Psychological Medicine

Original Articles

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

R. H. Perlisa1a2 c1, D. V. Iosifescua1a3, V. M. Castroa4, S. N. Murphya5, V. S. Gainera4, J. Minniera6, T. Caia6, S. Goryacheva4, Q. Zenga7, P. J. Gallaghera2, M. Favaa1, J. B. Weilburga1, S. E. Churchilla8, I. S. Kohanea9 and J. W. Smollera2

a1 Depression Clinic and Research Program, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA

a2 Psychiatric and Neurodevelopmental Genetics Unit, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA

a3 Mood and Anxiety Disorders Program, Department of Psychiatry, Mount Sinai Hospital, New York, NY, USA

a4 Partners Research Computing, Partners HealthCare System, Boston, MA, USA

a5 Department of Neurology, Massachusetts General Hospital, Boston, MA, USA

a6 Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA

a7 Department of Radiology, Brigham & Women's Hospital, Boston, MA, USA

a8 Information Systems, Partners HealthCare System, Boston, MA, USA

a9 Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA


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.

(Received January 18 2011)

(Revised May 10 2011)

(Accepted May 14 2011)

(Online publication June 20 2011)


c1 Address for correspondence: Dr R. H. Perlis, Simches Research Building, 185 Cambridge St, 6th Floor, Boston, MA 02114, USA (Email: