Our Research

Artificial intelligence (AI) has primarily enhanced individual primary care visits, yet its potential for population health management remains untapped. Effective AI should integrate longitudinal patient data, automate proactive outreach, and mitigate disparities by addressing barriers such as transportation and language. Properly deployed, AI can significantly reduce administrative burden, facilitate early intervention, and improve equity in primary care.

We conducted a natural experiment study using difference-in-differences analysis of propensity score–matched US counties from 2011 through 2019. Loss of CHC sites was associated with an increase in age-adjusted all-cause mortality of 3.54 deaths per 100 000 population (95% CI: 1.19, 5.90; p = 0.003) in the year following the loss. The largest increase was observed for cancer mortality (2.61 per 100 000; 95% CI: 0.59, 4.62; p = 0.011). Primary care physician density and patient volume loss both mediated the relationship.

We found that the benefits of expansion generally balanced out the costs while redistributing health from higher to lower income groups. In probabilistic sensitivity analysis, we found—using a health opportunity cost threshold of $150,000—that Medicaid expansion was cost-effective in reducing CVD outcomes 53% of the time and both cost-effective (efficient) and equity enhancing 26% to 29% of the time.

In this observational study, we used a target trial emulation framework to estimate the potential of addressing transportation barriers to improve T2DM outcomes. Specifically, we sought to estimate whether not experiencing transportation barriers, compared with experiencing them, would be associated with better glycemic, blood pressure, and cholesterol outcomes among adults with T2DM in a network of community-based health centers.