Human- Versus Machine Learning-Based Triage Using Digitalized Patient Histories in Primary Care: Comparative Study

Research output: Contribution to journalArticle

Abstract

BACKGROUND: Smartphones have made it possible for patients to digitally report symptoms before physical primary care visits. Using machine learning (ML), these data offer an opportunity to support decisions about the appropriate level of care (triage).

OBJECTIVE: The purpose of this study was to explore the interrater reliability between human physicians and an automated ML-based triage method.

METHODS: After testing several models, a naïve Bayes triage model was created using data from digital medical histories, capable of classifying digital medical history reports as either in need of urgent physical examination or not in need of urgent physical examination. The model was tested on 300 digital medical history reports and classification was compared with the majority vote of an expert panel of 5 primary care physicians (PCPs). Reliability between raters was measured using both Cohen κ (adjusted for chance agreement) and percentage agreement (not adjusted for chance agreement).

RESULTS: Interrater reliability as measured by Cohen κ was 0.17 when comparing the majority vote of the reference group with the model. Agreement was 74% (138/186) for cases judged not in need of urgent physical examination and 42% (38/90) for cases judged to be in need of urgent physical examination. No specific features linked to the model's triage decision could be identified. Between physicians within the panel, Cohen κ was 0.2. Intrarater reliability when 1 physician retriaged 50 reports resulted in Cohen κ of 0.55.

CONCLUSIONS: Low interrater and intrarater agreement in triage decisions among PCPs limits the possibility to use human decisions as a reference for ML to automate triage in primary care.

Details

Authors
Organisations
External organisations
  • Karolinska Institutet
  • Smartera AB
  • Doctrin AB
  • KTH Royal Institute of Technology
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Health Care Service and Management, Health Policy and Services and Health Economy
Original languageEnglish
Pages (from-to)e18930
JournalJMIR medical informatics
Volume8
Issue number9
Publication statusPublished - 2020 Sep 3
Publication categoryResearch
Peer-reviewedYes

Bibliographic note

©Artin Entezarjou, Anna-Karin Edstedt Bonamy, Simon Benjaminsson, Pawel Herman, Patrik Midlöv. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 03.09.2020.