Most health AI papers are about how to new ways to build better predictive models. But this post highlights a different kind of work: it's about new ways to use them. In this study, AI models are a tool to probe and understand human decision-making. (A summary also appeared in JAMA.)
Testing for a heart attack is complex. The researchers focus on testing patterns for acute coronary syndrome (heart attack) in the ED. The decision to test is complex. Many heart attacks don't involve stereotypical symptoms like chest pain. And definitive diagnosis is invasive and expensive, because it often requires catheterization.
Using AI to probe human decision-making. The researchers build an AI model to predict the risk of heart attack from EHR data. The technical methodology is standard, but that is not the main point. Rather, the idea is to compare model predictions to testing patterns and use them to detect both under- and over-testing.
Inefficiency is not just overtesting. It turns out that undertesting is also a big problem. For example, the authors show that patients with chest pain tend to be overtested, while patients without it are undertested. The way to improve is the system is, of course, to test the right patients.
A framework for insight generation. The paper takes an economic rather than clinical outlook. But, it offers a useful and approachable framework to gain clinical, operational, and financial insight. It is relevant for any healthcare organization trying to identify areas for improvement, especially in value-based settings.