JMIR Publications

ABSTRACT

Background:

Generative artificial intelligence (GenAI) systems like Anthropic's Claude and OpenAI’s ChatGPT are rapidly being adopted in various sectors, including healthcare, 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 healthcare-related tasks remain poorly understood, distinct from performance benchmarks in controlled settings. Understanding these organic usage patterns is key for assessing GenAI's impact on healthcare delivery and patient-provider dynamics.

Objective:

To quantify the real-world frequency and scope of healthcare-related tasks performed using Anthropic's Claude GenAI. We sought to: (1) measure the proportion of Claude interactions related to healthcare tasks versus other domains; (2) identify specific healthcare occupations (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 (professionals vs. public) in the data.

Methods:

We performed a cross-sectional analysis of over four million anonymized user conversations with Claude (free and pro subscribers) from December 2024 to January 2025, using a publicly available dataset from Anthropic’s “Economic Index” research. Interactions were pre-classified by Anthropic's proprietary Clio model into standardized occupational tasks mapped to the U.S. Department of Labor's O*NET database. The dataset did not allow differentiation between healthcare 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 healthcare occupation, the overall healthcare interaction share versus other categories, and the digital adoption rate (distinct tasks performed via GenAI / total possible tasks per occupation).

Results:

Healthcare-related tasks accounted for 2.58% of total GenAI analyzed conversations, significantly lower than domains like Computing (37.22%). Within healthcare, interaction frequency varied notably by role. Occupations emphasizing patient education and guidance exhibited the highest shares, including Dietitians and Nutritionists (6.61% of healthcare conversations), Nurse Practitioners (5.63%), Music Therapists (4.54%), and Clinical Nurse Specialists (4.53%). Digital adoption rates (task breadth) ranged widely across top healthcare roles (13.33%-65%), averaging 16.92%, below the global average (21.13%). Medical Records and Health Information Technician tasks had the highest adoption rate (65%).

Conclusions:

GenAI tools are being adopted for a measurable subset of healthcare-related tasks, with usage concentrated in specific, often patient-facing roles. The critical limitation of user anonymity prevents definitively concluding whether this reveals primarily patient information-seeking (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.