Writings

Complex models don't outperform simple ones at the health plan level. A deep analysis using AHRQ MEPS data showing that a 12-question health status survey can be as accurate as full HCC diagnosis models — with far lower administrative costs.

A Society of Actuaries publication providing a comprehensive overview of risk scoring fundamentals, adjustment frameworks, data requirements, and model validation. Co-authored with Geof Hileman and Margorie Rosenberg.

Published in the SOA Health Watch newsletter — exploring social determinants of health and non-clinical variables as potential inputs to risk adjustment models.

An earlier analysis of how behavioral, socioeconomic, and geographic data could improve the predictive accuracy of risk adjustment models without increasing coding burden.

Risk adjustment models are presented with false precision. This paper examines the inherent uncertainty in risk scores and what it means for payment accuracy and policy design.

Winner of the Society of Actuaries research prize. A complexity modeling approach to healthcare exchanges that anticipated many of the design challenges seen in ACA implementation.

An exploration of measurement fundamentals in actuarial modeling — when precision is illusory, and what we're really doing when we "count" in complex systems.

An early career comparison of the major risk adjustment methodologies — DCG/HCC, ERG, ACG — with empirical performance data across commercial and Medicare populations.

Reflections on digital transformation — how technology reshapes not just what we do, but how we think, connect, and find meaning.

Nassim Nicholas Taleb's Black Swan had a profound impact on how I think about risk. A review of the ideas that stuck — and the ones I push back on.