- Center on Health Equity & Access
- Clinical
- Health Care Cost
- Health Care Delivery
- Insurance
- Policy
- Technology
- Value-Based Care
Structured Care Pathways and Predictive Modeling
Panelists discuss how clinical decision support tools, care pathways, and artificial intelligence can address primary care workforce shortages by providing real-time guidance, predictive modeling for high-risk patients, and autonomous agents for patient outreach and care coordination.
EP: 1.Hormonal Adaptations in Type 2 Diabetes and Obesity
EP: 2.Importance of Clinical Trials and Guideline Recommendations
EP: 3.Barriers Associated with Guideline-Directed Care
EP: 4.The Role of Weight Management and Glycemic Control in the Management of Type 2 Diabetes
EP: 5.Improving Patient Outcomes Beyond Glycemic Control
EP: 6.Taking a Cardiometabolic Health Approach in Patients with Type 2 Diabetes
EP: 7.Seamless Management Between Primary Care Providers and Specialists
EP: 8.Supporting Patients and Primary Care Providers
EP: 9.Optimizing the Clinician-Patient Relationship
EP: 10.Overcoming Weight Bias in Clinical Practice
EP: 11.Creating a Positive Culture Around Weight Discussions
EP: 12.Leveraging Registries and Data Analytics
EP: 13.Structured Care Pathways and Predictive Modeling
EP: 14.Additional Clinical Considerations for the Management of Obesity
Technology and Future Innovations
Artificial intelligence and predictive modeling represent potential solutions to primary care workforce shortages and the impossible demands of comprehensive chronic disease management. AI systems can support clinical decision-making by identifying patients who qualify for specific medications and prompting providers about evidence-based treatment options. These technologies may elevate the performance of all healthcare providers, particularly advanced practice providers, to near physician-level capabilities.
Predictive modeling can identify the 20% of patients who generate 80% of medical expenditures and complications, enabling resource allocation that matches patient complexity with appropriate care intensity. Future AI agents may autonomously interact with patients, conducting medication reconciliation, scheduling appointments, and managing routine follow-up care between visits.
The evolution toward right-sizing healthcare encounters based on patient complexity and needs represents a fundamental shift from current transaction-based models. An 18-year-old with a sore throat requires different resources than an 85-year-old with multiple chronic conditions, and technology can help match patients with appropriate care models. Genetic AI agents capable of autonomous patient interaction may soon revolutionize chronic disease management and patient engagement.
Integrated Care for Chronic Conditions: A Randomized Care Management Trial
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Managed Care Reflections: A Q&A With A. Mark Fendrick, MD, and Michael E. Chernew, PhD
December 2nd 2025To mark the 30th anniversary of The American Journal of Managed Care (AJMC), each issue in 2025 includes a special feature: reflections from a thought leader on what has changed—and what has not—over the past 3 decades and what’s next for managed care. The December issue features a conversation with AJMC Co–Editors in Chief A. Mark Fendrick, MD, director of the Center for Value-Based Insurance Design and a professor at the University of Michigan in Ann Arbor; and Michael E. Chernew, PhD, the Leonard D. Schaeffer Professor of Health Care Policy and the director of the Healthcare Markets and Regulation Lab at Harvard Medical School in Boston, Massachusetts.
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