Multi-model member outreach system

A system, comprising:

• one or more processors;
• and
• memory storing a healthcare data repository of data including historical healthcare data and healthcare data associated with a member of a health plan, the memory further storing computer-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
• training a set of machine learning models to identify features in historical healthcare data that are predictive of at least one of historical healthcare costs, historical healthcare utilizations, or historical risk metrics indicated in the historical healthcare data, wherein different machine learning models, of the set of machine learning models, vary based on at least one of:
• being different types of machine learning models, or
• being trained on different subsets of the historical healthcare data;
• generating, using the set of machine learning models, a set of base predictions associated with the member based on the features in the healthcare data associated with the member, wherein:
• the different machine learning models, of the set of machine learning models, generate different base predictions of the set of base predictions,
• the set of base predictions indicate at least one of:
• predicted healthcare costs associated with the member,
• predicted healthcare utilizations associated with the member, or
• predicted risk metrics associated with the member, and
• the different base predictions indicate one or more outreach priority levels associated with the member;
• generating a final prediction associated with the member by combining the set of base predictions based at least in part on a set of weights that correspond to the set of machine learning models;
• generating an outreach ticket associated with the member based on the final prediction;
• providing the outreach ticket to a computing device operable by a representative;
• receiving ticket feedback associated with the outreach ticket from the computing device, wherein the ticket feedback indicates at least one score, subjectively assigned by the representative, including one or more of:
• an outreach intervenability score of the member, or
• an interpretability score of the outreach ticket;
• and
• adjusting the set of weights by using a prediction combination model to increase or decrease individual weights, of the set of weights, that correspond to the different machine learning models, based on correlations between the at least one score indicated in the ticket feedback and the one or more outreach priority levels indicated by the different base predictions generated by the different machine learning models.

One or more non-transitory computer-readable media storing computer-executable instructions that, when executed by one or more processors of at least one computing device, cause the one or more processors to perform operations comprising:

• training a set of machine learning models to identify features in historical healthcare data that are predictive of at least one of historical healthcare costs, historical healthcare utilizations, or historical risk metrics indicated in the historical healthcare data, wherein different machine learning models, of the set of machine learning models, vary based on at least one of:
• being different types of machine learning models, or
• being trained on different subsets of the historical healthcare data;
• generating a set of base predictions associated with a member of a health plan using the set of machine learning models, wherein:
• the set of machine learning models generates the set of base predictions using the features in healthcare data associated with the member,
• the set of base predictions indicate at least one of:
• predicted healthcare costs associated with the member,
• predicted healthcare utilizations associated with the member, or
• predicted risk metrics associated with the member, and
• individual base predictions, of the set of base predictions, indicate corresponding outreach priority levels associated with the member;
• generating a final prediction associated with the member by combining the set of base predictions, based at least in part on a set of weights that correspond to the set of machine learning models, wherein the set of weights is determined by a prediction combination model based at least in part on correlations between:
• outreach intervenability scores indicated in subjective representative feedback associated with past member outreach services offered by representatives to other members, and
• previous outreach priority levels associated with previous base predictions, associated with the other members, generated by the different machine learning models of the set of machine learning models;
• and
• generating a notification associated with the member based on the final prediction.