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The Potential of Training to Increase Acceptance and Use of Computerized Decision Support Systems for Medical Diagnosis

2006·46 Zitationen·Human Factors The Journal of the Human Factors and Ergonomics Society
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46

Zitationen

4

Autoren

2006

Jahr

Abstract

OBJECTIVE: The goals of this study were to understand the reasons underlying the limited use of medical decision-support tools and to explore the potential of a computer-based tutorial to mitigate barriers to use. BACKGROUND: Medical decision-support tools such the Acute Cardiac Ischemia Time-Insensitive Predictive Instrument (ACI-TIPI) have demonstrated statistical validity and clinical impact for patient safety but have seen limited adoption and use. METHODS: The study developed a brief Web-based "demystifying" ACI-TIPI tutorial employing case-based training and evaluated the effectiveness of that tutorial in changing self-reported attitudes and behaviors. RESULTS: Clinicians using the tutorial reported greater understanding of how to use the ACI-TIPI score appropriately and increased confidence in the score. Case studies in the tutorial that provided examples of how to use the score for actual cases were rated as especially helpful. CONCLUSION: This study suggests that a primary barrier to the use of statistical decision support tools for patient diagnosis is lack of training or experience in combining a population-based numerical risk score with other diagnostic information about the individual patient's case that is not considered in that score. The results of this study indicate that there is a potential for a relatively brief tutorial to increase acceptance and use of decision support tools for medical diagnosis. APPLICATION: These findings have the potential for the identification of methods to help clinicians learn how to use statistical and probabilistic information to better assess risk and to promote integration of decision support tools into medical decision making for improvement of patient safety.

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Clinical Reasoning and Diagnostic SkillsArtificial Intelligence in Healthcare and EducationExplainable Artificial Intelligence (XAI)
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