title: Allocating scarce resources in real-time to reduce heart failure readmissions: a prospective, controlled study.
abstract: OBJECTIVE
  To test a multidisciplinary approach to reduce heart failure (HF) readmissions that tailors the intensity of care transition intervention to the risk of the patient using a suite of electronic medical record (EMR)-enabled programmes.
  
  
  METHODS
  A prospective controlled before and after study of adult inpatients admitted with HF and two concurrent control conditions (acute myocardial infarction (AMI) and pneumonia (PNA)) was performed between 1 December 2008 and 1 December 2010 at a large urban public teaching hospital. An EMR-based software platform stratified all patients admitted with HF on a daily basis by their 30-day readmission risk using a published electronic predictive model. Patients at highest risk received an intensive set of evidence-based interventions designed to reduce readmission using existing resources. The main outcome measure was readmission for any cause and to any hospital within 30 days of discharge.
  
  
  RESULTS
  There were 834 HF admissions in the pre-intervention period and 913 in the post-intervention period. The unadjusted readmission rate declined from 26.2% in the pre-intervention period to 21.2% in the post-intervention period (p=0.01), a decline that persisted in adjusted analyses (adjusted OR (AOR)=0.73; 95% CI 0.58 to 0.93, p=0.01). In contrast, there was no significant change in the unadjusted and adjusted readmission rates for PNA and AMI over the same period. There were 45 fewer readmissions with 913 patients enrolled and 228 patients receiving intervention, resulting in a number needed to treat (NNT) ratio of 20.
  
  
  CONCLUSIONS
  An EMR-enabled strategy that targeted scarce care transition resources to high-risk HF patients significantly reduced the risk-adjusted odds of readmission.
authors.affiliation: Parkland Center for Clinical Innovation, , Dallas, Texas, USA.
authors.forename: Ruben
authors.initials: R
authors.lastname: Amarasingham
authors.forename: Parag C
authors.initials: PC
authors.lastname: Patel
authors.forename: Kathleen
authors.initials: K
authors.lastname: Toto
authors.forename: Lauren L
authors.initials: LL
authors.lastname: Nelson
authors.forename: Timothy S
authors.initials: TS
authors.lastname: Swanson
authors.forename: Billy J
authors.initials: BJ
authors.lastname: Moore
authors.forename: Bin
authors.initials: B
authors.lastname: Xie
authors.forename: Song
authors.initials: S
authors.lastname: Zhang
authors.forename: Kristin S
authors.initials: KS
authors.lastname: Alvarez
authors.forename: Ying
authors.initials: Y
authors.lastname: Ma
authors.forename: Mark H
authors.initials: MH
authors.lastname: Drazner
authors.forename: Usha
authors.initials: U
authors.lastname: Kollipara
authors.forename: Ethan A
authors.initials: EA
authors.lastname: Halm
mesh_terms: D000368:Aged; D057286:Electronic Health Records; D005260:Female; D015276:Health Care Rationing; D006333:Heart Failure; D006786:Hospitals, Urban; D006801:Humans; D008297:Male; D008875:Middle Aged; D019982:Organizational Case Studies; D010359:Patient Readmission / Q000191:economics* / Q000706:statistics & numerical data; D011446:Prospective Studies; D012308:Risk Management / Q000379:methods; D013781:Texas
keywords: Decision support, clinical; Health services research; Healthcare quality improvement; Information technology
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