Shifting Information Needs in Clinical Practice: The Evolving Role of Generative AI in Addressing Clinician Demands for Context-Specific Knowledge.

Abstract

This study explores clinicians' evolving information needs and evaluates the potential of Generative Artificial Intelligence (Gen AI) to address these gaps by reassessing and extending the Currie et al. (2003) taxonomy. Despite advancements in electronic health records (EHRs), unresolved information needs persist, impacting clinical efficiency and patient care. A cross-sectional survey conducted at Columbia University Irving Medical Center (CUIMC) analyzed clinician-generated Gen AI prompts, comparing them against the 2003 taxonomy. Findings reveal that while 80% of prompts align with existing categories, 20% represent emerging needs, including AI-driven workflow optimization and fairness-related inquiries. These findings highlight the necessity of adapting clinical decision support frameworks to integrate AI-driven solutions, ensuring that modern tools meet evolving clinician needs. By formally extending the Currie et al. taxonomy, this study provides a foundational framework for leveraging Gen AI to bridge long-standing information gaps and enhance patient outcomes in an increasingly complex healthcare environment.

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