# Understanding Clinician Perceptions of GenAI: A Mixed Methods Analysis of Clinical Documentation Tasks

**Authors:** David Fraile Navarro, A. Baki Kocaballi, Shlomo Berkovsky

PMC · DOI: 10.1007/s10916-025-02234-8 · Journal of Medical Systems · 2025-08-02

## TL;DR

This study explores how clinicians perceive the use of Generative AI in electronic health records, finding that they prefer moderate automation to balance efficiency and control.

## Contribution

The novel contribution is a mixed-methods analysis of clinician perceptions of GenAI in clinical documentation tasks with varying automation levels.

## Key findings

- Clinicians prefer medium automation levels for GenAI in EHR systems, viewing it as 'safe with caution'.
- Five key themes emerged: efficiency benefits, system reliability concerns, safety considerations, automation bias, and deployment requirements.
- EHR satisfaction did not significantly predict automation acceptance, but task relevance and importance were strongly correlated.

## Abstract

This mixed-methods study evaluated clinicians’ user experience (UX) with Generative AI (GenAI) in Electronic Health Record (EHR) systems across three clinical documentation tasks (Information Extraction, Summarization, and Speech-to-Text) at varying levels of user supervision (low, medium, high), focusing on workflow improvements, safety, and acceptable automation levels. Using conceptual prototyping in a usability study framework, we evaluated how incorporating GenAI into EHR could support the three documentation tasks at varying automation levels. A total of 38 clinicians interacted with the prototype and completed a questionnaire on task relevance, perceived importance, desired automation level, and EHR satisfaction. Both quantitative (descriptive statistics, Kruskal-Wallis tests, Spearman correlations) and qualitative (thematic) analyses were conducted with equal priority to explore preferences, perceived safety, and practical requirements. Clinicians showed positive reception to GenAI integration, particularly for streamlining documentation. While task relevance and importance were strongly correlated, EHR satisfaction did not significantly predict automation acceptance. Medium automation emerged as the preferred level, considered “safe with caution”. Five key themes emerged from qualitative analysis: efficiency and quality benefits; system reliability concerns; safety and medico-legal considerations; automation bias and loss of nuance; and deployment requirements including adjustable settings and oversight. While clinicians welcome GenAI-driven documentation, they prefer moderate automation to balance efficiency with clinical control. Successful integration requires addressing safety concerns, conducting real-world trials, and mitigating potential biases and medico-legal challenges. These findings suggest a cautious but optimistic path forward for AI integration in EHR systems, emphasizing the importance of maintaining clinician oversight while leveraging automation benefits.

The online version contains supplementary material available at 10.1007/s10916-025-02234-8.

## Full-text entities

- **Diseases:** fatigue (MESH:D005221), diabetes (MESH:D003920), GenAI (MESH:D004829), pain (MESH:D010146), burnout (MESH:D002055), stroke (MESH:D020521)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Full text

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## Figures

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## References

20 references — full list in the complete paper: https://tomesphere.com/paper/PMC12317912/full.md

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Source: https://tomesphere.com/paper/PMC12317912