title: Using electronic medical records to enable large-scale studies in psychiatry: treatment resistant depression as a model.
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.
  
  
  METHODS
  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.
authors.affiliation: Depression Clinic and Research Program, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA. rperlis@partners.org
authors.forename: R H
authors.initials: RH
authors.lastname: Perlis
authors.forename: D V
authors.initials: DV
authors.lastname: Iosifescu
authors.forename: V M
authors.initials: VM
authors.lastname: Castro
authors.forename: S N
authors.initials: SN
authors.lastname: Murphy
authors.forename: V S
authors.initials: VS
authors.lastname: Gainer
authors.forename: J
authors.initials: J
authors.lastname: Minnier
authors.forename: T
authors.initials: T
authors.lastname: Cai
authors.forename: S
authors.initials: S
authors.lastname: Goryachev
authors.forename: Q
authors.initials: Q
authors.lastname: Zeng
authors.forename: P J
authors.initials: PJ
authors.lastname: Gallagher
authors.forename: M
authors.initials: M
authors.lastname: Fava
authors.forename: J B
authors.initials: JB
authors.lastname: Weilburg
authors.forename: S E
authors.initials: SE
authors.lastname: Churchill
authors.forename: I S
authors.initials: IS
authors.lastname: Kohane
authors.forename: J W
authors.initials: JW
authors.lastname: Smoller
mesh_terms: D000328:Adult; D000465:Algorithms; D000553:Ambulatory Care; D035843:Biomedical Research / Q000379:methods*; D003430:Cross-Sectional Studies; D061218:Depressive Disorder, Treatment-Resistant / Q000188:drug therapy* / Q000453:epidemiology; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D038801:International Classification of Diseases; D016015:Logistic Models; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D009323:Natural Language Processing; D009511:New England; D017063:Outcome Assessment, Health Care / Q000379:methods / Q000706:statistics & numerical data*; D011570:Psychiatry; D012372:ROC Curve