What are the primary causes of (AI) clinical decision support systems adoptions failure?
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The primary causes of failure in the adoption of AI-based clinical decision support systems (CDSS) include small sample sizes in research, focusing mainly on algorithm development rather than real-world interaction . Additionally, there is a lack of clinician acceptance due to issues such as trustworthiness, biases, and the complexity of adopting new technology in healthcare settings . Moreover, the struggle to gain adoption is also attributed to the challenge of integrating ML systems into clinical workflows and the need for clinicians to perceive themselves as partners with the technology rather than relying solely on it as a surrogate for their judgment . Furthermore, concerns regarding the perceived role of AI, the division of clinical labor, and the uniqueness of medicine as a domain also contribute to the barriers in CDSS adoption .
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| Papers (5) | Insight |
|---|---|
31 Jan 2023 | Clinician acceptance barriers for AI-based treatment recommendations include ignoring, trusting, or negotiating the AI suggestions, revealing challenges in adoption of AI tools in healthcare. |
| Clinicians' lack of partnership with AI systems, viewing them as surrogates rather than collaborators, and challenges integrating AI into time-constrained clinical workflows contribute to adoption failures. | |
| Physician attitudes towards AI clinical decision support systems adoption failure primarily stem from concerns about the perceived role of AI, the role of physicians, and general AI-related worries. | |
1 Citations | Primary causes of AI clinical decision support systems adoption failure include expert-centric healthcare, low adoption rates, inefficient processes, and error-prone outcomes due to lack of systematic implementation strategies. |
7 Citations | Primary causes of AI clinical decision support systems adoption failure include small sample sizes, focus on algorithm development over real-world interaction, and methodological shortcomings in evaluation processes. |
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