Healthcare practices and operations must face life-and-death challenges every day, and for best performance organizations need to minimize waste, yet fraudsters cost healthcare providers billions of dollars a year.
Current fraud detection methods often rely on broad-strokes predictions or brute force human analysis. Make the shift to machine learning-based models with this guidebook, which includes:
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A broader overview of the role of anomaly detection in healthcare (beyond fraud detection) and ways to integrate the process into existing workflows.
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Code samples for a simple machine learning-based stock optimization model, along with ways to customize and improve it.
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How to approach ROI calculations when determining the first steps towards machine learning integration.
