Designing a Consumer-centric Care Management Program by Prioritizing Interventions Using Deep Learning Causal Inference.

Abstract

Care management is a team-based and patient-centered approach to reduce health risks and improve outcomes for managed populations. Post Discharge Management (PDM) is an important care management program at Elevance Health, which is aimed at reducing 30-day readmission risk for recently discharged patients. The current PDM program suffers from low engagement. When assigning interventions to patients, case managers choose the interventions to be conducted in each call only based on their limited personal experiences. In this work, we use deep learning causal inference to analyze the impact of interventions conducted on the first call on consumer engagement in the PDM program, which provides a reliable reference for case managers to select interventions to promote consumer engagement. With three experiments cross-validating the results, our results show that consumers will engage more in the program if the case manager conducts interventions that require more nurse-patient interactions on the first call. On the other hand, conducting less interactive and more technical interventions on the first call leads to relatively poor consumer engagement. These findings correspond to the clinical sense of experienced nurses and are consistent with previous findings in patient engagement in hospital settings.