Patient-to-nurse ratios: Balancing quality, nurse turnover, and cost

David D. Cho (), Kurt M. Bretthauer () and Jan Schoenfelder ()
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David D. Cho: California State University, Fullerton
Kurt M. Bretthauer: Kelley School of Business, Indiana University
Jan Schoenfelder: University of Augsburg

Health Care Management Science, 2023, vol. 26, issue 4, No 12, 807-826

Abstract: Abstract We consider the problem of setting appropriate patient-to-nurse ratios in a hospital, an issue that is both complex and widely debated. There has been only limited effort to take advantage of the extensive empirical results from the medical literature to help construct analytical decision models for developing upper limits on patient-to-nurse ratios that are more patient- and nurse-oriented. For example, empirical studies have shown that each additional patient assigned per nurse in a hospital is associated with increases in mortality rates, length-of-stay, and nurse burnout. Failure to consider these effects leads to disregarded potential cost savings resulting from providing higher quality of care and fewer nurse turnovers. Thus, we present a nurse staffing model that incorporates patient length-of-stay, nurse turnover, and costs related to patient-to-nurse ratios. We present results based on data collected from three participating hospitals, the American Hospital Association (AHA), and the California Office of Statewide Health Planning and Development (OSHPD). By incorporating patient and nurse outcomes, we show that lower patient-to-nurse ratios can potentially provide hospitals with financial benefits in addition to improving the quality of care. Furthermore, our results show that higher policy patient-to-nurse ratio upper limits may not be as harmful in smaller hospitals, but lower policy patient-to-nurse ratios may be necessary for larger hospitals. These results suggest that a “one ratio fits all” patient-to-nurse ratio is not optimal. A preferable policy would be to allow the ratio to be hospital-dependent.

Keywords: Hospital capacity planning; Nurse staffing; Patient-to-nurse ratios; Quality of care; Operations research; Operations management; Optimization (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10729-023-09659-y

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