Abstract
Hospital inpatient bed capacity might be better described as evolved than planned. At least two challenges lead to this behaviour: different views of patient demand implied by different data sets in a hospital and limited use of scientific methods for capacity estimation. In this paper, we statistically examine four distinct hospital inpatient data sets for internal consistency and potential usefulness for estimating true patient bed demand. We conclude that posterior financial data, billing data, rather than the census data commonly relied upon, yields true hospital bed demand. Subsequently, a capacity planning tool, based upon queuing theory and financial data only, is developed. The delivery mechanism is an Excel spreadsheet. One adjusts input parameters including patient volume and mix and instantaneously monitors the effect on bed needs across multiple levels of care. A case study from a major hospital in Phoenix, Arizona, USA is used throughout to demonstrate the methodologies.
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Acknowledgements
We wish to thank leadership at The Hospital for their support and interest. This work was funded in part by the Agency for Healthcare Research and Quality under Partnership in Patient Safety grant #HS015921-01 whose PI is Twila Burdick of Banner Health. We also thank two anonymous referees for valuable improvements to this manuscript.
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Cochran, J., Roche, K. A queuing-based decision support methodology to estimate hospital inpatient bed demand. J Oper Res Soc 59, 1471–1482 (2008). https://doi.org/10.1057/palgrave.jors.2602499
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DOI: https://doi.org/10.1057/palgrave.jors.2602499