About 27% of diabetics also suffer from depression, and the presence of co-morbid depression could increase the cost of care for diabetes by up to 100%. Several randomized clinical trials have demonstrated that physical and mental health are more likely to improve for diabetes patients suffering from depression when regular treatment for depression is provided in a primary care setting (called Collaborative Care). An important operational lever in managing Collaborative Care is the allocation of the care manager’s time to enrolled patients based on their requirements, which in turn influences the revenue, costs, and patient health outcomes. We present a mathematical modeling approach that determines the optimal allocation of care manager’s time and quantifies the costs and benefits of Collaborative Care. In particular, we model Collaborative Care management at the clinic level as an infinite horizon Markov Dynamic Program. The objective is a weighted sum of total patient QALYs and the clinic profits. The model incorporates insurance payment, resource utilization costs, and disease progression of comorbid diabetes and depression. We derive structural properties of the optimal allocation of the care manager’s time. Using these structural properties, we develop a practical and easy-to-implement solution approach that performs close to the optimal solution. We calibrate the model with data obtained from a large academic medical center and show that our solutions could potentially improve total QALYs and clinic profits when compared to current practices. We also perform sensitivity analysis to different payment models to derive insights relevant to healthcare policy.
Note: Research papers posted on SSRN, including any findings, may differ from the final version chosen for publication in academic journals.