Harnessing electronic medical records to advance research on multiple sclerosis.

Academic Medical Centers Adult Biomedical Research Electronic Health Records Female Humans Information Storage and Retrieval Male Middle Aged Multiple Sclerosis / epidemiology Natural Language Processing Severity of Illness Index
Electronic medical records natural language processing

Journal

Multiple sclerosis (Houndmills, Basingstoke, England)
ISSN: 1477-0970
Titre abrégé: Mult Scler
Pays: England
ID NLM: 9509185

Informations de publication

Date de publication:
03 2019
Historique:
pubmed: 10 1 2018
medline: 14 1 2020
entrez: 10 1 2018
Statut: ppublish

Résumé

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. 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. 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. 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. Large real-world cohorts algorithmically extracted from the EMR can expand opportunities for MS clinical research.

Sections du résumé

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.

Identifiants

DOI: 10.1177/1352458517747407 PMID: 29310490
pubmed: 29310490
doi: 10.1177/1352458517747407
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

408-418

Auteurs

Vincent Damotte (V)

MS Genetics, Department of Neurology, School of Medicine, University of California San Francisco (UCSF), San Francisco, CA, USA.
ORCID: 0000-0002-7321-8575

Antoine Lizée (A)

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.

Matthew Tremblay (M)

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.

Alisha Agrawal (A)

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

Pouya Khankhanian (P)

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.

Adam Santaniello (A)

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

Refujia Gomez (R)

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

Robin Lincoln (R)

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

Wendy Tang (W)

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

Tiffany Chen (T)

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

Nelson Lee (N)

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

Pablo Villoslada (P)

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.

Jill A Hollenbach (JA)

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

Carolyn D Bevan (CD)

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

Jennifer Graves (J)

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

Riley Bove (R)

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

Douglas S Goodin (DS)

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

Ari J Green (AJ)

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

Sergio E Baranzini (SE)

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

Bruce Ac Cree (BA)

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

Roland G Henry (RG)

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

Stephen L Hauser (SL)

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

Jeffrey M Gelfand (JM)

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

Pierre-Antoine Gourraud (PA)

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.

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Classifications MeSH

questionsmedicales.fr › Établissements, main d'oeuvre et services de soins de santé › Établissements de santé › Centres hospitaliers universitaires › Harnessing electronic medical records to...
questionsmedicales.fr › Personnes › Tranches d'âge › Adulte › Harnessing electronic medical records to...
questionsmedicales.fr › Disciplines des sciences naturelles › Science › Recherche › Recherche biomédicale › Harnessing electronic medical records to...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Collecte de données › Documents › Dossiers médicaux › Systèmes informatisés de dossiers médicaux › Dossiers médicaux électroniques › Harnessing electronic medical records to...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Harnessing electronic medical records to...
questionsmedicales.fr › Sciences de l'information › Mémorisation et recherche des informations › Harnessing electronic medical records to...
questionsmedicales.fr › Personnes › Tranches d'âge › Adulte › Adulte d'âge moyen › Harnessing electronic medical records to...
questionsmedicales.fr › Maladies du système immunitaire › Maladies auto-immunes › Maladies auto-immunes du système nerveux › Maladies démyélinisantes auto-immunes du SNC › Sclérose en plaques › Harnessing electronic medical records to...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Intelligence artificielle › Traitement du langage naturel › Harnessing electronic medical records to...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Collecte de données › Enquêtes et questionnaires › Enquêtes de santé › Indicateurs d'état de santé › Acuité des besoins du patient › Indice de gravité de la maladie › Harnessing electronic medical records to...