⚡ Quick Summary
This study evaluated the use of Generative AI (GenAI), specifically Anthropic’s Claude, in health care settings, revealing that only 2.58% of interactions were health care-related. The findings highlight a significant concentration of usage among patient-facing roles, emphasizing the need for further research on user identity and adoption patterns.
🔍 Key Details
- 📊 Dataset: Over 4 million anonymized user conversations with Claude from December 2024 to January 2025.
- 🧩 Focus: Health care-related tasks mapped to O*NET Healthcare Practitioners and Technical Occupations.
- ⚙️ Analysis Method: Cross-sectional analysis using Anthropic’s Clio model for task classification.
- 🏆 Key Metrics: Health care tasks accounted for 2.58% of total interactions; digital adoption rates averaged 16.92%.
🔑 Key Takeaways
- 📊 Limited Adoption: Only 2.58% of GenAI interactions were related to health care tasks.
- 💡 Role Variation: Occupations like dietitians (6.61%) and nurse practitioners (5.63%) had the highest interaction rates.
- 👩🔬 Digital Adoption Rates: Varied widely, with some roles reaching up to 65% for specific tasks.
- 🏥 User Ambiguity: The dataset did not differentiate between health care professionals and the general public.
- 🌍 Need for Caution: Findings necessitate careful interpretation regarding user identity and task relevance.
- 🔍 Future Research: Essential to differentiate user types and develop targeted guidance for GenAI integration.
- ⚠️ Implications for Workflow: Potential impacts on clinical workflows and patient decision-making must be addressed.
📚 Background
The integration of Generative AI in health care is rapidly evolving, with tools like Claude and ChatGPT offering promising benefits for clinical support and administrative efficiency. However, understanding the real-world adoption patterns of these technologies is crucial for assessing their impact on health care delivery and patient-provider dynamics.
🗒️ Study
This cross-sectional study aimed to quantify the frequency and scope of health care-related tasks performed using Claude. By analyzing over 4 million user conversations, researchers sought to measure the proportion of interactions related to health care, identify high-volume occupations, and assess the digital adoption rate across various roles.
📈 Results
The analysis revealed that health care-related tasks constituted only 2.58% of total GenAI conversations, significantly lower than other domains like computing (37.22%). Notably, roles focused on patient education, such as dietitians and nurse practitioners, exhibited the highest interaction rates, while the overall digital adoption rate averaged 16.92%, below the global average of 21.13%.
🌍 Impact and Implications
The findings underscore the concentrated use of GenAI tools in specific, often patient-facing roles, highlighting the need for strategies to address potential impacts on clinical workflows and health equity. As GenAI continues to evolve, understanding its adoption patterns will be vital for ensuring safe and effective integration into health care practices.
🔮 Conclusion
This study sheds light on the current state of Generative AI adoption in health care, revealing both its potential and limitations. As we move forward, it is essential to differentiate user types and develop targeted strategies for responsible integration, ensuring that GenAI enhances patient care and supports health care professionals effectively.
💬 Your comments
What are your thoughts on the adoption of Generative AI in health care? We invite you to share your insights and engage in a discussion! 💬 Leave your comments below or connect with us on social media:
Evaluating User Interactions and Adoption Patterns of Generative AI in Health Care Occupations Using Claude: Cross-Sectional Study.
Abstract
BACKGROUND: Generative artificial intelligence (GenAI) systems like Anthropic’s Claude and OpenAI’s ChatGPT are rapidly being adopted in various sectors, including health care, offering potential benefits for clinical support, administrative efficiency, and patient information access. However, real-world adoption patterns and the extent to which GenAI is used for health care-related tasks remain poorly understood and distinct from performance benchmarks in controlled settings. Understanding these organic usage patterns is key for assessing GenAI’s impact on health care delivery and patient-provider dynamics.
OBJECTIVE: This study aimed to quantify the real-world frequency and scope of health care-related tasks performed using Anthropic’s Claude GenAI. We sought to (1) measure the proportion of Claude interactions related to health care tasks versus other domains; (2) identify specific health care occupations (as per O*NET classifications) with high associated interaction volumes; (3) assess the breadth of task adoption within roles using a “digital adoption rate”; and (4) interpret these findings considering the inherent ambiguity regarding user identity (ie, professionals vs public) in the dataset.
METHODS: We performed a cross-sectional analysis of more than 4 million anonymized user conversations with Claude (ie, including both free and pro subscribers) from December 2024 to January 2025, using a publicly available dataset from Anthropic’s Economic Index research. Interactions were preclassified by Anthropic’s proprietary Clio model into standardized occupational tasks mapped to the US Department of Labor’s O*NET database. The dataset did not allow differentiation between health care professionals and the general public as users. We focused on interactions mapped to O*NET Healthcare Practitioners and Technical Occupations. Main outcomes included the proportion of interactions per health care occupation, proportion of overall health care interaction versus other categories, and the digital adoption rate (ie, distinct tasks performed via GenAI divided by the total possible tasks per occupation).
RESULTS: Health care-related tasks accounted for 2.58% of total analyzed GenAI conversations, significantly lower than domains such as computing (37.22%). Within health care, interaction frequency varied notably by role. Occupations emphasizing patient education and guidance exhibited the highest proportion, including dietitians and nutritionists (6.61% of health care conversations), nurse practitioners (5.63%), music therapists (4.54%), and clinical nurse specialists (4.53%). Digital adoption rates (task breadth) ranged widely across top health care roles (13.33%-65%), averaging 16.92%, below the global average (21.13%). Tasks associated with medical records and health information technicians had the highest adoption rate (65.0%).
CONCLUSIONS: GenAI tools are being adopted for a measurable subset of health care-related tasks, with usage concentrated in specific, often patient-facing roles. The critical limitation of user anonymity prevents definitive conclusions regarding whether usage primarily reflects patient information-seeking behavior (potentially driven by access needs) or professional workflow assistance. This ambiguity necessitates caution when interpreting current GenAI adoption. Our findings emphasize the urgent need for strategies addressing potential impacts on clinical workflows, patient decision-making, information quality, and health equity. Future research must aim to differentiate user types, while stakeholders should develop targeted guidance for both safe patient use and responsible professional integration.
Author: [‘Alain G’, ‘Crick J’, ‘Snead E’, ‘Quatman-Yates CC’, ‘Quatman CE’]
Journal: J Med Internet Res
Citation: Alain G, et al. Evaluating User Interactions and Adoption Patterns of Generative AI in Health Care Occupations Using Claude: Cross-Sectional Study. Evaluating User Interactions and Adoption Patterns of Generative AI in Health Care Occupations Using Claude: Cross-Sectional Study. 2025; 27:e73918. doi: 10.2196/73918