# Patterns of AI Use in Clinical Work by Hospitalists: Survey Study

## TL;DR

A survey of hospitalists found that two-thirds use AI tools in clinical practice, mostly for answering questions and generating diagnoses, with a preference for medical-specific platforms.

## Contribution

This study provides empirical insights into hospitalists' organic adoption of AI in clinical settings without institutional endorsement.

## Key findings

- 66.7% of hospitalists reported using AI in clinical practice, with OpenEvidence being the most used platform.
- AI was most commonly used for answering clinical questions and generating differential diagnoses.
- Most AI use occurred in less than 25% of clinical encounters, and there were significant differences in frequency across use cases.

## Abstract

Artificial intelligence (AI) tools are widely and freely available for clinical use. Understanding hospitalists’ real-world adoption patterns in the absence of organizational endorsement is essential for health care institutions to develop governance frameworks and optimize AI integration.

The objective of this study was to investigate hospitalists’ use of AI, examining the AI platforms being used, frequency of use, and clinical contexts of application. We hypothesized that AI use is more common among younger, less experienced hospitalists, albeit at an overall low frequency.

An anonymous online survey was distributed via email to all 70 hospitalists (physicians, nurse practitioners, and physician assistants) providing direct patient care at a large urban academic tertiary care hospital. Demographic data, the AI platform used (if any), the purpose for AI use, and the frequency of use information were collected. The CHERRIES (Checklist for Reporting Results of Internet E-Surveys) checklist was used for creating, testing, administering, and reporting the results of the survey. Chi-square test was used where possible; when expected cell values were low, the Fisher's exact test was used instead. The Friedman test and the pairwise Wilcoxon signed-rank test were used for analyzing the differences in the frequency of AI use for various tasks. Likert-scale responses to frequency questions (never, rarely, sometimes, often, and always) were converted to ordinal values (1-5, respectively) to facilitate analysis.

Of the 70 providers, 54 (77.1%) responded to the survey. No significant differences in AI usage were observed across shift type, years of practice, time allocation to hospitalist duties, sex, age, or provider designation, contrary to our hypothesis. Overall, 36 of 54 respondents (66.7%; 95% CI 53.4%-77.8%) reported using AI in clinical practice. OpenEvidence was the most used platform (28/54, 51.9%), far exceeding general-purpose tools like OpenAI’s ChatGPT (4/54, 7.4%), suggesting a preference for medical-specific platforms. Among nonusers, primary concerns were AI accuracy and preference for established resources. The most common application was answering miscellaneous clinical questions (32/36, 88.9%), generating differential diagnoses (31/36, 86.1%), and determining management options (31/36, 86.1%), with much lower use for patient education materials (16/36, 44.4%). There was a statistically significant difference in the frequency of AI use across these clinical scenarios (Friedman test χ24 37.6; P<.001). Pairwise comparisons using the Wilcoxon signed-rank test revealed significant differences between use for answering miscellaneous questions and confirming suspected diagnosis (P=.003) and generating patient education materials (P=.004), respectively. Most respondents reported using AI for under 25% of clinical encounters across all use cases.

Two-thirds of hospitalists organically adopted AI despite the absence of institutional oversight. AI is predominantly used as a supplementary decision support tool, with a preference for a medical-specific platform. Health care institutions must develop governance frameworks, validation protocols, and educational initiatives to ensure safe and effective AI deployment in clinical practice.