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Patients with multiple chronic conditions and social needs represent a small percentage of the population but have a disproportionate impact on healthcare costs and utilization. Organizations around the United States have created programs, often referred to as complex care interventions, to improve the health and well-being of such patients and reduce avoidable hospital and emergency department use. In this tutorial, we focus on two emerging themes in the field: (1) identifying clinically meaningful subgroups in complex care populations through unsupervised learning methods and (2) describing the key operational features of interventions with an emphasis on staffing needs and the impact on patient outcomes. The material presented in this tutorial draws on the research of the Healthcare Operations Research Laboratory at the University of Massachusetts, Amherst, and its collaborating partners. To illustrate these themes and contextualize the details of complex care delivery, we use a range of patient-level examples, visualizations, descriptive summaries, case studies, and results from the clinical literature. 

Healthcare spending in the United States is concentrated on a small percentage of individuals, with 5% of the population accounting for 50% of annual spending. Many patients among the top 5% of spenders have complex health and social needs. Care coordination interventions, often led by a multidisciplinary team consisting of nurses, community health workers and social workers, are one strategy for addressing the challenges facing such patients. Care teams strive to improve health outcomes by forging strong relationships with clients, visiting them on a regular basis, reconciling medications, arranging primary and speciality care visits, and addressing social needs such as housing instability, unemployment and insurance. In this paper, we propose a simulation algorithm that samples longitudinal patient-level encounter histories to estimate the staffing needs for a multidisciplinary care team. Our numerical results illustrate multiple uses of the algorithm for staffing under stationary and non-stationary patient enrollment rates.