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

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Entezarjou, ArtinKarolinska Institutet,Lund University,Lunds universitet,Allmänmedicin och samhällsmedicin,Forskargrupper vid Lunds universitet,Family Medicine and Community Medicine,Lund University Research Groups (author)

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

Article/chapterEnglish2020

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2020-09-03
JMIR Publications Inc.2020
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LIBRIS-ID:oai:DiVA.org:kth-285742
https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-285742URI
https://doi.org/10.2196/18930DOI
https://lup.lub.lu.se/record/5b055c28-fabb-4f42-a1d6-8b6b57aadee7URI
http://kipublications.ki.se/Default.aspx?queryparsed=id:144921175URI

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Language:English
Summary in:English

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SwePubSwePub

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Subject category:ref swepub-contenttype
Subject category:art swepub-publicationtype

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QC 20201113
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 naive 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 kappa (adjusted for chance agreement) and percentage agreement (not adjusted for chance agreement). Results: Interrater reliability as measured by Cohen kappa 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 kappa was 0.2. Intrarater reliability when 1 physician retriaged 50 reports resulted in Cohen kappa 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.

Subject headings and genre

MEDICIN OCH HÄLSOVETENSKAP Hälsovetenskap Hälso- och sjukvårdsorganisation, hälsopolitik och hälsoekonomi hsv//swe
MEDICAL AND HEALTH SCIENCES Health Sciences Health Care Service and Management, Health Policy and Services and Health Economy hsv//eng
machine learning
artificial intelligence
decision support
primary care
triage

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Bonamy, Anna-Karin EdstedtKarolinska Institutet,Doctrin AB (author)
Benjaminsson, SimonSmartera AB, Stockholm, Sweden. (author)
Herman, Pawel,1979-KTH Royal Institute of Technology,KTH,Beräkningsvetenskap och beräkningsteknik (CST)(Swepub:kth)u19pqm1e (author)
Midlöv, PatrikLund University,Lunds universitet,Institutionen för kliniska vetenskaper, Malmö,Medicinska fakulteten,Allmänmedicin och samhällsmedicin,Forskargrupper vid Lunds universitet,Department of Clinical Sciences, Malmö,Faculty of Medicine,Family Medicine and Community Medicine,Lund University Research Groups(Swepub:lu)klin-pmi (author)
Allmänmedicin och samhällsmedicinForskargrupper vid Lunds universitet (creator_code:org_t)

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