My research addresses a fundamental gap in modern medicine: we know which treatments work, yet millions of patients never receive them or do not take them consistently. For over two decades, this work has centered on designing, rigorously testing, and scaling solutions to that gap — combining behavioral science with artificial intelligence and digital technology across the full lifecycle of prescribing, from helping patients take the medications they need to safely stopping those they no longer should.
Why patients don't take their prescribed medications — and how to fix it at population scale. Despite robust evidence for many treatments, nearly half of patients with chronic conditions do not take their medications as prescribed. The consequences are profound: preventable hospitalizations, strokes, and deaths.
This work has encompassed a wide range of interventions tested in partnership with health insurers and delivery systems across the United States — including financial incentives that eliminate cost barriers, behavioral nudges drawing on insights from economics and psychology, pharmacist-led coaching, and reminder devices.
Behavioral interventions work better when they are precisely targeted. A critical frontier in my work is predicting which patients will become non-adherent — before it happens — and using that prediction to deliver interventions to those most likely to benefit.
This research has developed and validated novel quantitative methods for clustering patients into longitudinal adherence trajectories, demonstrated the capacity to predict trajectory membership with high accuracy using claims data and electronic health records, and explored novel data sources — including retail purchasing information — to improve predictive ability. The application of reinforcement learning represents a particularly promising direction: algorithms that learn, in real time, which message content elicits adherence behavior for a given individual — and adapt automatically.
The medication use problem is bidirectional. Just as too many patients fail to take medications they need, too many patients — especially older adults — continue taking medications that are potentially harmful, unnecessary, or no longer appropriate. Deprescribing these high-risk medications is a recognized priority, yet clinician behavior is difficult to change.
More broadly, improving prescribing quality requires addressing not just what patients take, but how and what clinicians prescribe — including reducing harmful or unnecessary prescribing and increasing appropriate preventive care. My work in this area applies behavioral science and electronic health record–based tools to change prescribing behavior at the point of care.
Effective interventions mean nothing if they don't reach patients. My research examines the policy conditions under which behavioral interventions succeed and fail — including the role of benefit design, drug costs, and insurance structure in determining whether patients receive the medications they need.
This includes work on value-based insurance design (VBID), comparative effectiveness research, cost-effectiveness modeling, and the design of large-scale trials within health insurance systems. Much of this work has been conducted in direct partnership with health systems and insurers positioned to implement findings at scale.
In NEJM, JAMA, Health Affairs, BMJ, and other leading journals.