Percentages of Surgical Procedure Combinations That Were Performed Just Once or Twice at Florida Hospital and Ambulatory Surgery Centers During Each Quarter From 2010 Through 2024

Although surgical procedure(s) are the most important predictors of operating room time, many procedures are performed rarely, resulting in little historical case duration data for case duration estimation. Freestanding ambulatory surgery centers have, for more than 10 years, remained different from hospitals in performing more common procedures. Hospitals should plan, when scheduling cases, that approximately 84% of distinct combinations of procedures and 44% of cases will have little to no procedure-specific historical data, and even less so by the combination of procedure and surgeon. Machine learning models for predicting case durations should therefore account for these uncommon procedures, for which Bayesian methods are well suited.

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Publication Details

Journal
Cureus
Published
2026-02-10
DOI
https://doi.org/10.7759/cureus.103338
Primary Topic
Medical Coding and Health Information
Type
article
Field-Weighted Citation Impact
0.00
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article

Percentages of Surgical Procedure Combinations That Were Performed Just Once or Twice at Florida Hospital and Ambulatory Surgery Centers During Each Quarter From 2010 Through 2024

Richard H. Epstein, Franklin Dexter, Brenda G Fahy
Cureus
Medical Coding and Health Information
article

Percentages of Surgical Procedure Combinations That Were Performed Just Once or Twice at Florida Hospital and Ambulatory Surgery Centers During Each Quarter From 2010 Through 2024

Richard H. Epstein, Franklin Dexter, Brenda G Fahy
article en

Abstract

Although surgical procedure(s) are the most important predictors of operating room time, many procedures are performed rarely, resulting in little historical case duration data for case duration estimation. Freestanding ambulatory surgery centers have, for more than 10 years, remained different from hospitals in performing more common procedures. Hospitals should plan, when scheduling cases, that approximately 84% of distinct combinations of procedures and 44% of cases will have little to no procedure-specific historical data, and even less so by the combination of procedure and surgeon. Machine learning models for predicting case durations should therefore account for these uncommon procedures, for which Bayesian methods are well suited.

CureusVol. 18(2)
University of Iowa (US), University of Miami (US), University of Florida (US)
Good health and well-being
Openalex Percentile: Top 62%
Medical Coding and Health Information
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