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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
1Department of Dermatology, Mayo Clinic, Rochester, Minnesota.
Background:
Implementing Mohs case complexity grading systems is critical due to inherent complexities of Mohs workflow coordination. Precise scheduling can improve wait times and resource allocation.
Objective:
To predict Mohs number of stages and reconstruction type as surrogates of case complexity and to use information derived from model training and practice to develop scheduling recommendations.
Methods:
Machine learning models were trained to predict number of Mohs stages and complex reconstruction across multiple practices and surgeons in an institutional enterprise, using information available on pathology reports. A workflow analysis by health systems engineering was conducted to identify improvement areas for efficiency. Information from modeling and workflow analysis was combined to develop fixed rules for scheduling recommendations.
Results:
In total, 18,473 Mohs cases were used for training/validation and 2,053 for testing. Models achieved an AUC-ROC of 0.83 (95% CI: 0.81-0.85) for prediction of complex reconstruction and 0.62 (0.60-0.66) for number of stages (1 vs 2 vs 3 or more). Anatomical location was the most valuable feature, with midface locations predicting higher case complexity and longer surgical time.
Conclusion:
We demonstrated feasibility for prediction of Mohs surgery case complexity. Combining machine and human-derived knowledge provides complementary insights that can assist with improving scheduling and workflow.
