With the growing cost of health care in the United States, the need to improve efficiency and efficacy has become increasingly urgent. There has been a keen interest in developing interventions to effectively coordinate the typically fragmented care of patients with many comorbidities. Evaluation of such interventions is often challenging given their long-term nature and their differential effectiveness among different patients. Furthermore, care coordination interventions are often highly resource-intensive. Hence there is pressing need to identify which patients would benefit the most from a care coordination program. In this work we introduce a subgroup identification procedure for long-term interventions whose effects are expected to change smoothly over time. We allow differential effects of an intervention to vary over time and encourage these effects to be more similar for closer time points by utilizing a fused lasso penalty. Our approach allows for flexible modeling of temporally changing intervention effects while also borrowing strength in estimation over time. We utilize our approach to construct a personalized enrollment decision rule for a complex case management intervention in a large health system and demonstrate that the enrollment decision rule results in improvement in health outcomes and care costs. The proposed methodology could have broad usage for the analysis of different types of long-term interventions or treatments including other interventions commonly implemented in health systems.
Keywords: Fused lasso; comparative effectiveness research; electronic health records; interaction modeling; precision medicine.