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Oniani D, Parmanto B, Saptono A, Bove A, Freburger J, Visweswaran S, Cappella N, McLay B, Silverstein JC, Becich MJ, Delitto A, Skidmore E, Wang Y. ReDWINE: A clinical datamart with text analytical capabilities to facilitate rehabilitation research. Int J Med Inform 2023; 177:105144. [PMID: 37459703 PMCID: PMC10528160 DOI: 10.1016/j.ijmedinf.2023.105144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 06/14/2023] [Accepted: 07/06/2023] [Indexed: 08/12/2023]
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.
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Affiliation(s)
- David Oniani
- Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA, USA
| | - Bambang Parmanto
- Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA, USA
| | - Andi Saptono
- Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA, USA
| | - Allyn Bove
- Department of Physical Therapy, University of Pittsburgh, Pittsburgh, PA, USA
| | - Janet Freburger
- Department of Physical Therapy, University of Pittsburgh, Pittsburgh, PA, USA
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA; Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, PA, USA
| | - Nickie Cappella
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, PA, USA
| | - Brian McLay
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jonathan C Silverstein
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, PA, USA
| | - Michael J Becich
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, PA, USA
| | - Anthony Delitto
- Department of Physical Therapy, University of Pittsburgh, Pittsburgh, PA, USA
| | - Elizabeth Skidmore
- Department of Occupational Therapy, University of Pittsburgh, Pittsburgh, PA, USA
| | - Yanshan Wang
- Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA, USA; Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA; Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, PA, USA.
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Visweswaran S, McLay B, Cappella N, Morris M, Milnes JT, Reis SE, Silverstein JC, Becich MJ. An atomic approach to the design and implementation of a research data warehouse. J Am Med Inform Assoc 2021; 29:601-608. [PMID: 34613409 PMCID: PMC8922189 DOI: 10.1093/jamia/ocab204] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 07/27/2021] [Accepted: 09/10/2021] [Indexed: 11/14/2022] Open
Abstract
Objective As a long-standing Clinical and Translational Science Awards (CTSA) Program hub, the University of Pittsburgh and the University of Pittsburgh Medical Center (UPMC) developed and implemented a modern research data warehouse (RDW) to efficiently provision electronic patient data for clinical and translational research. Materials and Methods We designed and implemented an RDW named Neptune to serve the specific needs of our CTSA. Neptune uses an atomic design where data are stored at a high level of granularity as represented in source systems. Neptune contains robust patient identity management tailored for research; integrates patient data from multiple sources, including electronic health records (EHRs), health plans, and research studies; and includes knowledge for mapping to standard terminologies. Results Neptune contains data for more than 5 million patients longitudinally organized as Health Insurance Portability and Accountability Act (HIPAA) Limited Data with dates and includes structured EHR data, clinical documents, health insurance claims, and research data. Neptune is used as a source for patient data for hundreds of institutional review board-approved research projects by local investigators and for national projects. Discussion The design of Neptune was heavily influenced by the large size of UPMC, the varied data sources, and the rich partnership between the University and the healthcare system. It includes several unique aspects, including the physical warehouse straddling the University and UPMC networks and management under an HIPAA Business Associates Agreement. Conclusion We describe the design and implementation of an RDW at a large academic healthcare system that uses a distinctive atomic design where data are stored at a high level of granularity.
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Affiliation(s)
- Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Brian McLay
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Nickie Cappella
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - John T Milnes
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Steven E Reis
- Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Jonathan C Silverstein
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Chief Research Information Officer, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Michael J Becich
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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