JMIR Publications

ABSTRACT

Background:

Artificial intelligence (AI) is poised to enhance primary care operations by automating administrative tasks and streamlining workflows, yet adoption in clinics remains limited and uneven.

Objective:

To characterize medical clinic personnel’s perceptions of integrating AI—particularly ChatGPT—into routine operations and to map the current evidence base relevant to primary care.

Methods:

We used a mixed-methods design comprising a scoping literature review, an online survey informed by Canada Health Infoway resources, and semi-structured interviews to capture local context. Quantitative data were summarized descriptively; qualitative data underwent thematic analysis to identify perceived opportunities, barriers, and readiness.

Results:

The scoping review identified 21 publications across 14 countries. Most were narrative reviews or conceptual commentaries emphasizing opportunities for AI scribes, chatbots, and real-time analytics, alongside concerns about ethics, professional identity, and training needs. Empirical studies (surveys and mixed-methods approach) reflected cautious optimism toward adoption. Locally, same sizes for survey (n=15) and interview (n=5) respondents reported high awareness and interest but limited operational use; at the time of data collection, two clinics reported routine use of ChatGPT. Interviews underscored demand for AI-enabled automation (e.g., documentation, triage, patient messaging) and noted active efforts to secure implementation funding. Across sources, anticipated benefits centered on efficiency (time savings, documentation accuracy), while adoption hinged on trust, perceived value, and organizational readiness, which corresponded with global findings of the literature of AI in primary.

Conclusions:

Primary care medical clinics view AI mostly as ChatGPT application (not LLMs). The clinic operators’ perceptions are promising for near-term efficiency gains in low-risk administrative workflows, but real-world use remains nascent. Implementation efforts should prioritize governance, training, and evaluation frameworks that track time, accuracy, and user experience, while addressing ethical and workforce considerations to support sustainable adoption. Clinical Trial: Not applicable.