Skip to main content
×
Home
    • Aa
    • Aa

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

  • R. H. Perlis (a1) (a2), D. V. Iosifescu (a1) (a3), V. M. Castro (a4), S. N. Murphy (a5), V. S. Gainer (a4), J. Minnier (a6), T. Cai (a6), S. Goryachev (a4), Q. Zeng (a7), P. J. Gallagher (a2), M. Fava (a1), J. B. Weilburg (a1), S. E. Churchill (a8), I. S. Kohane (a9) and J. W. Smoller (a2)...
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.

Copyright
Corresponding author
*Address for correspondence: Dr R. H. Perlis, Simches Research Building, 185 Cambridge St, 6th Floor, Boston, MA 02114, USA (Email: rperlis@partners.org)
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Psychological Medicine
  • ISSN: 0033-2917
  • EISSN: 1469-8978
  • URL: /core/journals/psychological-medicine
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×

Keywords:

Type Description Title
WORD
Supplementary Materials

Perlis Supplementary Material
Perlis Supplementary Material

 Word (1.5 MB)
1.5 MB

Metrics

Altmetric attention score

Full text views

Total number of HTML views: 13
Total number of PDF views: 108 *
Loading metrics...

Abstract views

Total abstract views: 554 *
Loading metrics...

* Views captured on Cambridge Core between September 2016 - 19th October 2017. This data will be updated every 24 hours.