In many healthcare settings, patients receive a series of services during a single visit, e.g., infusion care and orthopedic visit. A key commonality is the tandem structure where each stage involves a non-trivial random service time. We develop the first analytic model to provide each patient an individualized visit itinerary in a tandem health service system. A case study populated by data from a large infusion center shows that our approach makes a remarkable 27% cost reduction over practice.

Delays in admission to rehabilitation care can be both capacity-driven and/or due to processing delays. Standard data however only includes a single measure of delay, and the bed allocation decisions in practice do not follow a systematic policy. We propose a hidden Markov model to estimate the processing times and the status-quo bed allocation policy. We validate our structural model and conduct counterfactual experiments to evaluate various operational interventions aimed at reducing delays.