Abstract: A computing system can obtain health record data of a user from a number of health record sources, and generate, on a computing device of the user, an interactive user interface comprising prepopulated data based on the health record data. Based on interactions by the user on the interactive user interface, the system can receive input data from the computing device of the user, the input data corresponding to health-related information provided by the user. Based on at least one of the health record data or the input data, the system can generate a set of health outcome predictions for the user, and based on the set of health outcome predictions, the input data, and the health record data, the system can generate a set of health care plan rankings for the user.

Abstract: A computing system can obtain a corpus of health records for a user based on received user credential information by accessing a plurality of heath record systems. The system can initiate a health record verification process to enable the user to verify each of the obtained health records, and subsequent to the health record verification, resulting in a verified set of health records for the user, the system can present, on a display screen of a computing device of the user, a set of one or more risk mitigation providers or health plans based at least in part on the verified set of health records of the user.

Abstract: A computing system can obtain health record data of a user from a number of health record sources, and determine a health profile of the user based on the health record data. The system can then generate content for the user based on the health profile, wherein the content includes content items from published journals and articles.

Abstract: A computing system can generate, for a computing device of a call agent, a dynamic scripting interface providing a dynamic script for the call agent during a call session with a user. Based on one or more authorizations provided by the user, the system can obtain, from the health record databases, health record data of the user. During the call session, the system can dynamically rank a set of health care plans for the user based at least in part on the health record data of the user.

Abstract: A computing system can obtain health record data of a user from health record sources. The system can generate, on a computing device of a call agent, an interactive agent interface comprising prepopulated data based on the health record data. Based on an interaction between the call agent and the user during a call session, the system can receive input data from the computing device of the call agent. Based on at least one of the health record data or the input data, the system can generate a set of health outcome predictions for the user, and based on the set of health outcome predictions, the input data, and the health record data, the system can generate a set of health care plan rankings for the user.

Abstract: A computing system can implement a cognitive-based risk mitigation service. The system can train and test a correlation model using a cognitive assessment test (CAT) distributed to control base users. The control base users may further provide access to motor vehicle records such that the system can determine a set of correlations between cognitive health metrics tested by the (CAT) and vehicle accident risk as indicated by actual accident data in the motor vehicle records. Once a threshold level of accuracy is attained, the correlation model may be executed on response data from inquiring users taking the CAT to determine motor vehicle risk of those users without them providing access to their motor vehicle records. This motor vehicle risk may then be used to generate a risk mitigation policy package for the inquiring user.

Abstract: A computing system can implement a health verification and prediction service by generating an interactive user interface through which users can provide responses to health assertions in one or more trivia sessions. Each of the health assertions can include a correlation value to actual health outcomes based on responses received from individuals a control group and the known health outcomes of those individuals. Based on the responses from the user, the computing system can generate a health profile for the user.

Abstract: A computing system can execute a correlation model for a library of health assertions to configure a correlation value for each health assertion. The correlation value comprises a correlation between knowledge associated with the health assertion and known health outcomes of individuals in a control group who have also provided responses to the health assertions. The computing system provides a health trivia session for users and based at least in part on the performance of the user, generates a morbidity risk profile that is used to classify the user in an underwriting class.

Abstract: A computing system includes a database having a collection of health-related assertions for a health quiz to be taken by users. Each health assertion is tuned with a set of correlative health values based on responses to the health assertion provided by a control group of users. The system can determine the set of correlative health values for each health assertion based on the actual health outcomes of the control group users. Using the correlative values for each health assertion and activity data from a wearable activity monitoring device of a user, the computing system can generate a health profile of the user who interacts with a health trivia session comprising a subset of the health assertions.

Abstract: A computing system can execute a correlation model to determine correlation values for health assertions, where the correlation values for each health assertion indicates correlations between the health assertion and one or more actual health outcomes. Actual health outcomes are provided by control group users who have also provided responses to each of the health assertions. The computing system may then provide users with a health trivia session comprised of these health assertions, and based at least in part on the performance of the user during the trivia session, generate a health profile for the user that is predictive of one or more health outcomes.

