AI in Primary Care Unlocks Population Health Potential

Artificial intelligence (AI) has primarily focused on enhancing individual primary care visits, but its potential for population health management remains largely untapped. A new perspective argues that AI must evolve to address broader challenges like workforce shortages, fragmented care, and health inequities.

Current Limitations and Future Potential

• Current Use: AI tools like ambient scribe systems and clinical decision-support tools improve efficiency during individual visits (Tierney et al., 2025).
• Future Focus: AI should integrate longitudinal patient data, automate proactive outreach, and mitigate disparities by addressing barriers like transportation and language.

Proactive Care and Equity

AI can transform primary care by:

• Continuous Monitoring: Tracking EHRs, claims data, and social service databases to identify at-risk patients.
• Reducing Disparities: Multilingual AI agents have shown higher engagement rates among Spanish-speaking patients for colorectal cancer screening (Bhimani et al., 2024).
• Addressing Social Determinants: For example, AI can flag insulin-dependent patients facing food insecurity to prevent hypoglycemia.

Value-Based Care and Challenges

• Financial Incentives: AI aligns with value-based care by reducing avoidable ED visits and hospitalizations. A study showed a 48.3% reduction in ambulatory care–sensitive hospitalizations (Baum et al., 2024).
• Pitfalls: Risks include algorithmic bias, outdated models, and variability in data quality.

Rigorous Evaluation Needed

Prospective studies are essential to assess AI's impact on:

• Clinical Outcomes: Hospitalization rates, preventive care uptake.
• Workflow Efficiency: Reducing clinician burnout and alert fatigue.

Conclusion

AI's future in primary care lies in population health management. By analyzing vast datasets, AI can enable proactive interventions, improve equity, and optimize resource allocation—but only with rigorous evaluation and workflow integration.