Journal of Global Antimicrobial Resistance · 2023 · 26 citations · 20 references
ComputingBiomedical InformaticsHealth InformaticsArtificial IntelligenceClinical Decision SupportDecision Support SystemsArtificial Intelligence AdoptionClinical Decision Support For PrescribingClinical Decision Support SystemsClinical Decision-makingDecision SupportDecision Support In HealthcareAi Decision SupportAntibiotic Decision-makingAntibiotic Prescribing Decision SupportClinical Decision Support System ImplementationClinical Decision Support ToolsCross-country ComparabilityDecision Support For PrescribingPrescription Decision Support
OBJECTIVES: Artificial intelligence (AI)-driven clinical decision support systems (CDSSs) can augment antibiotic decision-making capabilities, but physicians' hesitancy in adopting them may undermine their utility. We conducted a cross-country comparison of physician perceptions on the barriers and facilitators in accepting an AI-enabled CDSS for antibiotic prescribing. METHODS: We conducted in-depth interviews with physicians from the National Centre for Infectious Diseases (NCID), Singapore, and Christian Medical College Vellore (CMCV), India, between April and December 2022. Our semi-structured in-depth interview guides were anchored on Venkatesh's UTAUT model. We used clinical vignettes to illustrate the application of AI in clinical decision support for antibiotic prescribing and explore medico-legal concerns. RESULTS: Most NCID physicians felt that an AI-enabled CDSS could facilitate antibiotic prescribing, while most CMCV physicians were sceptical about the tool's utility. The hesitancy in adopting an AI-enabled CDSS stems from concerns about the lack of validated and successful examples, fear of losing autonomy and clinical skills, difficulty of use, and impediment in work efficiency. Physicians from both sites felt that a user-friendly interface, integration with workflow, transparency of output, a guiding medico-legal framework, and training and technical support would improve the uptake of an AI-enabled CDSS. CONCLUSION: In conclusion, the acceptance of AI-enabled CDSSs depends on the physician's confidence with the tool's recommendations, perceived ease of use, familiarity with AI, the organisation's digital culture and support, and the presence of medico-legal governance of AI. Progressive implementation and continuous feedback are essential to allay scepticism around the utility of AI-enabled CDSSs.
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User Acceptance of Information Technology: Toward A Unified View1
Venkatesh · MIS Quarterly · 2003 · 42.5K citations
Artificial intelligence in healthcare: transforming the practice of medicine
Junaid Bajwa, Usman Munir, Aditya Nori et al. · Future Healthcare Journal · 2021 · 1.6K citations · Full text