Risk Scoring Models in Healthcare Data Analytics: A Practitioner’s Perspective on Machine Learning, Interpretability, and Regulatory Compliance

The development of risk scoring models has significantly contributed to the field of healthcare analytics because of its capabilities in predicting diseases, managing healthcare resources, and even affecting policy formulation. Advanced machine learning (ML) techniques have been developed over time that are outpacing the ability of traditional statistical models to achieve their goals. However, concerns regarding model transparency, real-world validation, and compliance with regulatory bodies, chiefly in relation to HIPAA and HCC in the US, persist. This paper presents consolidated comparisons of testimonies acquired during the rollout of an enterprise scale risk scoring module leveraging patient health records and claims documents focusing on effective model design, interpretability, and compliance imperatives. The objective of this paper is to provide other healthcare practitioners and researchers who wish to employ sophisticated risk scoring systems which can function seamlessly within the bounds of legal compliance and sufficient explanation afterwards with both compliance and practical aspects in mind.