Triaging Patients With Artificial Intelligence for Respiratory Symptoms in Primary Care to Improve Patient Outcomes: A Retrospective Diagnostic Accuracy Study
PURPOSE: Respiratory 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, possib...
| Published in: | The Annals of Family Medicine |
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| Main Authors: | , , , |
| Format: | Text |
| Language: | English |
| Published: |
American Academy of Family Physicians
2023
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| Subjects: | |
| Online Access: | http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10202502/ http://www.ncbi.nlm.nih.gov/pubmed/37217331 https://doi.org/10.1370/afm.2970 |
| Summary: | PURPOSE: Respiratory 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. METHODS: We 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 International Classification of Diseases 10th Revision codes (J00, J10, JII, J15, J20, J44, J45). All primary care clinics in the Reykjavík area of Iceland were included. The model scored patients in 2 extrinsic data sets and divided them into 10 risk groups (higher values having greater risk). We analyzed selected outcomes in each group. RESULTS: Risk 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. CONCLUSIONS: The 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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