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The CLASS Project: A Proof-of-Concept Machine Learning-Driven Complexity Level Algorithm for Surgical Scheduling in

Jorge A Rios-Duarte1, Heather D Hardway1,2, Nahid Y Vidal2,3

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PubMed
Summary

Predicting Mohs surgery complexity using machine learning can optimize scheduling. Combining AI insights with workflow analysis improves efficiency and patient wait times for Mohs procedures.

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Area of Science:

  • Dermatology
  • Surgical Oncology
  • Health Systems Engineering

Background:

  • Mohs surgery workflow coordination presents inherent complexities.
  • Effective case complexity grading is crucial for efficient scheduling.
  • Precise scheduling can reduce patient wait times and optimize resource allocation.

Purpose of the Study:

  • To predict Mohs surgery case complexity by estimating the number of stages and reconstruction type.
  • To develop data-driven scheduling recommendations by integrating model insights and workflow analysis.

Main Methods:

  • Machine learning models were trained using pathology report data to predict Mohs stages and complex reconstruction.
  • A health systems engineering approach was employed for workflow analysis to identify efficiency improvements.
  • Scheduling recommendations were developed by combining machine learning predictions and workflow analysis findings.

Main Results:

  • Machine learning models demonstrated feasibility in predicting Mohs case complexity.
  • Models achieved an AUC-ROC of 0.83 for complex reconstruction prediction and 0.62 for number of stages.
  • Anatomical location, particularly midface, was a key predictor of higher complexity and longer surgical duration.

Conclusions:

  • The study demonstrated the feasibility of predicting Mohs surgery case complexity.
  • Integrating machine-derived and human-derived knowledge offers complementary insights for workflow enhancement.
  • These combined insights can significantly improve Mohs surgery scheduling and overall workflow efficiency.