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Short term hospital occupancy prediction

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Abstract

Inpatient census, or occupancy, is a primary driver of resource use in hospitals. Fluctuations in occupancy complicate decisions related to staffing, bed management, ambulance diversions, and may ultimately impact both quality of patient care and nursing job satisfaction. We describe our approach in building a computerized model to provide short-term occupancy predictions for an entire hospital by nursing unit and shift. Our model is a comprehensive system built using real hospital data and utilizes statistical predictions at the individual patient level. We discuss the results of piloting an early version of the model at a mid-size community hospital. The primary focus of the paper is on the development and methodology of a second generation of the predictive occupancy model. The results and accuracy of this new model is compared to a variety of other predictive methods based on tests using 2 years of actual hospital data.

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Authors and Affiliations

  1. Improvement Path Systems, Inc., Farmington Hills, MI, USA

    Steven J. Littig

  2. Department of Decision and Information Sciences, Oakland University, Rochester, MI, USA

    Mark W. Isken

  3. School of Business Administration, Oakland University, 317 Elliott Hall, Rochester, MI, 48309, USA

    Mark W. Isken

Authors
  1. Steven J. Littig
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  2. Mark W. Isken
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Correspondence to Mark W. Isken.

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Littig, S.J., Isken, M.W. Short term hospital occupancy prediction. Health Care Manage Sci 10, 47–66 (2007). https://doi.org/10.1007/s10729-006-9000-9

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  • DOI: https://doi.org/10.1007/s10729-006-9000-9

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