Intention to Use Automated Diagnosis and Clinical Risk Perceptions Among First Contact Clinicians in Resource-Poor Settings: Questionnaire-Based Study Focusing on Acute Burns
Abstract BackgroundBurn automated diagnosis may be instrumental for accurate and timely decision-making at point-of-care, helping to ensure that the right patients are triaged to burns centers. This is particularly important in resource-poor settings. ObjectiveWe s...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
JMIR Publications
2025-06-01
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| Series: | JMIR Human Factors |
| Online Access: | https://humanfactors.jmir.org/2025/1/e56300 |
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| Summary: | Abstract
BackgroundBurn automated diagnosis may be instrumental for accurate and timely decision-making at point-of-care, helping to ensure that the right patients are triaged to burns centers. This is particularly important in resource-poor settings.
ObjectiveWe studied the intention of nonspecialized clinicians to engage in automated diagnosis in burn care as well as their perceptions toward clinical risks.
MethodsA self-administered survey was used among a purposive sample of first contact clinicians (n=56) and burns specialists (n=35). The survey had 2 main parts: 1 measuring the intention to use automated diagnosis as per 7 constructs of the Automation Acceptance Model (yielding 8 hypotheses) and 1 on clinical risk perceptions (likelihood and severity of 7 risks). Structural Equation Modelling was used to test the hypotheses among first contact clinicians, and the Mann-Whitney U test was used to measure differences in risk perceptions between the two clinical groups.
ResultsMany first contact clinicians would intend to use automated diagnosis for burns should the technology be made available in their departments (41/56, 73%). The Automation Acceptance Model concepts contributed moderately to explain what the intention to use automated diagnosis rests on (R2
ConclusionsAlmost three-quarters of first contact clinicians were inclined to seek automated advice for burn diagnosis. The proposed model contributes to explaining the intention to use with 5 hypotheses supported. When seeking additional determinants, clinical risk perception is a dimension that should be considered in any artificial intelligence implementation process, to help ensure sustainability. |
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| ISSN: | 2292-9495 |