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Hospitals are under significant pressure from payers to reduce costs. The single largest fixed cost for a hospital is inpatient beds, yet there is significant variation in hospital capacity utilization. We study bed capacity in New York City hospital obstetrics units and find that while many hospitals have an insufficient number of beds to provide timely access to care, overall there is significant excess capacity. Our findings, coupled with current demographic and clinical practice trends, indicate that a large fraction of obstetrics units nationwide could likely reduce their bed capacity while assuring timely access to care, resulting in large savings in capital and staffing costs. Given emerging health care delivery and payment models that will likely decrease demand for other types of hospital beds, our study suggests that data-based methodologies should be used by hospitals and policy makers to identify opportunities for reducing excess bed capacity in other inpatient units as well.

This book focuses on the emotional hazards of conducting fieldwork about or within contexts of violence and provides a forum for field-based researchers to tell their stories. Increasingly novice and seasoned ethnographers alike, whether by choice or chance, are working in situations where multidimensional forms of violence, conflict and war are facets of everyday life. The volume engages with the methodological and ethical issues involved and features a range of expressive writings that reveal personal consequences and dilemmas. The contributors use their emotions, their scars, outrage and sadness alongside their hopes and resilience to give voice to that which is often silenced, to make visible the entanglements of fieldwork and its lingering vulnerabilities. The book brings to the fore the lived experiences of researchers and their interlocutors alike with the hope of fostering communities of care.

AbstractConstrained Markov decision process (CMDP) is a methodology that has not seen wide applications in the literature, but is a more natural specification for modeling preferences in modern service systems. In this paper we present a general framework for solving two‐class CMDPs. In particular, we show that CMDPs can be solved by using the Lagrangian dual to specify a particular unconstrained problem. If an appropriate Lagrange multiplier can be discerned, structural results can be exploited to solve the original CMDP with the appropriate structure. We show that for two queues in parallel or two queues in series, the framework leads to simple threshold‐like optimal policies. The results in each case are used to develop heuristics for analogous problems with abandonments with applications to health care, call centers, and manufacturing systems. The efficacy of the heuristics is verified in each case via a detailed numerical study.