Assessing AI tool use in among New York State clinicians
Galfano, A.; Barbosu, C. M.; Aladin, B.; Rivera, I.; Dye, T. D. V.
Show abstract
Artificial intelligence (AI) is dramatically changing the healthcare landscape by providing patients, clinicians, administrators, and public health professionals with tools aiming to improve efficiency, outcomes, and experience in health. As elsewhere, New York State (NYS) experiences high demand for - and high investment in - transformation in healthcare with AI tools, though little is known about clinicians use and interest in adopting AI tools in their work. A large share of the nations future primary care clinicians train and work in NYS, and the states ability to establish clear policies, provide tools, and elevate AI competency have implications for care delivery nationally. As a result, we undertook this analysis of NYS clinicians use of AI to better understand opportunities for its adoption and inclusion in continuing education. For this analysis, we included healthcare providers who deliver ambulatory or specialty medical care within NYS, with use/frequency/purpose of AI tools by clinicians in their work as the main outcome. Of 305 NYS clinical providers responding, 23.4% indicated they use AI tools for work, and 11.1% report monthly use, 8.5% weekly use, and 4.6% daily use. AI was primarily used to search guidelines and ask clinical questions, followed by identifying drug interactions, analyzing data, analyzing images/labs, and creating care plans and patient recommendations. AI use did not vary significantly across professional disciplines or practice types, though independent practitioners were significantly more likely than advanced practice providers to use AI in their work, as were providers using social media and digital methods for obtaining continuing education. AI use increased substantially in 2025 compared with 2024. Overall, our findings suggest that programs targeting clinicians could incorporate these findings in designing accessible and acceptable AI-related continuing education opportunities to help familiarize clinicians with opportunities and risks for integrating AI tools into their practices. Author SummaryAI tools are rapidly gaining traction in the delivery of healthcare. We found that clinician use of AI was quite limited (23%), though growing. Those using AI tools used them sparingly in their work, with only about 5% reporting daily use. The purposes for which clinicians report using AI - asking clinical questions, interpreting patient results, creating patient educational materials - could contribute substantially to healthcare outcomes if widely adopted. Designers of continuing education for clinicians should help provide opportunities for clinicians to improve their familiarity, use, and competency with AI tools, to help maximize the potential health benefits possible for patients and communities.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Health worker acceptability of an HIV testing mobile health application within a rural Zambian HIV treatment programme 94%
- Sources of Successful Participant Engagement in a Public Health Research Study: a Focus on a Latino Community 93%
- Essential Indicators of Quality in Primary Care Settings: An Evidence-Based, Structured, Expert Approach 93%
Similar papers in this journal
- The Lifecycle of Electronic Health Record Data in HIV-Related Big Data Studies: A Qualitative Study of Instances of and Potential Opportunities to Minimize Bias 95%
- Using a Multilingual AI Care Agent to Reduce Disparities in Colorectal Cancer Screening: Higher FIT Test Adoption Among Spanish-Speaking Patients 93%
- Understanding how the design and implementation of Online Consultations influence primary care outcomes: Systematic review of evidence with recommendations for designers, providers, and researchers 93%
Similar papers in this journal
- Patterns of SARS-CoV-2 testing preferences in a national cohort in the United States 92%
- Initial Experience in Predicting the Risk of Hospitalization of 496 Outpatients with COVID-19 Using a Telemedicine Risk Assessment Tool 91%
- Testing, Testing: What SARS-CoV-2 testing services do adults in the United States actually want? 91%
Similar papers in this journal
- Measures of socioeconomic advantage are not independent predictors of support for healthcare AI: subgroup analysis of a national Australian survey 95%
- Connecting Artificial Intelligence and Primary Care Challenges: Findings from a Multi-Stakeholder Collaborative Consultation 95%
- Impact of the Federated Data Platform's digital surgery scheduling system on elective theatre utilisation at an NHS Trust: an interrupted time series analysis 92%
Similar papers in this journal
- Benefits and Challenges of Using Virtual Primary Care During the COVID-19 Pandemic: From Key Lessons to a Framework for Implementation 94%
- Development and preliminary testing of Health Equity Across the AI Lifecycle (HEAAL): A framework for healthcare delivery organizations to mitigate the risk of AI solutions worsening health inequities 93%
- Theory of radiologist interaction with instant messaging decision support tools: a sequential-explanatory study 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.