Enterprise AI Analysis
Artificial Intelligence in Outpatient Primary Care: A Scoping Review on Applications, Challenges, and Future Directions
Artificial intelligence (AI) has significant potential to impact clinical decision-making and improve patient outcomes in outpatient primary care. However, despite rapid advancements, the extent of AI implementation in outpatient primary care remains unclear. This scoping review explores how AI functions, undergoes trials, or integrates into non-urgent outpatient primary care settings. This scoping review was conducted in accordance with the Joanna Briggs Institute methodology and reported following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. We searched MEDLINE, CINAHL, Scopus, and clinicaltrials.gov databases. Eligible studies were peer-reviewed articles published in English between January 2019 and November 22, 2024, examining AI applications in primary care settings with a direct focus on patient care. Studies were excluded if they were not in English, did not address primary care workflows, or if the full text was unavailable. We added clinicaltrials.gov to uncover active protocols that suggested wider potential adoption. We used thematic analysis to synthesize findings related to AI application domains, research stage, and status of implementation. Overall, based on this scoping review of peer-reviewed literature, AI in primary care remains in the developmental stage, with minimal real-world use beyond ambient scribing, clinical decision support, and workflow automation.
Executive Impact & Key Findings
Our analysis reveals the current landscape of AI integration in outpatient primary care, highlighting both progress and areas requiring further development.
61
Total Studies Analyzed
43%
Studies in Development Stage
10%
Studies in Clinical Use
2,854
Manuscripts Screened
Deep Analysis & Enterprise Applications
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Focus on Early Detection and Predictive Models
Of the 61 studies included, 15 (25%) focused on disease diagnosis, and 14 (23%) on risk prediction, making these the two most common areas of investigation. Researchers applied AI tools to detect cardiovascular disease, diabetes, dermatopathology, cognitive impairment, lung cancer risk, and mental health conditions. Most studies used retrospective EHR data, while several integrated wearable or patient-reported data, reporting improvements in diagnostic accuracy compared to standard care or clinician-only assessments. However, methods varied widely, and very few linked predictions directly to patient outcomes.
AI as a Cognitive Aid for Primary Care Providers
Clinical decision-making was the aim of 9 (15%) studies, while 5 (8%) targeted PCP cognitive support. Studies examined decision support for prescribing and guideline adherence, personalized treatment planning, mental health crises, and documentation automation. Most of these studies were pilot trials or simulations, and they rarely evaluated downstream effects on patient outcomes.
Streamlining Practice Workflows and Reducing Clinician Burden
Several recent studies evaluated AI tools that reduce clinician burden and streamline practice workflows. Researchers tested applications such as automated chart review, natural language processing for documentation, and AI-powered triage for patient messages. Most reported improved efficiency, but few studies assessed whether efficiency gains allow providers to spend more time with patients or improve patient satisfaction.
Enterprise Process Flow
Potentially relevant manuscripts identified (n=3,203)
→
Duplicate manuscripts identified by Covidence (n=349)
→
Manuscripts screened (n=2,854)
→
Full text manuscripts reviewed for eligibility (n=105)
→
Manuscripts included in Scoping Review (n=61)
25%
Studies Focused on Disease Diagnosis
23% Studies Focused on Risk Prediction
23% Studies Focused on Risk Prediction
| Category | Ideation | Development | Validation | Protocol | Trial | Use | Total |
|---|---|---|---|---|---|---|---|
| PCP cognitive support | 0 | 0 | 0 | 0 | 0 | 5 | 5 |
| Clinical decision-making | 1 | 5 | 2 | 0 | 0 | 1 | 9 |
| Disease diagnosis | 0 | 7 | 5 | 0 | 2 | 1 | 15 |
| Risk prediction | 0 | 11 | 3 | 0 | 0 | 0 | 14 |
Strategic Recommendations for AI Integration in Primary Care
The review highlights critical challenges including securing NIH funding for non-disease-specific projects, limited willingness from busy practices to test unproven technologies, and the rapid deployment of commercial AI tools without academic validation. To bridge these gaps, federal and state governments should fund large-scale clinical trials. Establishing rigorous standards for data security, privacy, and transparent reporting of LLM use is crucial. Finally, strong partnerships between AI developers and healthcare delivery experts are essential to ensure equitable access and effective integration of AI into primary care, ultimately benefiting all patients and providers.
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Phase 1: Ideation (6-12 Months)
Early stage investigations and theoretical frameworks, comprising 10% of studies. Focus on identifying potential AI applications and assessing feasibility within primary care settings.
Phase 2: Development (12-24 Months)
The most common stage, accounting for 43% of studies. This phase involves building and refining AI/ML models, often using retrospective data, with emphasis on algorithm development and initial testing.
Phase 3: Validation (18-30 Months)
Comprises 25% of the reviewed studies. AI models are evaluated for performance in controlled environments or retrospectively, assessing their accuracy and reliability before real-world trials.
Phase 4: Protocol & Trial (24-48 Months)
Only 5% of studies are identified as protocols (registered trials) and 8% have reached the trial stage. This involves prospective testing within actual practice environments to assess practical efficacy and safety.
Phase 5: Clinical Use & Adoption (36+ Months)
Represents 10% of the studies, indicating AI applications implemented and actively evaluated in clinical workflows. This stage focuses on the integration into existing systems and assessing long-term impact on patient outcomes.
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