Artificial Intelligence in Primary Care: Perceptions and Applications in Medical Clinic Operations (Preprint)
Bibliographic record
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. CLINICALTRIAL Not applicable.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".