Machine learning for sub-population assessment: evaluating the C-section rate of different physician practices

Area of Science:

• Healthcare Analytics
• Machine Learning Applications
• Maternal Health Outcomes

Background:

• Caesarian Section (C-section) rates vary significantly among different physician groups.
• Understanding the drivers of these variations is crucial for improving maternal healthcare.
• Subpopulation assessment aims to differentiate between patient intrinsic risk and physician practice.

Purpose of the Study:

• To apply machine learning for subpopulation assessment of C-section rates.
• To determine if observed C-section rate variations are due to patient risk or physician practice.
• To analyze C-section data from a large cohort of 22,176 expectant mothers.

Main Methods:

• Utilized machine learning algorithms for predictive modeling.
• Analyzed C-section outcomes across 17 distinct physician groups.
• Quantified C-section rates, ranging from 11% to 23% within the studied population.

Main Results:

• Identified variations in C-section rates attributable to both patient subpopulations and physician practices.
• Found that while some patient risk variation exists, physician practice differences play a substantial role.
• Machine learning models helped disentangle the contributions of patient risk and physician behavior.

Conclusions:

• Physician practice variations significantly contribute to differing C-section rates among patient groups.
• Further investigation into practice patterns can help standardize and optimize C-section delivery.
• Machine learning offers a powerful tool for analyzing complex healthcare outcome disparities.