Recent studies documented the importance of individuality and heterogeneity in care planning. In practice, varying behavioral responses are revealed in patients’ care management (CM) records. However, today’s care programs are structured around population-level evidence. What if care managers can take advantage of the revealed behavioral response for personalization? The goal of this study is thus to quantify behavioral response from CM records for informing individual-level intervention decisions. We present a Behavioral Response Inference Framework (BRIeF) for understanding differential behavioral responses that are key to effective care planning. We analyze CM records from a healthcare network over a 14-month period and obtain a set of 2,416 intervention-goal attainment records. Promising results demonstrate that the individual-level care planning strategies that are learned from practice by BRIeF, outperform population-level strategies, yielding significantly more accurate intervention recommendations for goal attainment. To our knowledge, this is the first study of learning practice- based evidence from CM records for care planning, suggesting that increased patient behavioral understanding could potentially benefit augmented intelligence for care management decision support.
Learning Objective 1: Able to describe the framework of deriving differential patient responses as practice-based evidence from observational care management records
Learning Objective 2: Able to envision more applications that involve the secondary use of care management records
Pei-Yun Hsueh (Presenter)
IBM T.J. Watson Research Center
Subhro Das, IBM T.J. Watson Research Center
Chandramouli Maduri, IBM T.J. Watson Research Center
Karie Kelly, Watson Health