Human- Versus Machine Learning-Based Triage Using Digitalized Patient Histories in Primary Care : Comparative Study
Entezarjou, Artin LU
; Bonamy, Anna-Karin Edstedt
; Benjaminsson, Simon
; Herman, Pawel
and Midlöv, Patrik
LU
(2020)
In JMIR Medical Informatics
8(9).
- 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... (More)
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.
(Less)
- author
- Entezarjou, Artin
LU
; Bonamy, Anna-Karin Edstedt
; Benjaminsson, Simon
; Herman, Pawel
and Midlöv, Patrik
LU
- organization
- publishing date
- 2020-09-03
- type
- Contribution to journal
- publication status
- published
- subject
- in
- JMIR Medical Informatics
- volume
- 8
- issue
- 9
- article number
- e18930
- publisher
- JMIR Publications Inc.
- external identifiers
-
- scopus:85097476675
- pmid:32880578
- ISSN
- 2291-9694
- DOI
- 10.2196/18930
- language
- English
- LU publication?
- yes
- additional info
- ©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.
- id
- 5b055c28-fabb-4f42-a1d6-8b6b57aadee7
- date added to LUP
- 2020-09-06 15:02:11
- date last changed
- 2024-04-03 13:34:04
@article{5b055c28-fabb-4f42-a1d6-8b6b57aadee7,
abstract = {{<p>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).</p><p>OBJECTIVE: The purpose of this study was to explore the interrater reliability between human physicians and an automated ML-based triage method.</p><p>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).</p><p>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.</p><p>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.</p>}},
author = {{Entezarjou, Artin and Bonamy, Anna-Karin Edstedt and Benjaminsson, Simon and Herman, Pawel and Midlöv, Patrik}},
issn = {{2291-9694}},
language = {{eng}},
month = {{09}},
number = {{9}},
publisher = {{JMIR Publications Inc.}},
series = {{JMIR Medical Informatics}},
title = {{Human- Versus Machine Learning-Based Triage Using Digitalized Patient Histories in Primary Care : Comparative Study}},
url = {{http://dx.doi.org/10.2196/18930}},
doi = {{10.2196/18930}},
volume = {{8}},
year = {{2020}},
}