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

Sachleen K Tuteja, Elise L Boventer, Abdulaziz Alkattan, Noemie Elhadad, Sarah C Rossetti

Abstract read

In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Sachleen K TutejaWeinberg College of Arts and Science, Northwestern University, Evanston, IL, USA.

Elise L BoventerDepartment of Biomedical Informatics, Columbia University, New York, NY, USA.

Abdulaziz AlkattanDepartment of Biomedical Informatics, Columbia University, New York, NY, USA.

Noemie ElhadadDepartment of Biomedical Informatics, Columbia University, New York, NY, USA.

Sarah C RossettiDepartment of Biomedical Informatics, Columbia University, New York, NY, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Artificial IntelligenceDecision Support Systems, ClinicalElectronic Health RecordsGenerative Artificial IntelligenceCross-Sectional StudiesHumans

Identifiers

PMID41726434

PMCPMC12919614

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.