This was part of Analytics for Improved Healthcare
Optimal Patient Selection into Care Management Programs
Dan Adelman, University of Chicago
Monday, April 3, 2023
Abstract: Care Management Programs (CMPs) coordinate the care for patients with complex care needs and older frail adults, who usually represent the top of healthcare spending. Although CMPs have appeared as credible avenues for reducing healthcare utilization, empirical evidence showed mixed results. Using patient-level data we evaluate the causal impact of the CMP of a major academic medical center, and we find no impact on five healthcare utilization measures. In the light of these negative results, one wonders how can CMPs be improved. To address this question, we use Markov Decision Processes (MDPs) and Dynamic Programming to model the task of optimally allocating treatment amongst patients while fulfilling some capacity constraints. The complexity of such a problem may be very high because healthcare populations may be large enough that gathering information of the current status of each patient and tracking the evolution of their covariates is untenable. To address this challenge we develop the so-called measurized theory, which allows to model MDPs that optimize the distribution of treated and untreated patients instead of dealing with identifed patients. This abstraction transforms a complicated problem into an intuitive formulation and sets the stage for delivering clinically implementable solutions in the future.