Department of Ophthalmology, University of Lausanne, Fondation Asile des Aveugles, Jules Gonin Eye Hospital, Lausanne, Switzerland.
Platform for Research in Ocular Imaging, Fondation Asile des Aveugles, Jules Gonin Eye Hospital, Lausanne, Switzerland.
Stud Health Technol Inform. 2024 Aug 22;316:1151-1155. doi: 10.3233/SHTI240613.
In clinical research, the analysis of patient cohorts is a widely employed method for investigating relevant healthcare questions. The ability to automatically extract large-scale patient cohorts from hospital systems is vital in order to unlock the potential of real-world clinical data, and answer pivotal medical questions through retrospective research studies. However, existing medical data is often dispersed across various systems and databases, preventing a systematic approach to access and interoperability. Even when the data are readily accessible, clinical researchers need to sift through Electronic Medical Records, confirm ethical approval, verify status of patient consent, check the availability of imaging data, and filter the data based on disease-specific image biomarkers. We present Cohort Builder, a software pipeline designed to facilitate the creation of patient cohorts with predefined baseline characteristics from real-world ophthalmic imaging data and electronic medical records. The applicability of our approach extends beyond ophthalmology to other medical domains with similar requirements such as neurology, cardiology and orthopedics.
在临床研究中,对患者队列进行分析是一种广泛采用的方法,可用于研究相关的医疗保健问题。为了挖掘真实世界临床数据的潜力,并通过回顾性研究回答关键的医学问题,能够从医院系统中自动提取大规模的患者队列至关重要。然而,现有的医疗数据通常分散在各种系统和数据库中,这使得难以进行系统的访问和互操作。即使数据可以轻松访问,临床研究人员也需要筛选电子病历,确认伦理批准,验证患者同意的状态,检查成像数据的可用性,并根据特定疾病的图像生物标志物对数据进行过滤。我们提出了 Cohort Builder,这是一个软件管道,旨在方便地从真实世界的眼科成像数据和电子病历中创建具有预定义基线特征的患者队列。我们的方法不仅适用于眼科,还适用于其他具有类似需求的医学领域,如神经病学、心脏病学和骨科。