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Can we predict when an operating list will finish?

A L Widdison1

  • 1Department of Surgery, Norfolk and Norwich Hospital.

Annals of the Royal College of Surgeons of England
|November 1, 1995
PubMed
Summary
This summary is machine-generated.

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Predicting operating list completion times using mean procedure durations showed moderate accuracy for early or late finishes. While reliable for identifying over/under-booking, this method alone won't optimize surgical list utilization.

Area of Science:

  • Healthcare Management
  • Surgical Operations Research
  • Anesthesiology

Background:

  • Optimizing operating list efficiency is crucial for healthcare resource management.
  • Accurate prediction of surgical case durations is essential for effective scheduling.
  • Current methods for predicting operating list completion times have limitations.

Purpose of the Study:

  • To evaluate the predictability of elective general surgical operating list finishing times using mean procedure durations.
  • To assess the accuracy and reliability of predictions for early, on-time, and late finishes.
  • To determine if mean procedure times can effectively identify under or over-utilization of surgical lists.

Main Methods:

  • Analysis of mean anesthetic, surgical, and turnover times for elective general surgical cases.

Related Experiment Videos

  • Development of a predictive model for operating list finishing times.
  • Calculation of prediction accuracy, sensitivity, and false positive rates for early, on-time, and late finishes.

    • Main Results:

    • Predicted early finishes were correct in 70% of cases, on-time finishes in 19%, and late finishes in 56%.
    • Predictions for early or late finishes demonstrated low sensitivity (62% and 65%) and high false positive rates (30% and 44%).
    • Reliable prediction of over-runs (due to too many cases) and early finishes (due to insufficient cases) was achieved.

    Conclusions:

    • Mean procedure times can help identify under or over-booking issues leading to list under or over-utilization.
    • Indiscriminate use of mean procedure times for prediction will not inherently improve overall list utilization.
    • Further refinement of predictive models is needed for enhanced operating list management.