# Intention to Use Automated Diagnosis and Clinical Risk Perceptions Among First Contact Clinicians in Resource-Poor Settings: Questionnaire-Based Study Focusing on Acute Burns

## TL;DR

This study explores how likely first contact clinicians in resource-poor areas are to use automated diagnosis for burns and their concerns about clinical risks.

## Contribution

The study applies the Automation Acceptance Model to burn diagnosis and highlights the importance of clinical risk perception in AI adoption.

## Key findings

- 73% of first contact clinicians would use automated diagnosis for burns if available.
- Perceived usefulness, not attitude, drives the intention to use automated diagnosis.
- First contact clinicians are more concerned than specialists about undermanagement and overmanagement of burns.

## Abstract

Burn 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.

We studied the intention of nonspecialized clinicians to engage in automated diagnosis in burn care as well as their perceptions toward clinical risks.

A 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.

Many 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=0.432), with 5 out of 8 hypotheses being supported. The intention to use automated diagnosis was associated with perceived usefulness but not with attitudes toward using it. Of the 7 risks studied, the 1 that was most often considered as high risk of occurring was that of complex burns not being recognized (n=23, 29%). The 2 groups differed significantly in their concern regarding both the likelihood of happening and the severity of 2 risks: the undermanagement of severe burns and the overmanagement of minor burns. Specifically, a larger proportion of first contact clinicians were more concerned than burns specialists (n=13, 27% versus 6% and n=11, 23% versus 6% for undermanagement and overmanagement, respectively).

Almost 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.