title: Development of an automated, real time surveillance tool for predicting readmissions at a community hospital.
abstract: BACKGROUND
  The Centers for Medicare and Medicaid Services' Readmissions Reduction Program adjusts payments to hospitals based on 30-day readmission rates for patients with acute myocardial infarction, heart failure, and pneumonia. This holds hospitals accountable for a complex phenomenon about which there is little evidence regarding effective interventions. Further study may benefit from a method for efficiently and inexpensively identifying patients at risk of readmission. Several models have been developed to assess this risk, many of which may not translate to a U.S. community hospital setting.
  
  
  OBJECTIVE
  To develop a real-time, automated tool to stratify risk of 30-day readmission at a semirural community hospital.
  
  
  METHODS
  A derivation cohort was created by extracting demographic and clinical variables from the data repository for adult discharges from calendar year 2010. Multivariate logistic regression identified variables that were significantly associated with 30-day hospital readmission. Those variables were incorporated into a formula to produce a Risk of Readmission Score (RRS). A validation cohort from 2011 assessed the predictive value of the RRS. A SQL stored procedure was created to calculate the RRS for any patient and publish its value, along with an estimate of readmission risk and other factors, to a secure intranet site.
  
  
  RESULTS
  Eleven variables were significantly associated with readmission in the multivariate analysis of each cohort. The RRS had an area under the receiver operating characteristic curve (c-statistic) of 0.74 (95% CI 0.73-0.75) in the derivation cohort and 0.70 (95% CI 0.69-0.71) in the validation cohort.
  
  
  CONCLUSIONS
  Clinical and administrative data available in a typical community hospital database can be used to create a validated, predictive scoring system that automatically assigns a probability of 30-day readmission to hospitalized patients. This does not require manual data extraction or manipulation and uses commonly available systems. Additional study is needed to refine and confirm the findings.
authors.affiliation: Augusta Health, Information Technology, Fishersville, Virginia 22939, USA.
authors.forename: R
authors.initials: R
authors.lastname: Gildersleeve
authors.forename: P
authors.initials: P
authors.lastname: Cooper
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D001331:Automation; D015331:Cohort Studies; D005260:Female; D006764:Hospitals, Community / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D008875:Middle Aged; D010359:Patient Readmission / Q000706:statistics & numerical data*; D062486:Public Health Surveillance / Q000379:methods*; D018570:Risk Assessment; D012424:Rural Population / Q000706:statistics & numerical data; D013997:Time Factors; D055815:Young Adult
keywords: Clinical decision support; alerting; data repositories; forecasting; monitoring and surveillance