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Modelling variability in hospital bed occupancy.

Gary W Harrison1, Andrea Shafer, Mark Mackay

  • 1Department of Mathematics, College of Charleston, Charleston, SC, USA. harrisong@cofc.edu

Health Care Management Science
|December 29, 2005
PubMed
Summary
This summary is machine-generated.

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This study introduces a stochastic hospital patient flow model to accurately predict occupancy variability and reduce overflows. The model helps hospitals optimize bed allocation and understand factors contributing to patient overflow risks.

Area of Science:

  • Operations Research
  • Healthcare Management
  • Stochastic Modeling

Background:

  • Hospital occupancy planning is challenged by variability, not just averages.
  • High occupancy increases overflow risks, impacting patient care and resource allocation.

Purpose of the Study:

  • Develop a stochastic model for hospital patient flow to capture occupancy variability.
  • Improve planning by accurately predicting occupancy fluctuations and overflow risks.

Main Methods:

  • Adapted the Harrison-Millard multistage model into a stochastic framework.
  • Modeled patient admissions using a Poisson process with time-varying rates (day of week, season).
  • Estimated model parameters using a year of hospital data, validating with six months of unseen data.

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Main Results:

  • The stochastic model accurately replicates the mean, standard deviation, and autocorrelation of occupancy data.
  • Simulations identified normal fluctuation ranges and substantive deviations from historical patterns.
  • Seasonal variations and variable discharge rates were found to be more significant contributors to overflows than day-of-week variations or variable admission rates.

Conclusions:

  • The developed stochastic model provides a robust tool for hospital capacity planning and risk management.
  • Larger hospital divisions achieve higher occupancy efficiency than smaller ones for equivalent overflow frequencies.
  • Understanding variability drivers is crucial for mitigating hospital overflows and optimizing resource utilization.