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Determining Prior Authorization Approval for Lumbar Stenosis Surgery With Machine Learning.

Amaury De Barros^1,2, Frederik Abel³, Serhii Kolisnyk⁴

¹Toulouse NeuroImaging Center (ToNIC), University of Toulouse Paul Sabatier-INSERM, Toulouse, France.

Global Spine Journal

|February 8, 2023

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This summary is machine-generated.

Machine learning (ML) models can accurately predict surgical recommendations for lumbar spinal stenosis (LSS), matching the performance of medical directors. This demonstrates ML

Area of Science:

Orthopedics
Medical Informatics
Health Services Research

Background:

Lumbar spinal stenosis (LSS) is a common degenerative condition in the elderly, often necessitating surgery.
Prior authorization for LSS surgery is subjective and clinician-dependent, impacting patient access and healthcare costs.
Current methods for surgical candidate approval lack standardization and objective criteria.

Purpose of the Study:

To develop and evaluate a machine learning (ML) model for predicting surgical recommendations in LSS patients.
To compare the predictive accuracy of the ML model against a panel of medical directors (MDs).

Main Methods:

A random forest ML model was trained using medical vignettes.
The model utilized patient demographics, medical history, symptoms, physical exams, and imaging findings.

Keywords:

Lumbar spinal stenosis artificial intelligence machine learning spinal surgery surgical decision making

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Training and testing involved sets of 400 and 100 medical vignettes, respectively, reviewed by MDs.

Main Results:

The ML model achieved a root mean square error (RMSE) of 0.1123, significantly lower than the average MD RMSE of 0.2661.
For binary classification, the ML model's AUROC was 0.959 and Cohen's kappa was 0.801.
These metrics outperformed the average MD performance (AUROC 0.844, Cohen's kappa 0.564).

Conclusions:

ML models demonstrate high accuracy in predicting surgical necessity for LSS.
Automating prior authorization for LSS surgery using ML shows comparable performance to human expert panels.
This approach offers potential for more objective and efficient surgical candidate approval.