Morrow Kristin, Datta Debajyoti, Spiegelman Lindsey, Almog Roy, Zheng Kai, Brown Don, Cooper Dan Michael
School of Engineering, University of Virginia, VA, USA.
Department of Emergency Medicine, University of California, Irvine, CA, USA.
J Clin Transl Sci. 2025 May 21;9(1):e133. doi: 10.1017/cts.2025.10051. eCollection 2025.
A critical challenge for biomedical investigators is the delay between research and its adoption, yet there are few tools that use bibliometrics and artificial intelligence to address this translational gap. We built a tool to quantify translation of clinical investigation using novel approaches to identify themes in published clinical trials from PubMed and their appearance in the natural language elements of the electronic health record (EHR).
As a use case, we selected the translation of known health effects of exercise for heart disease, as found in published clinical trials, with the appearance of these themes in the EHR of heart disease patients seen in an emergency department (ED). We present a self-supervised framework that quantifies semantic similarity of themes within the EHR.
We found that 12.7% of the clinical trial abstracts dataset recommended aerobic exercise or strength training. Of the ED treatment plans, 19.2% related to heart disease. Of these, the treatment plans that included heart disease identified aerobic exercise or strength training only 0.34% of the time. Treatment plans from the overall ED dataset mentioned aerobic exercise or strength training less than 5% of the time.
Having access to publicly available clinical research and associated EHR data, including clinician notes and after-visit summaries, provided a unique opportunity to assess the adoption of clinical research in medical practice. This approach can be used for a variety of clinical conditions, and if assessed over time could measure implementation effectiveness of quality improvement strategies and clinical guidelines.
生物医学研究人员面临的一项关键挑战是研究成果与实际应用之间的延迟，然而，很少有工具利用文献计量学和人工智能来弥合这一转化差距。我们构建了一个工具，使用新颖的方法来量化临床研究的转化情况，该方法可识别来自PubMed的已发表临床试验中的主题及其在电子健康记录（EHR）自然语言元素中的出现情况。
作为一个应用案例，我们选择了已发表临床试验中发现的运动对心脏病已知健康影响的转化情况，并观察这些主题在急诊科（ED）心脏病患者的EHR中的出现情况。我们提出了一个自监督框架，用于量化EHR中主题的语义相似性。
我们发现，临床试验摘要数据集中12.7%推荐有氧运动或力量训练。在ED治疗计划中，19.2%与心脏病有关。其中，包含心脏病的治疗计划仅0.34%的时间提到有氧运动或力量训练。整个ED数据集的治疗计划中提到有氧运动或力量训练的时间不到5%。
能够获取公开可用的临床研究和相关的EHR数据，包括临床医生记录和随访后总结，为评估临床研究在医疗实践中的应用提供了独特的机会。这种方法可用于多种临床情况，如果长期进行评估，则可以衡量质量改进策略和临床指南的实施效果。
