Reasons For Physicians Not Adopting Clinical Decision Support Systems: Critical Analysis
By Saif S. Khairat, David T. Marc, William Crosby, Ali Al Sanousi. JMIR Medical Informatics, 2018
304 citations recorded by the source corpus.
Background Clinical decision support systems (CDSSs) are an integral component of today’s health information technologies. They assist with interpretation, diagnosis, and treatment. A CDSS can be embedded throughout the patient safety continuum providing reminders, recommendations, and alerts to health care providers. Although CDSSs have been shown to reduce medical errors and improve patient outcomes, they have fallen short of their full potential. User acceptance has been identified as one of the potential reasons for this shortfall. Objective The purpose of this paper was to conduct a critical review and task analysis of CDSS research and to develop a new framework for CDSS design in order to achieve user acceptance. Methods A critical review of CDSS papers was conducted with a focus on user acceptance. To gain a greater understanding of the problems associated with CDSS acceptance, we conducted a task analysis to identify and describe the goals, user input, system output, knowledge requirements, and constraints from two different perspectives: the machine (ie, the CDSS engine) and the user (ie, the physician). Results Favorability of CDSSs was based on user acceptance of clinical guidelines, reminders, alerts, and diagnostic suggestions. We propose two models: (1) the user acceptance and system adaptation design model, which includes optimizing CDSS design based on user needs/expectations, and (2) the input-process-output-engagemodel, which reveals to users the processes that govern CDSS outputs. Conclusions This research demonstrates that the incorporation of the proposed models will improve user acceptance to support the beneficial effects of CDSSs adoption. Ultimately, if a user does not accept technology, this not only poses a threat to the use of the technology but can also pose a threat to the health and well-being of patients.
Subfields: Health Informatics, Health Information Management, Surgery
View Paper: https://www.semanticscholar.org/paper/a7d79a8d642fe454770d0048b052dbd29a512a3e
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In Health Informatics
Citations in the corpus
- "Hello AI": Uncovering the Onboarding Needs of Medical Practitioners for Human-AI Collaborative Decision-Making (2019, background): “Despite the high-performing nature of these algorithms, prior work has shown that CDSSes can be difficult to successfully integrate into practice, citing a lack of HCI consideration as one of the primary reasons for failure [41, 44, 55, 76, 78].”
- Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making (2019, background): “As seen in the past, medical experts are likely to resist automation if it appears brittle or limits their autonomy (24, 46).”