Rehabilitation research focuses on determining the components of a treatment intervention, the mechanism of how these components lead to recovery and rehabilitation, and ultimately the optimal intervention strategies to maximize patients' physical, psychologic, and social functioning. Traditional randomized clinical trials that study and establish new interventions face several challenges, such as high cost and time commitment. Observational studies that use existing clinical data to observe the effect of an intervention have shown several advantages over RCTs. Electronic Health Records (EHRs) have become an increasingly important resource for conducting observational studies. To support these studies, we developed a clinical research datamart, called ReDWINE (Rehabilitation Datamart With Informatics iNfrastructure for rEsearch), that transforms the rehabilitation-related EHR data collected from the UPMC health care system to the Observational Health Data Sciences and Informatics (OHDSI) Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to facilitate rehabilitation research. The standardized EHR data stored in ReDWINE will further reduce the time and effort required by investigators to pool, harmonize, clean, and analyze data from multiple sources, leading to more robust and comprehensive research findings. ReDWINE also includes deployment of data visualization and data analytics tools to facilitate cohort definition and clinical data analysis. These include among others the Open Health Natural Language Processing (OHNLP) toolkit, a high-throughput NLP pipeline, to provide text analytical capabilities at scale in ReDWINE. Using this comprehensive representation of patient data in ReDWINE for rehabilitation research will facilitate real-world evidence for health interventions and outcomes.

翻译：康复研究致力于明确治疗干预的组成要素、这些要素如何促进功能恢复与康复的机制，以及最终优化干预策略以最大化患者身体、心理和社会功能的方案。传统的随机对照试验（RCTs）在研究和确立新干预措施时面临成本高、周期长等挑战。利用现有临床数据观察干预效果的观察性研究相比RCTs展现出多重优势。电子健康记录（EHRs）已成为开展观察性研究的重要资源。为支持这类研究，我们构建了名为ReDWINE（基于信息学基础设施的康复研究数据仓库）的临床研究数据仓库，将匹兹堡大学医学中心（UPMC）医疗系统中与康复相关的EHR数据转换为观察性健康数据科学与信息学联盟（OHDSI）的观察性医疗结果合作研究（OMOP）通用数据模型（CDM），以促进康复研究。存储在ReDWINE中的标准化EHR数据将进一步减少研究人员跨来源整合、协调、清洗和分析数据所需的时间与精力，从而获得更可靠全面的研究结果。ReDWINE还部署了数据可视化与数据分析工具，支持队列定义及临床数据分析。其中，开放健康自然语言处理（OHNLP）工具包作为高通量NLP管线，为ReDWINE提供大规模文本分析能力。利用ReDWINE中患者数据的综合表示进行康复研究，将促进健康干预措施及结果的真实世界证据生成。