ReDWINE: A Clinical Datamart with Text Analytical Capabilities to Facilitate Rehabilitation Research

Details

• Personal Author:
  Becich MJ ; Bove A ; Cappella N ; Delitto A ; Freburger J ; McLay B ; Oniani D ; Parmanto B ; Saptono A ; Silverstein JC ; Skidmore E ; Visweswaran S ; Wang Y
• Description:
  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. [Description provided by NIOSH]
• Subjects:
  Database Occupational Health Safety Software
• Keywords:
  Author Keywords: Clinical Research Informatics Bioinformatics Common Data Model Computer Applications Databases Electronic Health Records Informatics Infrastructure Information Systems Intervention Medical Records Medical Research Natural Language Processing Rehabilitation

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• ISSN:
  1386-5056
• Document Type:
  Text
• Funding:
  Cooperative Agreement
• Genre:
  Journal Article
• Place as Subject:
  OSHA Region 3 ; Pennsylvania
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  177
• NIOSHTIC Number:
  nn:20068058
• Citation:
  Int J Med Inform 2023 Sep; 177:105144
• Contact Point Address:
  Yanshan Wang, Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA, USA
• Email:
  yanshan.wang@pitt.edu
• Federal Fiscal Year:
  2023
• NORA Priority Area:
  Construction ; Manufacturing
• Performing Organization:
  University of Pittsburgh at Pittsburgh
• Peer Reviewed:
  True
• Start Date:
  20060901
• Source Full Name:
  International Journal of Medical Informatics
• End Date:
  20260831
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:f214db28a5e295d1724ab626ad7fbfd9fca6fe79864b403bb52e5b82879fd17b4b0eff0e74e9f2d0afdb32a9cf1215d12400f31ec1c6d30c2f9ea44c22a33f48
• Download URL:
  https://stacks.cdc.gov/view/cdc/230277/cdc_230277_DS1.pdf
• File Type:
  [PDF - 1.13 MB ]