AI in Primary Care: A Problem-First Approach

Last week I wrote about my search for a new primary care physician. Based on my medical physical exam experience and my involvement in the development and teaching of a couple of Artificial Intelligence (AI) courses, thoughts automatically went to where they seem to go a lot these days “Why not use AI?” So I did a little research. Bottom line – it still has a ways to go.

In a recent special report in the Annals of Family Medicine titled AI in Primary Care, Start With the Problem, Dr. John Thomas Menchaca argues for a strategic approach to implementing AI in primary care. Rather than pursuing AI for its own sake, physicians and developers must first identify the right problems to solve. Dr Menchaca compares misguided AI implementations to the Segway—a technological marvel that failed because it didn't address real needs. In contrast, electric scooters succeeded by solving the specific "last mile" problem in urban commuting. Similarly, AI must target precise pain points in healthcare.

Primary care's most pressing issue isn't clinical complexity but time management. Studies reveal full-time primary care physicians work over 11 hours daily, with more than half spent on electronic health record (EHR) tasks—a workload directly linked to high burnout rates. This data provides a clear roadmap for effective AI implementation. The most time-consuming EHR tasks include documentation (the largest time sink), chart review, medication management (which could save up to 2 hours daily based on studies with pharmacy technicians), triaging laboratory results, managing refills, responding to patient messages, and order entry.

Current AI documentation tools show mixed results. Many generate rough drafts requiring substantial editing, sometimes taking as much time as writing notes from scratch. This mirrors issues with traditional clinical decision support tools, which often increase rather than decrease workload. The challenge is developing AI that genuinely saves time in clinical settings by integrating seamlessly into workflows, minimizing oversight requirements, empowering team members to resolve issues independently, and measuring impact through time-saving metrics.

Dr Menchaca calls for academic medicine to bridge the gap between clinicians and developers through partnerships at national conferences, research focused on root causes of inefficiency, detailed workflow analyses, and implementation in organizations that truly prioritize clinician well-being. A key concern is that time saved by AI might simply be filled with additional work—more patients or administrative tasks—highlighting that technology alone cannot fix systemic issues in primary care delivery.

AI won't magically solve problems like overwhelming patient panels or overloaded schedules. As Dr Menchaca notes, "AI is just one tool—a means to an end, not the end itself." Meaningful solutions must ultimately lighten clinicians' workloads. By targeting specific, high-impact areas and measuring success through time saved, AI can contribute to a more sustainable future for primary care.

The message for AI innovators is clear: solve real problems, save real time, and keep the clinicians central to your design process. Only then can AI fulfill its potential to transform primary care by making existing systems work more efficiently rather than attempting to reinvent them.

And of course a disclaimer: I’m just a patient, not a doctor. I have done a lot of industry specific development work over the years though. From a development perspective, Dr Menchaca's approach sure makes a lot of sense.