ArticleAnnals of family medicine
Triaging Patients With Artificial Intelligence for Respiratory Symptoms in Primary Care to Improve Patient Outcomes: A Retrospective Diagnostic Accuracy Study.
Article in Annals of family medicine. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Review
- AI Triage in Primary Care: Building Safer and More Equitable Real-World Evidence.Journal of medical Internet research · 2026Article
- Innovative Applications and Challenges of Artificial Intelligence in the Whole-Course Management of Chronic Obstructive Pulmonary Disease.International journal of chronic obstructive pulmonary disease · 2026Review
- Artificial Intelligence in Outpatient Primary Care: A Scoping Review on Applications, Challenges, and Future Directions.Journal of general internal medicine · 2026Article
- Should we leave paediatric emergency triage to artificial intelligence? A comparison of ChatGPT 4o and Grok 3.Frontiers in pediatrics · 2026Article
- Patients' perspectives regarding antibiotic treatment for acute sinusitis in Norwegian general practice. A qualitative interview study.Scandinavian journal of primary health care · 2025Article
- AI and Primary Care: Scoping Review.Journal of medical Internet research · 2025Article
- Artificial Intelligence in Optimizing the Functioning of Emergency Departments; a Systematic Review of Current Solutions.Archives of academic emergency medicine · 2024Review
- A comparison of self-triage tools to nurse driven triage in the emergency department.PloS one · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeRespiratory symptoms are the most common presenting complaint in primary care. Often these symptoms are self resolving, but they can indicate a severe illness. With increasing physician workload and health care costs, triaging patients before in-person consultations would be helpful, possibly offering low-risk patients other means of communication. The objective of this study was to train a machine learning model to triage patients with respiratory symptoms before visiting a primary care clinic and examine patient outcomes in the context of the triage.
methodsWe trained a machine learning model, using clinical features only available before a medical visit. Clinical text notes were extracted from 1,500 records for patients that received 1 of 7
resultsRisk groups 1 through 5 consisted of younger patients with lower C-reactive protein values, re-evaluation rates in primary and emergency care, antibiotic prescription rates, chest x-ray (CXR) referrals, and CXRs with signs of pneumonia, compared with groups 6 through 10. Groups 1 through 5 had no CXRs with signs of pneumonia or diagnosis of pneumonia by a physician.
conclusionsThe model triaged patients in line with expected outcomes. The model can reduce the number of CXR referrals by eliminating them in risk groups 1 through 5, thus decreasing clinically insignificant incidentaloma findings without input from clinicians.
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