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Estimation of Surgery Durations Using Machine Learning Methods-A Cross-Country Multi-Site Collaborative Study.

Sean Shao Wei Lam1,2, Hamed Zaribafzadeh3,4, Boon Yew Ang1

  • 1Health Services and Systems Research, Duke-NUS Medical School, Health Services Research Centre, Singapore Health Services, Singapore 169856, Singapore.

Healthcare (Basel, Switzerland)
|July 27, 2022
PubMed
Summary
This summary is machine-generated.

Machine learning models accurately predict surgery durations, outperforming traditional methods for operating room scheduling. This advancement improves efficiency in surgical planning across international healthcare systems.

Keywords:
big data analyticsdata sharingmachine learningmulti-countrymulti-sitesurgical durations

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Area of Science:

  • Healthcare Operations Research
  • Medical Informatics
  • Surgical Workflow Optimization

Background:

  • Accurate prediction of surgery duration is crucial for efficient operating room (OR) slot scheduling.
  • Existing Moving Average (MA) methods provide baseline estimates for surgery durations.
  • Cross-institutional data sharing presents challenges but is vital for robust analytics.

Purpose of the Study:

  • To evaluate novel machine learning (ML) models against existing Moving Average (MA) models for predicting surgery durations.
  • To assess the performance of ML models across two international sites (US and Singapore).
  • To demonstrate the utility of the Duke Protected Analytics Computing Environment (PACE) for cross-border data analytics.

Main Methods:

  • Utilized colorectal surgery data from Singapore (2012-2017) and the US (2015-2019).
  • Developed categorical gradient boosting (CatBoost) ML models trained on shared data fields.
  • Mapped Singapore's Table of Surgical Procedure (TOSP) codes and US Current Procedural Terminology (CPT) codes, transforming CPT to relative value units (RVU).

Main Results:

  • ML models significantly outperformed baseline MA models in predicting surgery durations.
  • Procedure codes and scheduled durations showed higher predictive loadings than surgeon factors.
  • The Duke PACE facilitated effective data sharing and big data analytics between US and Singapore sites.

Conclusions:

  • ML-based models offer superior accuracy for surgery duration prediction compared to MA methods.
  • The study highlights the importance of procedure coding and scheduled durations in predictive models.
  • Duke PACE is a valuable platform for enabling collaborative, big data research in healthcare.