title: Linking electronic health record-extracted psychosocial data in real-time to risk of readmission for heart failure.
abstract: BACKGROUND
  Knowledge of psychosocial characteristics that helps to identify patients at increased risk for readmission for heart failure (HF) may facilitate timely and targeted care.
  
  
  OBJECTIVE
  We hypothesized that certain psychosocial characteristics extracted from the electronic health record (EHR) would be associated with an increased risk for hospital readmission within the next 30 days.
  
  
  METHODS
  We identified 15 psychosocial predictors of readmission. Eleven of these were extracted from the EHR (six from structured data sources and five from unstructured clinical notes). We then analyzed their association with the likelihood of hospital readmission within the next 30 days among 729 patients admitted for HF. Finally, we developed a multivariable predictive model to recognize individuals at high risk for readmission.
  
  
  RESULTS
  We found five characteristics-dementia, depression, adherence, declining/refusal of services, and missed clinical appointments-that were associated with an increased risk for hospital readmission: the first four features were captured from unstructured clinical notes, while the last item was captured from a structured data source.
  
  
  CONCLUSIONS
  Unstructured clinical notes contain important knowledge on the relationship between psychosocial risk factors and an increased risk of readmission for HF that would otherwise have been missed if only structured data were considered. Gathering this EHR-based knowledge can be automated, thus enabling timely and targeted care.
authors.affiliation: Center for Connected Health, Partners Healthcare, Boston, MA, USA.
authors.forename: Alice J
authors.initials: AJ
authors.lastname: Watson
authors.forename: Julia
authors.initials: J
authors.lastname: O'Rourke
authors.forename: Kamal
authors.initials: K
authors.lastname: Jethwani
authors.forename: Aurel
authors.initials: A
authors.lastname: Cami
authors.forename: Theodore A
authors.initials: TA
authors.lastname: Stern
authors.forename: Joseph C
authors.initials: JC
authors.lastname: Kvedar
authors.forename: Henry C
authors.initials: HC
authors.lastname: Chueh
authors.forename: Adrian H
authors.initials: AH
authors.lastname: Zai
mesh_terms: D000368:Aged; D003704:Dementia / Q000150:complications; D003863:Depression / Q000150:complications; D057286:Electronic Health Records; D005260:Female; D006333:Heart Failure / Q000209:etiology* / Q000523:psychology / Q000628:therapy; D006801:Humans; D016015:Logistic Models; D008297:Male; D008498:Medical Record Linkage; D010349:Patient Compliance / Q000706:statistics & numerical data; D010359:Patient Readmission / Q000706:statistics & numerical data; D011584:Psychology; D012307:Risk Factors; D013997:Time Factors; D016312:Treatment Refusal / Q000706:statistics & numerical data