title: Incorporating patient-reported outcome measures into the electronic health record for research: application using the Patient Health Questionnaire (PHQ-9).
abstract: OBJECTIVE
  Electronic health records (EHRs) present an opportunity to access large stores of data for research, but mapping raw EHR data to clinical phenotypes is complex. We propose adding patient-reported data to the EHR to improve phenotyping performance and describe a retrospective cohort study demonstrating a test case in depressive disorder.
  
  
  METHODS
  We compared four EHR-phenotyping methods based on International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes, medication records, and the Patient Health Questionnaire 9 (PHQ-9) regarding the ability to identify cases with depression and characteristics of patients identified with depression. Our sample included 168,884 patients seen (2007-2013) at our neurological institute. We assessed the diagnostic performance in a subset of 225 patients who had a reference standard measurement available.
  
  
  RESULTS
  ICD-9-CM codes identified the fewest number of patients as depressed (4,658), followed by PHQ-9 (46,565), and medication data (50,505). The presence of at least one of these criteria identified the largest number (78,322). The PHQ-9 identified a higher proportion of elderly, disabled, Medicaid, and rural patients, as compared to ICD-9-CM codes. ICD-9-CM codes were least sensitive (6.7% sensitivity), whereas the method using at least one of the criteria identified the highest number of truly depressed patients (93.3% sensitivity); however, specificity dropped from 97.7 to 58.1%.
  
  
  CONCLUSIONS
  The choice of phenotyping method may disproportionately exclude patient groups from research. Patient-reported data hold potential to improve sensitivity while maintaining an acceptable loss of specificity, depending on the context. Researchers should consider including patient-reported data in EHR-driven phenotyping methods.
authors.affiliation: Department of Quantitative Health Sciences, Cleveland Clinic, 9500 Euclid Avenue, JJN3-01, Cleveland, OH, 44195, USA, griffis5@ccf.org.
authors.forename: Sandra D
authors.initials: SD
authors.lastname: Griffith
authors.forename: Nicolas R
authors.initials: NR
authors.lastname: Thompson
authors.forename: Jaivir S
authors.initials: JS
authors.lastname: Rathore
authors.forename: Lara E
authors.initials: LE
authors.lastname: Jehi
authors.forename: George E
authors.initials: GE
authors.lastname: Tesar
authors.forename: Irene L
authors.initials: IL
authors.lastname: Katzan
mesh_terms: D000368:Aged; D003866:Depressive Disorder / Q000628:therapy; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D038801:International Classification of Diseases; D008297:Male; D008875:Middle Aged; D063868:Patient Outcome Assessment; D012372:ROC Curve; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D011795:Surveys and Questionnaires; D014481:United States
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