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 base predictive models, wherein individual ones of the set of base predictive models are machine learning models and the training is based on the historical healthcare data;
• generating, using the set of base predictive models, a set of base predictions associated with the member based on the healthcare data associated with the member, wherein:
• different base predictive models of the set of base predictive 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 base predictive 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 that correspond to the set of base predictive models, based on at least one comparison 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 set of base predictive 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 at least one computing device to perform operations comprising:

• training a set of base predictive models, wherein individual ones of the set of base predictive models are machine learning models and the training is based on historical healthcare data;
• generating a set of base predictions associated with a member of a health plan using the set of base predictive models, wherein;
• the set of base predictive models generate the set of base predictions using 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 base predictive models, wherein the set of weights are determined based at least in part on comparisons between:
• subjective representative feedback associated with past member outreach services offered by representatives to other members, the subjective representative feedback including outreach intervenability scores associated with the past member outreach services offered to the other members, and
• previous outreach priority levels associated with previous base predictions, associated with the other members, generated by the set of base predictive models;
• and
• generating a notification associated with the member based on the final prediction.