Abstract: Systems and methods for medication fillings management are disclosed. For example, user information and/or refill schedules associated with users may be utilized to predict which users are likely to miss a medication refill. The presently disclosed systems and methods may determine health indicators associated with users and, by utilizing one or more machine learning techniques, determine which health indicators, alone or in combination, are associated with users prone to missing refills. As such, the system may predict users that are likely to miss a refill and may generate preemptive, targeted refill reminders in order to prevent the missed refill before it occurs.

Abstract: Systems and methods including analyzing profiles, generating recommendation(s) and supporting evidence associated with the recommendation(s) related to medical services provided to a patient, and transmitting the recommendation(s) and supporting evidence associated with the recommendation(s) to a device that displays the information are disclosed. The supporting evidence may be presented based on a statistical relevance of the information and/or a likelihood that a medical professional will utilize the information.

Abstract: Systems and methods for identifying related data for variant testing are disclosed. For example, data stored for records from disparate data sources may not include the same identifiers for all records such that it may not be readily identified as record for the same member. The presently-disclosed systems and methods generate data tagged as identifier information and determine the degree of similarity between the identifier information. Based at least in part on the degree of similarity meeting or exceeding a threshold amount of similarity, the data may be associated with a member identifier. By properly identifying user information corresponding to member identifiers, the members may be split in meaningful ways to perform variant test.

Abstract: Systems and methods for medication fillings management are disclosed. For example, user information and/or refill schedules associated with users may be utilized to predict which users are likely to miss a medication refill. The presently disclosed systems and methods may determine health indicators associated with users and, by utilizing one or more machine learning techniques, determine which health indicators, alone or in combination, are associated with users prone to missing refills. As such, the system may predict users that are likely to miss a refill and may generate preemptive, targeted refill reminders in order to prevent the missed refill before it occurs.

Abstract: Systems and methods including analyzing profiles, generating questions relate to suspected diagnoses of a patient, and transmitting the questions to a device that displays the questions are disclosed. Responses to the questions are received which may be used in subsequent suspected diagnosis, generating updated questions, or confirming a suspected diagnosis.