Harnessing electronic medical records to advance research on multiple sclerosis

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.

Keywords

Electronic medical records natural language processing

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References

Garrett

Seidman

. EMR vs EHR—What is the difference? HealthIT.gov, 2011, http://www.healthit.gov/buzz-blog/electronic-health-and-medical-records/emr-vs-ehr-difference/

Jensen

Brunak

. Mining electronic health records: Towards better research applications and clinical care. Nat Rev Genet 2012; 13(6): 395–405.

Kohane

. Using electronic health records to drive discovery in disease genomics. Nat Rev Genet 2011; 12(6): 417–428.

Malhotra

Gundel

Rajput

et al . Knowledge retrieval from PubMed abstracts and electronic medical records with the Multiple Sclerosis Ontology. PLoS ONE 2015; 10(2): e0116718.

Krysko

Ivers

Young

et al . Identifying individuals with multiple sclerosis in an electronic medical record. Mult Scler 2015; 21(2): 217–224.

Davis

Sriram

Bush

et al . Automated extraction of clinical traits of multiple sclerosis in electronic medical records. J Am Med Inform Assoc 2013; 20(e2): e334–e340.

Ritchie

Denny

Crawford

et al . Robust replication of genotype-phenotype associations across multiple diseases in an electronic medical record. Am J Hum Genet 2010; 86(4): 560–572.

Xia

Secor

Chibnik

et al . Modeling disease severity in multiple sclerosis using electronic health records. PLoS ONE 2013; 8(11): e78927.

Nelson

Butler

LaFleur

et al . Determining multiple sclerosis phenotype from electronic medical records. J Manag Care Spec Pharm 2016; 22(12): 1377–1382.

10.

Cree

Gourraud

Oksenberg

et al . Long-term evolution of multiple sclerosis disability in the treatment era. Ann Neurol 2016; 80: 499–510.

11.

R Core Team. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing, 2015.

12.

Landis

Koch

. The measurement of observer agreement for categorical data. Biometrics 1977; 33(1): 159–174.

13.

Whitacre

. Sex differences in autoimmune disease. Nat Immunol 2001; 2(9): 777–780.

14.

Pittock

Mayr

McClelland

et al . Disability profile of MS did not change over 10 years in a population-based prevalence cohort. Neurology 2004; 62(4): 601–606.

15.

Zulman

Shah

Verghese

. Evolutionary pressures on the electronic health record: Caring for complexity. JAMA 2016; 316: 923–924.

16.

Gourraud

Henry

Cree

et al . Precision medicine in chronic disease management: The multiple sclerosis BioScreen. Ann Neurol 2014; 76(5): 633–642.

17.

Davis

Haines

. The intelligent use and clinical benefits of electronic medical records in multiple sclerosis. Expert Rev Clin Immunol 2015; 11(2): 205–211.

18.

Neamatullah

Douglass

Lehman

et al . Automated de-identification of free-text medical records. BMC medical informatics and decision making 2008; 8: 32.

19.

Doan

Conway

Phuong

Ohno-Machado

. Natural language processing in biomedicine: a unified system architecture overview. Methods Mol Biol 2014; 1168: 275–294.

20.

Wickham Hadley

. readr: Read Tabular Data. R package version 0.2.2 2015.

21.

Wickham Hadley

. dplyr: A Grammar of Data Manipulation. R package version 0.4.3 2015.

22.

Wickham Hadley

. tidyr: Easily Tidy Data with ‘spread()’ and ‘gather()’ Functions. R package version 0.3.1 2015.

23.

Wickham Hadley

. ggplot2: Elegant Graphics for Data Analysis. New York: Springer-Verlag, 2009.

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