Harnessing electronic medical records to advance research on multiple sclerosis-Reference-Cited by-同舟云学术

Harnessing electronic medical records to advance research on multiple sclerosis

Published:2018-01-09 Issue:3 Volume:25 Page:408-418
ISSN:1352-4585
Container-title:Multiple Sclerosis Journal
language:en
Short-container-title:Mult Scler

Author:

Damotte Vincent¹^ORCID,Lizée Antoine²,Tremblay Matthew³,Agrawal Alisha¹,Khankhanian Pouya⁴,Santaniello Adam¹,Gomez Refujia¹,Lincoln Robin¹,Tang Wendy¹,Chen Tiffany¹,Lee Nelson⁵,Villoslada Pablo⁶,Hollenbach Jill A¹,Bevan Carolyn D¹,Graves Jennifer¹,Bove Riley¹,Goodin Douglas S¹,Green Ari J¹,Baranzini Sergio E¹,Cree Bruce AC¹,Henry Roland G¹,Hauser Stephen L¹,Gelfand Jeffrey M¹,Gourraud Pierre-Antoine²

Affiliation:

1. MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA

2. MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA/Université de Nantes, INSERM, UMR 1064, ATIP-Avenir, Equipe 5 Centre de Recherche en Transplantation et Immunologie, Nantes, France

3. MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA/Department of Neurology, John Dempsey Hospital, University of Connecticut Health Center, Farmington, CT, USA

4. MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA/Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA

5. Information Technology, University of California San Francisco (UCSF), San Francisco, CA, USA

6. MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA/IDIBAPS—Hospital Clinic of Barcelona, Barcelona, Spain

Abstract

Background: Electronic medical records (EMR) data are increasingly used in research, but no studies have yet evaluated similarity between EMR and research-quality data and between characteristics of an EMR multiple sclerosis (MS) population and known natural MS history. Objectives: To (1) identify MS patients in an EMR system and extract clinical data, (2) compare EMR-extracted data with gold-standard research data, and (3) compare EMR MS population characteristics to expected MS natural history. Methods: Algorithms were implemented to identify MS patients from the University of California San Francisco EMR, de-identify the data and extract clinical variables. EMR-extracted data were compared to research cohort data in a subset of patients. Results: We identified 4142 MS patients via search of the EMR and extracted their clinical data with good accuracy. EMR and research values showed good concordance for Expanded Disability Status Scale (EDSS), timed-25-foot walk, and subtype. We replicated several expected MS epidemiological features from MS natural history including higher EDSS for progressive versus relapsing–remitting patients and for male versus female patients and increased EDSS with age at examination and disease duration. Conclusion: Large real-world cohorts algorithmically extracted from the EMR can expand opportunities for MS clinical research.

Publisher

SAGE Publications

Subject

Clinical Neurology,Neurology

Link

http://journals.sagepub.com/doi/pdf/10.1177/1352458517747407

Reference19 articles.

1. Mining electronic health records: towards better research applications and clinical care

2. Knowledge Retrieval from PubMed Abstracts and Electronic Medical Records with the Multiple Sclerosis Ontology

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