Integrating clinical decision support systems, nursing vigilance, and physician prescribing patterns to reduce preventable adverse drug events: a structured evidence-based narrative review on human-AI interface in medication safety - Summary - MDSpire
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Integrating clinical decision support systems, nursing vigilance, and physician prescribing patterns to reduce preventable adverse drug events: a structured evidence-based narrative review on human-AI interface in medication safety
To critically examine the effectiveness of clinical decision support systems (CDSS) in reducing preventable adverse drug events (ADEs), human factors influencing clinician-AI interactions, and challenges in implementing digital health technologies.
Approach:
Review Methodology: A structured narrative review was conducted using the SANRA framework and PRISMA-ScR guidance, analyzing literature from various databases published between January 2015 and March 2024.
Key Findings:
Five themes emerged: transition from passive to adaptive decision support; AI's dual role as a safety enhancer and a source of new risks; persistent alert fatigue; nursing vigilance's contribution; and gaps in equity and governance.
The Clinical Safety Intelligence Loop (CSIL) was proposed as a framework integrating AI within a sociotechnical system.
Interpretation:
Achieving medication safety improvements requires a systems-level approach that integrates AI, clinician cognition, organizational culture, and governance.
Limitations:
The review highlights the need for further empirical validation of the proposed CSIL framework.
Existing literature may not fully capture the complexities of human-AI interactions in medication safety.
Conclusion:
A shift from technology-focused solutions to comprehensive systems-level strategies is necessary for enhancing medication safety.
