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. 2023 Sep:177:105144.
doi: 10.1016/j.ijmedinf.2023.105144. Epub 2023 Jul 11.

ReDWINE: A clinical datamart with text analytical capabilities to facilitate rehabilitation research

David Oniani  1 , Bambang Parmanto  1 , Andi Saptono  1 , Allyn Bove  2 , Janet Freburger  2 , Shyam Visweswaran  3 , Nickie Cappella  4 , Brian McLay  4 , Jonathan C Silverstein  4 , Michael J Becich  4 , Anthony Delitto  2 , Elizabeth Skidmore  5 , Yanshan Wang  6
Affiliations

ReDWINE: A clinical datamart with text analytical capabilities to facilitate rehabilitation research

David Oniani et al. Int J Med Inform. 2023 Sep.

Abstract

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 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.

Keywords: Clinical research informatics; Common data model; Electronic health records; Informatics infrastructure; Natural language processing; Rehabilitation.

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Conflict of interest statement

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Figure 1.
Figure 1.
ReDWINE Architecture and Data Pipeline.
Figure 2.
Figure 2.
Cohort definition inside Atlas.
See this image and copyright information in PMC

References

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