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

Background 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. Objective 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. Methods 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. Results 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