Outpatient services account for more than four-fifths of patient care in the United States, and most patients access these services via appointment scheduling. Because of the possibility of patient no-shows, healthcare providers usually rely on overbooking. If very few patients show up, it will leave hospital resources underutilized, whereas too many showing up will increase patient wait times and increase staff overtime costs. In a hospital or a network of clinics, where patients could enter and exit at various stations and have complex flow patterns, scheduling becomes even more of a challenge. In “Coordinated Patient Appointment Scheduling for a Multistation Healthcare Network,” D. Wang, K. Muthuraman, and D. Morrice propose a multistation network model that carefully strikes a balance between assumptions that allow tractability and assumptions that disallow real-world adoption. Given the complexity involved in solving the model, they explore a sequence of approximations and find one that offers a significant computational advantage.

The process of preparing patients for outpatient surgery is information intensive. However, medical records are often fragmented among different providers and systems. As a result, the preoperative assessment process is frequently prolonged by missing information, potentially leading to surgery delay or cancellation. In this study, we simulate an anesthesiology pre-operative assessment clinic to quantify the impact of patient information deficiency and to assist in the development of a patient-centered surgical home to mitigate this problem through better system-wide coordination.