Abstract: A computing system can execute a correlation model to determine correlation values for health assertions, where the correlation value for each health assertion indicates a level of correlation between the health assertion and one or more actual health outcomes. Actual health outcomes are provided by control group users who have also provided responses, either correct or incorrect, to each of the health assertions. The computing system may then provide users with a health trivia session comprised of these health assertions, and based at least in part on the performance of the user during the trivia session, generate a health profile for the user.

Abstract: A computing system includes a database having a collection of health-related assertions for a health quiz to be taken by users. Each health assertion is tuned with a correlative health value based on answers to the health assertion provided by a control group of users. For each user in the control group, at least one health outcome is known, and therefore the computing system can use a big data approach to determine the correlative value for each health assertion to the actual health outcomes of the control group users. Using the correlative values for each health assertion, the computing system can generate a health profile of each user who takes the health quiz. In certain examples, the health quiz correlation can be combined with activity monitoring data to develop a highly accurate health profile for each user.

Abstract: A computing system can implement a risk-assessment service through execution of a health lab data classifier model that classifies a prospective client's clinical health lab data, comprising a set of health aspect levels, within a set of health aspect ranges. When the set of health aspect levels of the prospective client are within a set of health aspect ranges, the computing system can generate one or more risk-mitigation packages at a particular tier of the risk-assessment service.

Abstract: A computing system can implement a health verification and prediction service by generating an interactive user interface through which users can provide responses to health assertions in one or more trivia sessions. Each of the health assertions can include a correlation value to actual health outcomes based on responses received from individuals a control group and the known health outcomes of those individuals. Based on the responses from the user, the computing system can generate a health profile for the user.

Abstract: A computing system can execute a correlation model for a library of health assertions to configure a correlation value for each health assertion. The correlation value comprises a correlation between knowledge associated with the health assertion and known health outcomes of individuals in a control group who have also provided responses to the health assertions. The computing system provides a health trivia session for users and based at least in part on the performance of the user, generates a morbidity risk profile that is used to classify the user in an underwriting class.

Abstract: A computing system can implement a health verification and prediction service by generating an interactive user interface through which users can provide responses to health assertions in one or more trivia sessions. Each of the health assertions can include a correlation value to actual health outcomes based on responses received from individuals a control group and the known health outcomes of those individuals. Based on the responses from the user, the computing system can generate a health profile for the user.

Abstract: A computing system can execute a correlation model for a library of health assertions to configure a correlation value for each health assertion. The correlation value comprises a correlation between knowledge associated with the health assertion and known health outcomes of individuals in a control group who have also provided responses to the health assertions. The computing system provides a health trivia session for users and based at least in part on the performance of the user, generates a mortality outcome profile that is used to classify the user in an underwriting class.

Abstract: A computing system can execute a correlation model for a library of health assertions to configure a correlation value for each health assertion. The correlation value comprises a correlation between knowledge associated with the health assertion and known health outcomes of individuals in a control group who have also provided responses to the health assertions. The computing system provides a health trivia session for users and based at least in part on the performance of the user, generates a mortality outcome profile that is used to classify the user in an underwriting class.

Abstract: A computing system can execute a correlation model to determine correlation values for health assertions, where the correlation value for each health assertion indicates a level of correlation between the health assertion and one or more actual health outcomes. Actual health outcomes are provided by control group users who have also provided responses, either correct or incorrect, to each of the health assertions. The computing system may then provide users with a health trivia session comprised of these health assertions, and based at least in part on the performance of the user during the trivia session, generate a health profile for the user.

Abstract: A computing system includes a database having a collection of health-related assertions for a health quiz to be taken by users. Each health assertion is tuned with a correlative health value based on answers to the health assertion provided by a control group of users. For each user in the control group, at least one health outcome is known, and therefore the computing system can use a big data approach to determine the correlative value for each health assertion to the actual health outcomes of the control group users. Using the correlative values for each health assertion, the computing system can generate a health profile of each user who takes the health quiz. In certain examples, the health quiz correlation can be combined with activity monitoring data to develop a highly accurate health profile for each user.