• create a provider user profile associated with the patient user profile and accessible via a provider user interface, wherein the EHRs requested from the patient are stored in a centralized database associated with the data analytics platform;
• receive a request from a patient user of the social health network for information associated with the patient user at a first external system, wherein the information requested comprises information identifying the patient user and a health provider user associated with the patient user in the social health network;
• determine if the first external system is authorized to transmit the information requested;
• in response to a determination that the first external system is authorized to transmit the information requested, process the request and extract the patient record of the patient user from the first external system to provide extracted data;
• transmit the extracted data directly to the social health network;
• separate the extracted data into subsets, wherein a first subset comprises health information with the patient user identified and not available for sharing with a second external system, and a second subset comprises health information of the patient user available for sharing with the second external system when the patient user is de-identified;
• de-identify a portion of the second subset of the extracted data to provide de-identified data of the patient user;
• in response to a query from the second external system, perform data analytics using the extracted data including the de-identified data,
• wherein the step to de-identify occurs prior to the step to perform data analytics, thereby protecting privacy of the patient user;
• and
• access the database by way of the processor to predict deep learning models in personalized health comprising one or more of diagnosis, treatment, healthcare operations, administration and cost, or alternative healthcare solutions,
• wherein the one or more processors are programmed to implement a network learning model, the model generated from a training network that models data based on patient inputs, patient data from the patient record, or health provider data, wherein the patient profile is customized at the patient user interface by AI generated mathematical models of disease, condition, or treatment, and health provider user data matched to a patient user to create accurate knowledge graphs respective of the user and trending data graphs to generate recommendations to the patient user and provider user.

An electronic health network system comprising:

• a patient controlled electronic health system comprising one or more health records, the health record shared-with a plurality of health provider systems and external databases;
• one or more processors programmed to update profiles of patient users and provider users, wherein the profiles are optimized by a mathematical model;
• at least one database to centralize data, to collect and analyze patient data and health provider data, alone or in combination;
• one or more servers to store the patient data and the health provider data, alone or in combination, the server (a) establishing social connections to create patient communities and (b) utilizing predictive analytics to support decision-making in a data analytics platform;
• the server programmed to:
• create the patient user profile through a first electronic device and having access to a patient user interface, wherein the mathematical model creates a structured patient user interface that personalizes recommendations through knowledge graphs and data trends generated by artificial intelligence;
• create the provider user profile associated with the patient user profile and accessible via a provider user interface, wherein the one or more health records requested from the patient are stored in a centralized database associated with the data analytics platform;
• receive a request from a patient user of the social health network, for information associated with the patient user at a first external system, wherein the information requested comprises information identifying the patient user and a health provider user associated with the patient user;
• determine if the first external system is authorized to transmit the information requested;
• in response to a determination that the first external system is authorized to transmit the information requested, processing the request and extracting the patient record of the patient user from the first external system to provide extracted data;
• transmit the extracted data directly to the social health network;
• separate the extracted data into subsets, wherein a first subset comprises health information with the patient user identified and not available for sharing with a second external system, and a second subset comprises health
• information of the patient user available for sharing with the second external system when the patient user is de-identified, wherein the second external system is different from the first external system;
• de-identify a portion of the second subset of the extracted data to provide de-identified data of the patient user;
• and
• in response to a query from the second external system, perform data analytics using the extracted data including the de-identified data,
• wherein the step to de-identify occurs prior to the step to perform data analytics, thereby protecting privacy of the patient user, and
• access the database by way of the processor to predict deep learning models in personalized health comprising one or more of—diagnosis, treatment, healthcare operations, administration and cost, and alternative healthcare solutions.