Evidence map›Paper›PMID 37459703›Full record

ArticleInternational journal of medical informatics2023

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

David Oniani, Bambang Parmanto, Andi Saptono, Allyn Bove, Janet Freburger, Shyam Visweswaran, Nickie Cappella, Brian McLay, Jonathan C Silverstein, Michael J Becich and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of medical informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

David OnianiDepartment of Health Information Management, University of Pittsburgh, Pittsburgh, PA, USA.
Bambang ParmantoDepartment of Health Information Management, University of Pittsburgh, Pittsburgh, PA, USA.
Andi SaptonoDepartment of Health Information Management, University of Pittsburgh, Pittsburgh, PA, USA.
Allyn BoveDepartment of Physical Therapy, University of Pittsburgh, Pittsburgh, PA, USA.
Janet FreburgerDepartment of Physical Therapy, University of Pittsburgh, Pittsburgh, PA, USA.
Shyam VisweswaranDepartment 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 CappellaDepartment of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, PA, USA.
Brian McLayDepartment 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 SilversteinDepartment 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 BecichDepartment of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, PA, USA.
Anthony DelittoDepartment of Physical Therapy, University of Pittsburgh, Pittsburgh, PA, USA.
Elizabeth SkidmoreDepartment of Occupational Therapy, University of Pittsburgh, Pittsburgh, PA, USA.
Yanshan WangDepartment 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. Electronic address: yanshan.wang@pitt.edu.

Funding

University of Pittsburgh Clinical and Translational Science InstituteUL1TR001857 · NCATS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI REIS, STEVEN E · 2016 to 2025
$129.3M
All of Us PennsylvaniaOT2OD026554 · OD · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI REIS, STEVEN E, VISWESWARAN, SHYAM · 2018 to 2023
$72.1M
ENACT: Translating Health Informatics Tools to Research and Clinical Decision MakingU24TR004111 · NCATS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI STEVEN E REIS, SHYAM VISWESWARAN · 2022 to 2026
$23.3M
ACL HHS U24OH009077NCATS NIH HHS U24 TR004111NCATS NIH HHS UL1 TR001857NIH HHS OT2 OD026554NIOSH CDC HHS U24 OH009077
6 · The paper itself

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.

Indexed as

Medical InformaticsRehabilitation ResearchElectronic Health RecordsHumansNatural Language ProcessingClinical research informaticsCommon data modelElectronic health recordsInformatics infrastructureNatural language processingRehabilitation

Identifiers

PMID37459703
PMCPMC10528160

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.