AI Triage in Primary Care: Building Safer Real-World Evidence
A new paper in the Journal of Medical Internet Research examines how AI-based triage systems in primary care settings can generate safer and more equitable real-world evidence — and the methodological challenges that come with it.
Why this matters
As health systems deploy AI triage tools, the data they generate flows into EHR systems and ultimately into observational databases like OMOP. Understanding how these tools affect data quality, patient selection, and outcome measurement is critical for researchers using RWE.
Key considerations for OMOP-based studies:
- Selection bias — AI triage may route patients differently, changing who appears in condition and procedure tables
- Measurement artifacts — automated assessments may create observation records that look different from clinician-entered data
- Equity implications — triage algorithms trained on biased data can amplify disparities visible in RWE analyses
Reference
Alamoudi A et al. AI Triage in Primary Care: Building Safer and More Equitable Real-World Evidence. J Med Internet Res. 2026.