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Using electronic medical records to enable large-scale studies in psychiatry: treatment resistant depression as a model

Published online by Cambridge University Press:  20 June 2011

R. H. Perlis ,
D. V. Iosifescu ,
V. M. Castro ,
S. N. Murphy ,
V. S. Gainer ,
J. Minnier ,
T. Cai ,
S. Goryachev ,
Q. Zeng  and
P. J. Gallagher
...Show all authors
R. H. Perlis*
Affiliation:
Depression Clinic and Research Program, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA Psychiatric and Neurodevelopmental Genetics Unit, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
D. V. Iosifescu
Affiliation:
Depression Clinic and Research Program, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA Mood and Anxiety Disorders Program, Department of Psychiatry, Mount Sinai Hospital, New York, NY, USA
V. M. Castro
Affiliation:
Partners Research Computing, Partners HealthCare System, Boston, MA, USA
S. N. Murphy
Affiliation:
Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
V. S. Gainer
Affiliation:
Partners Research Computing, Partners HealthCare System, Boston, MA, USA
J. Minnier
Affiliation:
Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA
T. Cai
Affiliation:
Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA
S. Goryachev
Affiliation:
Partners Research Computing, Partners HealthCare System, Boston, MA, USA
Q. Zeng
Affiliation:
Department of Radiology, Brigham & Women's Hospital, Boston, MA, USA
P. J. Gallagher
Affiliation:
Psychiatric and Neurodevelopmental Genetics Unit, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
M. Fava
Affiliation:
Depression Clinic and Research Program, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
J. B. Weilburg
Affiliation:
Depression Clinic and Research Program, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
S. E. Churchill
Affiliation:
Information Systems, Partners HealthCare System, Boston, MA, USA
I. S. Kohane
Affiliation:
Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
J. W. Smoller
Affiliation:
Psychiatric and Neurodevelopmental Genetics Unit, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
*
*Address for correspondence: Dr R. H. Perlis, Simches Research Building, 185 Cambridge St, 6th Floor, Boston, MA 02114, USA (Email: rperlis@partners.org)
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Abstract

Background

Electronic medical records (EMR) provide a unique opportunity for efficient, large-scale clinical investigation in psychiatry. However, such studies will require development of tools to define treatment outcome.

Method

Natural language processing (NLP) was applied to classify notes from 127 504 patients with a billing diagnosis of major depressive disorder, drawn from out-patient psychiatry practices affiliated with multiple, large New England hospitals. Classifications were compared with results using billing data (ICD-9 codes) alone and to a clinical gold standard based on chart review by a panel of senior clinicians. These cross-sectional classifications were then used to define longitudinal treatment outcomes, which were compared with a clinician-rated gold standard.

Results

Models incorporating NLP were superior to those relying on billing data alone for classifying current mood state (area under receiver operating characteristic curve of 0.85–0.88 v. 0.54–0.55). When these cross-sectional visits were integrated to define longitudinal outcomes and incorporate treatment data, 15% of the cohort remitted with a single antidepressant treatment, while 13% were identified as failing to remit despite at least two antidepressant trials. Non-remitting patients were more likely to be non-Caucasian (p<0.001).

Conclusions

The application of bioinformatics tools such as NLP should enable accurate and efficient determination of longitudinal outcomes, enabling existing EMR data to be applied to clinical research, including biomarker investigations. Continued development will be required to better address moderators of outcome such as adherence and co-morbidity.

Keywords

Antidepressantclassificationmachine learningnatural language processingremissiontreatment resistant depression
Type
Original Articles
Information
Psychological Medicine , Volume 42 , Issue 1 , January 2012 , pp. 41 - 50
Copyright
Copyright © Cambridge University Press 2011

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