Inpatient staffing costs are significantly affected by nurse absenteeism, which is typically high in U.S. hospitals. We use data from multiple inpatient units of two hospitals to study which factors, including unit culture, short-term workload, and shift type, explain nurse absenteeism. The analysis highlights the importance of paying attention to heterogeneous absentee rates among individual nurses. We then develop models to investigate the impact of demand and absentee rate variability on the performance of staffing plans and obtain some structural results. Utilizing these results, we propose and test three easy-to-use heuristics to identify near-optimal staffing strategies. Such strategies could be useful to hospitals that periodically reassign nurses with similar qualifications to inpatient units in order to balance workload and accommodate changes in patient flow. Although motivated by staffing of hospital inpatient units, the approach developed in this paper is also applicable to other team-based and labor-intensive service environments.

Patients' satisfaction with an appointment system when they attempt to book a nonurgent appointment is affected by their ability to book with a doctor of choice and to book an appointment at a convenient time of day. For medical conditions requiring urgent attention, patients want quick access to a familiar physician. For such instances, it is important for clinics to have open slots that allow same-day (urgent) access. A major challenge when designing outpatient appointment systems is the difficulty of matching randomly arriving patients' booking requests with physicians' available slots in a manner that maximizes patients' satisfaction as well as clinics' revenues. What makes this problem difficult is that booking preferences are not tracked, may differ from one patient to another, and may change over time. This paper describes a framework for the design of the next generation of appointment systems that dynamically learn and update patients' preferences and use this information to improve booking decisions. Analytical results leading to a partial characterization of an optimal booking policy are presented. Examples show that heuristic decision rules, based on this characterization, perform well and reveal insights about trade-offs among a variety of performance metrics important to clinic managers.