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: The present disclosure describes methods and systems for machine learning models utilized for diagnosis suspecting. These methods and systems utilize machine learning models may be trained to diagnose diseases or conditions. The models may be trained with data from disparate sources that are aggregated and formatted to be utilized in these models.

Abstract: Techniques are described herein that provide relevant information associated with a patient to a medical provider. In some instances, the relevant information may be provided to the medical provider during a clinical visit with the patient, such as via an application managed by a service provider. In some instances, the relevant information may be provided to the medical provider at another time, such as that associated with a referral submission. The relevant information may be provided via one or more interfaces associated with an application. In some instances, the interface(s) may guide the medical provider through a clinical visit to maximize a level of care provided to the member and minimize an amount of time associated with the clinical visit.

Abstract: The present disclosure describes methods and systems for machine learning models utilized for identifying issues with medications and gaps in care. The present disclosure also describes methods and systems for machine learning models utilized to provide recommended actions for addressing the issues with medication and gaps in care. These methods and systems utilize machine learning models that are trained to identify issues with medications and gaps in care, as well as provide recommended actions for those medications issues and gaps in care. The models are trained with data from disparate sources that are aggregated and formatted to be utilized in these models.

Abstract: Systems and methods including analyzing profiles, generating medical recommendations, prioritizing the medical recommendations, throttling the medical recommendations, and transmitting a prioritized set of medical recommendations to a device that displays the information are disclosed. The medical recommendations may be prioritized based on an importance of the recommendation to a patient, a medical severity, and/or prior interaction with the system. The medical recommendations may be throttled to determine a number of recommendations to display to a medical provider during a period of time, such as a patient examination.

Abstract: Systems and methods including analyzing profiles, generating medical recommendations, prioritizing the medical recommendations, throttling the medical recommendations, and transmitting a prioritized set of medical recommendations to a device that displays the information are disclosed. The medical recommendations may be prioritized based on an importance of the recommendation to a patient, a medical severity, and/or prior interaction with the system. The medical recommendations may be throttled to determine a number of recommendations to display to a medical provider during a period of time, such as a patient examination.

Abstract: The present disclosure describes methods and systems for machine learning models utilized for diagnosis suspecting. These methods and systems utilize machine learning models may be trained to diagnose diseases or conditions. The models may be trained with data from disparate sources that are aggregated and formatted to be utilized in these models.

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 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.