Sumner Jennifer, Wang Yuchen, Tan Si Ying, Chew Emily Hwee Hoon, Wenjun Yip Alexander
Alexandra Research Centre for Healthcare in a Virtual Environment, Alexandra Hospital, Singapore, Singapore.
School of Computing, National University of Singapore, Singapore, Singapore.
J Med Internet Res. 2025 May 1;27:e67383. doi: 10.2196/67383.
Large language models (LLMs) are transforming how data is used, including within the health care sector. However, frameworks including the Unified Theory of Acceptance and Use of Technology highlight the importance of understanding the factors that influence technology use for successful implementation.
This study aimed to (1) investigate users' uptake, perceptions, and experiences regarding LLMs in health care and (2) contextualize survey responses by demographics and professional profiles.
An electronic survey was administered to elicit stakeholder perspectives of LLMs (health care providers and support functions), their experiences with LLMs, and their potential impact on functional roles. Survey domains included: demographics (6 questions), user experiences of LLMs (8 questions), motivations for using LLMs (6 questions), and perceived impact on functional roles (4 questions). The survey was launched electronically, targeting health care providers or support staff, health care students, and academics in health-related fields. Respondents were adults (>18 years) aware of LLMs.
Responses were received from 1083 individuals, of which 845 were analyzable. Of the 845 respondents, 221 had yet to use an LLM. Nonusers were more likely to be health care workers (P<.001), older (P<.001), and female (P<.01). Users primarily adopted LLMs for speed, convenience, and productivity. While 75% (470/624) agreed that the user experience was positive, 46% (294/624) found the generated content unhelpful. Regression analysis showed that the experience with LLMs is more likely to be positive if the user is male (odds ratio [OR] 1.62, CI 1.06-2.48), and increasing age was associated with a reduced likelihood of reporting LLM output as useful (OR 0.98, CI 0.96-0.99). Nonusers compared to LLM users were less likely to report LLMs meeting unmet needs (45%, 99/221 vs 65%, 407/624; OR 0.48, CI 0.35-0.65), and males were more likely to report that LLMs do address unmet needs (OR 1.64, CI 1.18-2.28). Furthermore, nonusers compared to LLM users were less likely to agree that LLMs will improve functional roles (63%, 140/221 vs 75%, 469/624; OR 0.60, CI 0.43-0.85). Free-text opinions highlighted concerns regarding autonomy, outperformance, and reduced demand for care. Respondents also predicted changes to human interactions, including fewer but higher quality interactions and a change in consumer needs as LLMs become more common, which would require provider adaptation.
Despite the reported benefits of LLMs, nonusers-primarily health care workers, older individuals, and females-appeared more hesitant to adopt these tools. These findings underscore the need for targeted education and support to address adoption barriers and ensure the successful integration of LLMs in health care. Anticipated role changes, evolving human interactions, and the risk of the digital divide further emphasize the need for careful implementation and ongoing evaluation of LLMs in health care to ensure equity and sustainability.
大语言模型（LLMs）正在改变数据的使用方式，包括在医疗保健领域。然而，包括技术接受与使用统一理论在内的框架强调了理解影响技术使用的因素对于成功实施的重要性。
本研究旨在（1）调查医疗保健领域用户对大语言模型的采用情况、看法和体验，以及（2）根据人口统计学和专业概况对调查回复进行背景分析。
进行了一项电子调查，以获取利益相关者（医疗保健提供者和支持人员）对大语言模型的看法、他们使用大语言模型的经历以及这些模型对其职能角色的潜在影响。调查领域包括：人口统计学（6个问题）、大语言模型的用户体验（8个问题）、使用大语言模型的动机（6个问题）以及对职能角色的感知影响（4个问题）。该调查通过电子方式开展，目标受众为医疗保健提供者或支持人员、医学生以及健康相关领域的学者。受访者为知晓大语言模型的成年人（>18岁）。
共收到1083人的回复，其中845人的回复可用于分析。在这845名受访者中，221人尚未使用过大语言模型。未使用者更有可能是医疗保健工作者（P<.001）、年龄较大（P<.001）且为女性（P<.01）。使用者主要出于速度、便利性和提高工作效率的目的采用大语言模型。虽然75%（470/624）的人认为用户体验是积极的，但46%（294/624）的人觉得生成的内容并无帮助。回归分析表明，如果用户为男性，使用大语言模型的体验更有可能是积极的（优势比[OR]为1.62，置信区间[CI]为1.06 - 2.48），且年龄增长与认为大语言模型输出有用的可能性降低相关（OR为0.98，CI为0.96 - 0.99）。与大语言模型使用者相比，未使用者报告大语言模型满足未满足需求的可能性较低（45%，99/221 对比 65%，407/624；OR为0.48，CI为0.35 - 0.65），而男性更有可能报告大语言模型确实满足了未满足的需求（OR为1.64，CI为1.18 - 2.28）。此外，与大语言模型使用者相比，未使用者不太可能认同大语言模型将改善职能角色（63%，140/221 对比 75%，469/624；OR为0.60，CI为0.43 - 0.85）。自由文本意见突出了对自主性、表现优异以及护理需求减少的担忧。受访者还预测了人际互动的变化，包括互动次数减少但质量提高，以及随着大语言模型变得更加普遍，消费者需求的变化，这将要求提供者做出调整。
尽管大语言模型有诸多益处，但未使用者（主要是医疗保健工作者、年长者和女性）似乎对采用这些工具更为犹豫。这些发现强调了有针对性的教育和支持的必要性，以消除采用障碍并确保大语言模型在医疗保健领域的成功整合。预期的角色变化、不断演变的人际互动以及数字鸿沟的风险进一步凸显了在医疗保健领域谨慎实施和持续评估大语言模型以确保公平性和可持续性的必要性。
