Helm, Jonathan Eugene

Problem definition: Coordinated care network (CCN) is a burgeoning paradigm where patients’ diagnosis and treatment plans are developed based on collaboration between multiple, colocated medical specialties to holistically address patients’ health needs. A primary performance metric for CCNs is how quickly patients can complete their itinerary of appointments at multiple medical services in the network. Rapid completion is critical to care delivery but also presents a major operational challenge. Because information about a patient’s condition and treatment options evolves over the course of the itinerary, care paths are not known a priori. Thus, appointments (except for the first one) cannot be reserved in advance, which may result in significant delays if capacity is not allocated properly. Methodology/results: We study capacity allocation for the patient’s first (root) appointment as the primary operational lever to achieve rapid itinerary completion in CCNs. We develop a novel queueing-based analytical framework to optimize this root appointment allocation, maximizing the proportion of patients completing care by prespecified deadlines. Our framework accounts for the complex interactions among all patients in the network through the blocking process, which contrasts with conventional siloed planning. We provide an exact characterization of the itinerary time and develop a mean-field approximation with convergence guarantees that permits tractable solutions for large-scale network problems. In a simulation case study of Mayo Clinic, our solution improves on-time completion from 60% under the current plan to more than 93%. Managerial implications: We demonstrate that root appointment allocation is a multifaceted problem and that ignoring any of those facets can lead to poor performance. Simultaneously accounting for all of these complexities makes manual template design or traditional optimization methods inadequate, highlighting the significance of our integrated approach. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0649 .

The prevailing first‐come‐first‐served approach to outpatient appointment scheduling ignores differing urgency levels, leading to unnecessarily long waits for urgent patients. In data from a partner healthcare organization, we found in some departments that urgent patients were inadvertently waiting longer for an appointment than non‐urgent patients. This study develops a capacity allocation optimization methodology that reserves appointment slots based on urgency in a complicated, integrated care environment where multiple specialties serve multiple types of patients. This optimization reallocates network capacity to limit access delays (indirect waiting times) for initial and downstream appointments differentiated by urgency. We formulate this problem as a queueing network optimization and approximate it via deterministic linear optimization to simultaneously smooth workloads and guarantee access delay targets. In a case study of our industry partner we demonstrate the ability to (i) reduce urgent patient mean access delay by 27% with only a 7% increase in mean access delay for non‐urgent patients, and (ii) increase throughput by 31% with the same service levels and overtime.