Article Text

Allocating scarce resources in real-time to reduce heart failure readmissions: a prospective, controlled study
  1. Ruben Amarasingham1,2,
  2. Parag C Patel3,
  3. Kathleen Toto2,
  4. Lauren L Nelson2,
  5. Timothy S Swanson1,
  6. Billy J Moore1,
  7. Bin Xie1,
  8. Song Zhang4,
  9. Kristin S Alvarez5,
  10. Ying Ma1,
  11. Mark H Drazner2,
  12. Usha Kollipara6,
  13. Ethan A Halm2
  1. 1Parkland Center for Clinical Innovation, Dallas, Texas, USA
  2. 2Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA
  3. 3Department of Cardiovascular Disease and Transplantation, Mayo Clinic, Jacksonville, Florida, USA
  4. 4Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, USA
  5. 5Department of Pharmacy, Parkland Health and Hospital System, Dallas, Texas, USA
  6. 6Department of Nutrition Services, Parkland Health and Hospital System, Dallas, Texas, USA
  1. Correspondence to Dr Ruben Amarasingham, Parkland Center for Clinical Innovation, 6300 Harry Hines Blvd, Dallas, TX 75235, USA; ruben.amarasingham{at}


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.

  • Decision support, clinical
  • Health services research
  • Healthcare quality improvement
  • Information technology

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 3.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:

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