title: Predicting readmission of heart failure patients using automated follow-up calls.
abstract: BACKGROUND
  Readmission rates for patients with heart failure (HF) remain high. Many efforts to identify patients at high risk for readmission focus on patient demographics or on measures taken in the hospital. We evaluated a method for risk assessment that depends on patient self-report following discharge from the hospital.
  
  
  METHODS
  In this study, we investigated whether automated calls could be used to identify patients who are at a higher risk of readmission within 30 days. An automated multi-call follow-up program was deployed with 1095 discharged HF patients. During each call, the patient reported his or her general health status. Patients were grouped by the trend of their responses over the two calls, and their unadjusted 30-day readmission rates were compared. Pearson's chi-square test was used to evaluate whether readmission risk was independent of response trend.
  
  
  RESULTS
  Of the 1095 patients participating in the program, 837 (76%) responded to the general status question in at least one of the calls and 515 (47%) patients responded to the general status question in both calls. Out of the 89 patients exhibiting a negative response trend, 37% were readmitted. By contrast, the 97 patients showing a positive trend and the 329 patients showing a neutral trend were readmitted at rates of 16% and 14% respectively. The dependence of readmission on trend group was statistically significant (P < 0.0001).
  
  
  CONCLUSIONS
  Patients at an elevated risk of readmission can be identified based on the trend of their responses to automated follow-up calls. This presents a simple method for risk stratification based on patient self-assessment.
authors.affiliation: Keck School of Medicine of USC, Los Angeles, CA, USA.
authors.forename: Shelby
authors.initials: S
authors.lastname: Inouye
authors.affiliation: CipherHealth, New York, NY, USA.
authors.forename: Vasileios
authors.initials: V
authors.lastname: Bouras
authors.affiliation: Department of Internal Medicine, Charleston Area Medical Center, Charleston, WV, USA.
authors.forename: Eric
authors.initials: E
authors.lastname: Shouldis
authors.affiliation: Department of Internal Medicine, Charleston Area Medical Center, Charleston, WV, USA.
authors.forename: Adam
authors.initials: A
authors.lastname: Johnstone
authors.affiliation: CipherHealth, New York, NY, USA.
authors.forename: Zachary
authors.initials: Z
authors.lastname: Silverzweig
authors.affiliation: Department of Chemistry and Chemical Biology, Harvard University, 12 Oxford Street, Cambridge, MA, 02138, USA. pallavkosuri@fas.harvard.edu.
authors.forename: Pallav
authors.initials: P
authors.lastname: Kosuri
mesh_terms: D000328:Adult; D003266:Continuity of Patient Care / Q000706:statistics & numerical data*; D006304:Health Status; D006333:Heart Failure / Q000628:therapy*; D006801:Humans; D010359:Patient Readmission / Q000706:statistics & numerical data*; D011379:Prognosis; D018570:Risk Assessment; D057566:Self Report; D013689:Telephone