Physical therapists’ perspectives on a large language model-powered knowledge translation tool for guideline adherence: A qualitative focus group study

Clinical practice guidelines support healthcare professionals in making evidence-based decisions, yet guideline adherence among physical therapists remains inconsistent. To address this gap, a prototype digital knowledge translation tool powered by a large language model (LLM) was developed, with content based on two exemplary high-quality German national guidelines.
To (1) explore the experiences of German physical therapists using the tool, (2) assess their perspectives on its utilization in clinical practice, and (3) compare perceptions between outpatient and inpatient settings.
Six focus group interviews were conducted: three in a university hospital inpatient setting and three in outpatient physical therapy practices. Discussions were analyzed using qualitative content analysis with inductive and deductive coding.
Twenty physical therapists (11 inpatient, 9 outpatient) participated. Overall experiences were positive, though prolonged response times were criticized. Utilization was thought to depend on time availability and workplace digitization. The tool’s potential assisting with clinical questions was highlighted. No considerable differences in experiences across settings were noted. Inpatient therapists envisioned using the tool between sessions for personal knowledge enhancement, whereas outpatient therapists anticipated utilization during sessions for patient education.
LLM-based knowledge translation tools may contribute to improving guideline adherence among physical therapists. Successful implementation requires assessment of digital infrastructure, relevance to clinical needs, and users’ digital literacy. Further research should evaluate the quality of LLM-generated summaries to ensure validity and trustworthiness, and optimize the tools’ usability regarding speed and content. Development should also prompt ethical considerations about their role in clinical decision-making and patient care.

临床实践指南可辅助医疗从业者制定循证决策，但物理治疗师对指南的依从性仍存在不一致性。为填补这一研究空白，本研究开发了一款由大语言模型（Large Language Model，LLM）驱动的原型数字化知识转化工具，其内容基于两部优质的德国国家级示范指南。

本研究旨在达成三个目标：（1）探究德国物理治疗师使用该工具的实际体验；（2）评估其对该工具在临床实践中应用的看法；（3）对比门诊与住院场景下使用者的认知差异。

本研究共开展6次焦点小组访谈，其中3场在大学附属医院的住院环境中进行，另外3场则于门诊物理治疗诊所开展。访谈内容采用结合归纳式与演绎式编码的质性内容分析法进行解析。

共有20名物理治疗师参与本次研究，其中11名来自住院科室，9名来自门诊科室。整体而言，参与者对该工具的体验呈积极态势，但也有使用者对过长的响应时长提出了批评。参与者普遍认为，工具的使用取决于可用时间与工作场所的数字化水平。该工具在解答临床疑问方面的应用潜力得到了突出强调。不同诊疗场景下的使用者体验未出现显著差异。住院物理治疗师计划在两次诊疗间隙使用该工具以提升个人专业知识，而门诊物理治疗师则期望在诊疗过程中使用该工具开展患者教育。

基于大语言模型的知识转化工具或可助力提升物理治疗师对临床指南的依从性。该工具的成功落地，需评估其数字化基础设施适配性、临床需求相关性以及使用者的数字素养。未来研究应评估大语言模型生成的总结文本质量，以确保其有效性与可信度，并从响应速度与内容维度优化工具的易用性。此外，工具开发还应引发关于其在临床决策与患者照护中角色的伦理思考。