The Potential of AI as a Clinical Co‑Pilot
Key Takeaways:
- Artificial intelligence (AI) functions as a clinical “co-pilot” by identifying patients most likely to engage in care, targeting high-risk individuals for intensive management, and supporting improvements in quality metrics and total cost of care.
- Automation of tasks such as lab review, documentation, and trend analysis reduces clinician administrative burden, helping alleviate burnout and sustain primary care capacity.
- Predictive analytics can streamline approvals, enhance utilization management, and enable payer–provider collaboration, particularly for chronic disease management and preventive care.
How is the integration of AI as a clinical “co-pilot” supporting better outcomes—particularly in terms of quality metrics, patient engagement, and total cost of care?
Gregory Whisman, MD, MBA: AI can help enhance care by identifying patients who are more likely to engage and complete required services. In addition, it can help to identify high-risk patients for more tailored, intensive management.
Clinician burnout and workforce shortages have ripple effects on access and cost. How can AI potentially be leveraged to help sustain primary care capacity and reduce burnout?
Dr Whisman: The biggest impact will likely be to make clinicians more efficient. Examples of this would be to aggregate tests and orders to identify trends, suggest next tests, and even proactively put them in pending orders. Right now, that administrative burden falls solely on clinicians and increases administrative time, which is triggering a lot of the burnout.
AI is meant to help relieve clinicians of administrative burdens like lab review and documentation. How do these efficiencies translate into measurable benefits—such as fewer denied claims, faster prior authorizations, or improved utilization management?
Dr Whisman: By aggregating data within the record, AI can be predictive about what tests should be ordered and what clinical information would be needed to ensure they would be approved. For health insurers, AI can predict and manage approvals while flagging specific authorizations that may not meet medical criteria for additional physician review.
As AI adoption accelerates, where do you see the greatest opportunities for managed care organizations to partner with providers on AI-driven initiatives? Are there specific use cases—such as chronic disease management or preventive care—where you anticipate the biggest impact?
Dr Whisman: Honestly, it is still nascent technology. The ability to identify patients who are more likely to utilize health services in a predictive manner will be a huge innovation if done correctly. Then, having organizations prepared to act on that identification will be critical. Thus, the symbiosis of AI integration with team-based care will be critical.
Author Information
Gregory Whisman, MD, MBA, is the chief medical advisor at CareMore Health. He is an experienced physician leader with a demonstrated history of working in health care leadership. He oversees the Medical Services Organization, including case management, medical management, and pharmacy. Dr Whisman develops, implements, and directs the clinical activities that impact health care quality and cost outcomes across the continuum.
