Hospitalists are medical doctors that specialize in the care of hospitalized patients, a role that until recently belonged to primary care physicians. We develop an operational model of hospitalist-patient interactions with rounding and responding service modes, optimizing hospitalist caseload and case-mix to achieve the maximal reduction in patient length of stay (LOS). We show that hospitalists are effective at reducing LOS for patients with complex conditions, corroborating intuitive reasoning. However, the optimal hospitalist case-mix also includes “simple” patients with few interventions and short LOS, as they can effectively reduce discharge delays. This actionable insight is particularly salient for small community hospitals with simple, short-stay patients, where hospitalists may be undervalued due to the prevailing belief that they are primarily effective for complex patients. We conduct a comparative case study of a small community hospital and a large academic hospital, drawing a stark contrast between the two in terms of ideal caseload and patient coverage. Despite the fact that the academic hospital treats higher complexity patients, hospitalists at the community hospital should actually have a lower caseload than hospitalists at the academic hospital due to shorter stays in the community hospital. We find that both hospitals are understaffed but for different reasons: the academic hospital needs to staff more hospitalists to reduce the current caseload of its hospitalists, whereas the community hospital needs to staff more hospitalists to expand its hospitalist coverage to more patients. We estimate that these hospitals can save on average $1.5 million annually by implementing the optimal staffing policies. This paper was accepted by Stefan Scholtes, healthcare management. Funding: This work was supported by a PSC-CUNY Award, jointly funded by The Professional Staff Congress and The City University of New York. Supplemental Material: The e-companion and data files are available at https://doi.org/10.1287/mnsc.2022.4342 .

Hospitals continue to face the challenge of providing high-quality patient care in an environment of rising healthcare costs. In response, a great deal of attention has been given to advance planning decisions such as nurse staffing, bed mix, scheduling, and patient flow. However, less attention has been given to incorporating quick-response methods in the nurse scheduling process by both anticipating and responding to patient demand fluctuations. Therefore, in this paper, we present a model that incorporates two classes of quick-response decisions in hospitals’ nurse scheduling: (i) adjustments to the unit assignments of cross-trained float nurses and (ii) transfers of patients between units and off-unit admissions. Analyzing three hospitals that are subject to different regulations with respect to patient-to-nurse ratios allows us to draw conclusions on how these hotly debated ratios impact hospital performance, nurse workload, and patient experience. We find that quick-response via cross-trained nurses may lead to higher total costs in settings where an upper limit on patient-to-nurse ratios is enforced. This result has significant managerial and political relevance in locations such as California. Another takeaway is that only a small number of patient transfers or off-unit admissions provides close to the full potential benefit, thus minimizing the negative impact on patient satisfaction and quality of care. Moreover, our proposed scheduling approach reduces the number of undesired assigned shifts. Finally, bed and nurse capacity utilization are shown to be important considerations when determining how and whether to use quick-response methods.

We consider the problem of optimal capacity allocation in a hospital setting, where patients pass through a set of units, for example intensive care and acute care (AC), or AC and post-acute care. If the second stage is full, a patient whose service at the first stage is complete is blocked and cannot leave the first stage. We develop a new heuristic for tandem systems to efficiently evaluate the effects of such blocking on system performance and we demonstrate that this heuristic performs well when compared with exact solutions and other approaches presented in the literature. In addition, we show how our tandem heuristic can be used as a building block to model more complex multi-stage hospital systems with arbitrary patient routing, and we derive insights and actionable capacity strategies for a real hospital system where such blocking occurs between units.