Optimizing AI solutions for population health in primary care

A recent publication entitled “Optimizing AI solutions for population health in primary care” published in Nature NPJ Digital Medicine depicts the future of incorporating AI into clinical workflows.

AI has made significant inroads into healthcare, particularly in easing the administrative and cognitive burdens of clinicians during patient visits. Tools such as ambient scribes and clinical decision support systems have helped optimize individual encounters by reducing documentation time and supporting real-time decision-making. However, while these innovations represent a valuable start, they only scratch the surface of what AI can—and must—achieve in primary care. To address deeper challenges such as workforce shortages, fragmented care delivery, and persistent health inequities, AI must evolve to support population-level health management.

Current AI systems are largely reactive, operating within the confines of individual patient visits. But the future of AI in primary care lies in its ability to continuously monitor and analyze data across entire patient panels. Instead of waiting for patients to initiate contact, population-level AI can flag at-risk individuals based on electronic health records, claims data, pharmacy records, and even social service databases.

Building trust among providers is critical for the adoption of such tools. Clinicians need assurance that AI can handle tasks like appointment reminders, medication refills, and follow-up scheduling both safely and accurately. Importantly, AI systems must support, not replace, human oversight, especially when care decisions involve patients with complex or contraindicated conditions. When used appropriately, population-level AI can minimize missed care opportunities and reduce the burden on already overextended primary care teams.

AI also has the potential to advance health equity. AI can identify and reach out to patients who may face structural barriers such as language differences, transportation limitations, or socioeconomic constraints. Multilingual AI tools have already demonstrated increased engagement rates in outreach programs for underserved populations, and more personalized, culturally-sensitive approaches can further bridge gaps in access to care.

In the context of value-based care, where providers are incentivized to prevent costly complications rather than maximize visit volume, AI becomes even more essential. Studies have shown that AI-generated "chase lists" for care teams can lead to significant reductions in emergency visits and hospitalizations, while also helping clinics allocate resources more effectively.

Despite its promise, the development and deployment of population-level AI is not without challenges. Issues such as algorithmic bias, outdated models, and variable data quality in electronic health records must be addressed. Additionally, there is a risk of "automation bias," may miss important nuances due to reliance on AI services. To mitigate these concerns, AI systems must be transparent, interpretable, and continuously updated through feedback and rigorous evaluation.

While AI has already begun to ease documentation and decision-making in individual encounters, its most transformative potential lies in managing population health. By analyzing data at scale, AI can proactively identify risks, support timely interventions, and reduce disparities, helping clinics shift from reactive care to a more efficient, equitable, and responsive model.