2019/06/19 by Subhro Das, Chandramouli Maduri, Das, Subhro +5
Health Professions · Medicine · #Applications (stat.AP) #Chronic Disease Management Strategies #Diabetes Management and Education #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Primary Care and Health Outcomes
paper · pdf · doi:10.48550/arxiv.1906.08339
openalex publication_date 2019/06/19 · openalex created_date 2019/06/27 · openalex updated_date 2026/07/28
The health outcomes of high-need patients can be substantially influenced by the degree of patient engagement in their own care. The role of care managers includes that of enrolling patients into care programs and keeping them sufficiently engaged in the program, so that patients can attain various goals. The attainment of these goals is expected to improve the patients' health outcomes. In this paper, we present a real world data-driven method and the behavioral engagement scoring pipeline for scoring the engagement level of a patient in two regards: (1) Their interest in enrolling into a relevant care program, and (2) their interest and commitment to program goals. We use this score to predict a patient's propensity to respond (i.e., to a call for enrollment into a program, or to an assigned program goal). Using real-world care management data, we show that our scoring method successfully predicts patient engagement. We also show that we are able to provide interpretable insights to care managers, using prototypical patients as a point of reference, without sacrificing prediction performance.