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Intention to Use Automated Diagnosis and Clinical Risk Perceptions Among First Contact Clinicians in Resource-Poor

Constance Boissin1,2, Lisa Blom2, Zara Taha2

  • 1Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

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Summary

Most first contact clinicians are willing to use automated burn diagnosis, but risk perception is key for AI implementation. This technology can improve triage, especially in under-resourced areas.

Keywords:
artificial intelligenceburnsclinical decision support systemsclinical risk perceptiondiagnosis, computer-assistedfirst contact cliniciansimage-based diagnosticsintention to usetelemedicinetriage

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Clinical Decision Support Systems

Background:

  • Automated diagnosis of burns can improve patient triage and care, particularly in resource-limited settings.
  • Timely and accurate diagnosis is crucial for directing patients to specialized burn centers.

Purpose of the Study:

  • To assess the intention of non-specialist clinicians to use automated burn diagnosis tools.
  • To evaluate clinicians' perceptions of clinical risks associated with automated diagnosis in burn care.

Main Methods:

  • A survey was administered to 56 first contact clinicians and 35 burn specialists.
  • The Automation Acceptance Model was used to measure intention to use, with 8 hypotheses tested.
  • Clinical risk perceptions (likelihood and severity) were assessed, and differences between groups analyzed using Mann-Whitney U test.

Main Results:

  • 73% of first contact clinicians intended to use automated diagnosis if available.
  • Perceived usefulness, but not attitude, was associated with intention to use (R2=0.432, 5/8 hypotheses supported).
  • The highest perceived risk was failure to recognize complex burns (29%). First contact clinicians showed greater concern than specialists regarding under- and over-management of burns.

Conclusions:

  • A significant majority of first contact clinicians are open to using automated burn diagnosis.
  • The study highlights the importance of perceived usefulness and clinical risk perception in the adoption of AI for burn care.
  • Addressing clinicians' risk concerns is vital for the sustainable implementation of artificial intelligence in burn management.