Alain Gabriel, Crick James, Snead Ella, Quatman-Yates Catherine C, Quatman Carmen E
School of Health and Rehabilitation Sciences, College of Medicine, The Ohio State University, Columbus, OH, 43210, United States, 1 614-292-1706.
The Center for the Advancement of Team Science, Analytics, and Systems Thinking in Health Services and Implementation Science Research (CATALYST), College of Medicine, The Ohio State University, Columbus, OH, United States.
J Med Internet Res. 2025 May 30;27:e73918. doi: 10.2196/73918.
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.
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.
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 ONET database. The dataset did not allow differentiation between health care professionals and the general public as users. We focused on interactions mapped to ONET 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).
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%).
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.
像Anthropic公司的Claude和OpenAI公司的ChatGPT这样的生成式人工智能（GenAI）系统正在迅速被包括医疗保健在内的各个领域所采用，为临床支持、行政效率和患者信息获取带来潜在益处。然而，实际应用模式以及GenAI用于医疗保健相关任务的程度仍鲜为人知，且与受控环境中的性能基准不同。了解这些自然使用模式是评估GenAI对医疗保健服务提供和医患动态影响的关键。
本研究旨在量化使用Anthropic公司的Claude GenAI执行的医疗保健相关任务的实际频率和范围。我们试图（1）测量与医疗保健任务相关的Claude交互与其他领域的比例；（2）识别具有高相关交互量的特定医疗保健职业（根据美国劳工部职业信息网络（O*NET）分类）；（3）使用“数字采用率”评估角色内任务采用的广度；（4）考虑数据集中关于用户身份（即专业人员与公众）的固有模糊性来解释这些发现。
我们使用Anthropic公司经济指数研究的公开可用数据集，对2024年12月至2025年1月与Claude的400多万条匿名用户对话（包括免费和专业订阅者）进行了横断面分析。交互由Anthropic公司专有的Clio模型预先分类为映射到美国劳工部ONET数据库的标准化职业任务。该数据集不允许区分医疗保健专业人员和作为用户的普通公众。我们专注于映射到ONET医疗保健从业者和技术职业的交互。主要结果包括每个医疗保健职业的交互比例、整体医疗保健交互与其他类别相比的比例以及数字采用率（即通过GenAI执行的不同任务除以每个职业的总可能任务）。
医疗保健相关任务占分析的GenAI对话总数的2.58%，显著低于计算等领域（37.22%）。在医疗保健领域内，交互频率因角色而异。强调患者教育和指导的职业比例最高，包括营养师和营养学家（占医疗保健对话的6.61%）、执业护士（5.63%）、音乐治疗师（4.54%）和临床护理专家（4.53%）。顶级医疗保健角色的数字采用率（任务广度）差异很大（13.33% - 65%），平均为16.92%，低于全球平均水平（21.13%）。与医疗记录和健康信息技术人员相关的任务采用率最高（65.0%）。
GenAI工具正被用于医疗保健相关任务的可测量子集中，使用集中在特定的、通常面向患者的角色。用户匿名性的关键限制使得无法就使用主要反映患者信息寻求行为（可能由获取需求驱动）还是专业工作流程协助得出明确结论。这种模糊性在解释当前GenAI的采用情况时需要谨慎。我们的发现强调迫切需要制定策略来应对对临床工作流程、患者决策、信息质量和健康公平性的潜在影响。未来的研究必须旨在区分用户类型，而利益相关者应为安全的患者使用和负责任的专业整合制定有针对性的指导。
