JMIR Publications

ABSTRACT

Background:

Primary care clinicians face clinical complexity alongside administrative burden from documentation, inbox work, prescriptions, referrals, and care coordination. Poor electronic health record usability can add work and is associated with dissatisfaction and burnout. Advances in artificial intelligence (AI) create an opportunity to relieve these pressures, but tools must be co-designed with clinicians, fit workflows, and be evaluated to avoid repeating the workload and usability problems of the electronic health record era.

Objective:

To identify current and near-term AI capabilities that health care AI experts viewed as relevant to primary care and to describe associated primary care AI project ideas suitable for early co-design and evaluation.

Methods:

We conducted a modified virtual nominal group technique (vNGT) with nine health care AI experts, including five physicians. The consultation was divided between two virtual meetings with non-overlapping participant groups. Participants generated and discussed current and emerging AI capabilities and associated primary care applications through silent idea generation, round-robin sharing, and facilitated discussion. Session outputs were consolidated after both meetings into capability and project-idea lists, followed by an individual post-session ranking survey. Participants ranked their top five project ideas and top five AI capabilities; ranks were converted to weighted points (rank 1=5 points; rank 5=1 point) and summed across respondents to generate descriptive weighted sum scores.

Results:

Participants generated two outputs: AI capabilities describing what systems would need to do and project ideas describing where they could be applied. Project ideas clustered around administrative relief (digital medical office assistant for forms, referrals, medication reviews, and scheduling; an electronic health record inbox manager), clinical encounter augmentation (ambient scribe plus micro-prompts), and pharmacy workflows (end-to-end prescribing support). Other domains addressed patient navigation and escalation, proactive identification of care gaps, and implementation readiness. Rankings were completed by 8 (projects) and 7 (capabilities) respondents. The digital medical office assistant received the highest project score (24), followed by the inbox manager (17); ambient scribe plus micro-prompts and the pharmacy agent tied (13 each). Retrieval-augmented generation (20) and clinical reasoning (19) received the highest capability scores, followed by multimodal understanding and generation and long-context handling (14 each), and function or tool calling (12). Discussion emphasized administrative overload, augmentation rather than clinical automation, workflow fit, clinician-led evaluation, interoperability, and reimbursement.

Conclusions:

Expert participants identified current and emerging AI capabilities and a focused set of near-term project ideas for workflow-embedded primary care AI, with emphasis on administrative relief, encounter support, reliable reasoning, and grounded generation. These findings provide a starting point for clinician-led co-design and prospective evaluation in real-world primary care settings, but successful implementation will depend on interoperability, rigorous evaluation, and viable payment models.