Validation of the potentially avoidable hospital readmission rate as a routine indicator of the quality of hospital care.
BACKGROUND
The hospital readmission rate has been proposed as an important outcome indicator computable from routine statistics. However, most commonly used measures raise conceptual issues.


OBJECTIVE
We sought to evaluate the usefulness of the computerized algorithm for identifying avoidable readmissions on the basis of minimum bias, criterion validity, and measurement precision.


METHODS
A total of 131,809 hospitalizations of patients discharged alive from 49 hospitals were used to compare the predictive performance of risk adjustment methods. A subset of a random sample of 570 medical records of discharge/readmission pairs in 12 hospitals were reviewed to estimate the predictive value of the screening of potentially avoidable readmissions.


METHODS
Potentially avoidable readmissions, defined as readmissions related to a condition of the previous hospitalization and not expected as part of a program of care and occurring within 30 days after the previous discharge, were identified by a computerized algorithm. Unavoidable readmissions were considered as censored events.


RESULTS
A total of 5.2% of hospitalizations were followed by a potentially avoidable readmission, 17% of them in a different hospital. The predictive value of the screen was 78%; 27% of screened readmissions were judged clearly avoidable. The correlation between the hospital rate of clearly avoidable readmission and all readmissions rate, potentially avoidable readmissions rate or the ratio of observed to expected readmissions were respectively 0.42, 0.56 and 0.66. Adjustment models using clinical information performed better.


CONCLUSIONS
Adjusted rates of potentially avoidable readmissions are scientifically sound enough to warrant their inclusion in hospital quality surveillance.
Halfon
Patricia
P
Institut Universitaire de Médecine Sociale et Préventive, University of Lausanne, Lausanne, Switzerland. Patricia.Halfon@chuv.ch
Eggli
Yves
Y
Prêtre-Rohrbach
Isaline
I
Meylan
Danielle
D
Marazzi
Alfio
A
Burnand
Bernard
B
D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D016009:Chi-Square Distribution; D002648:Child; D002675:Child, Preschool; D016001:Confidence Intervals; D005260:Female; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D019300:Medical Errors; D008875:Middle Aged; D008962:Models, Theoretical; D010351:Patient Discharge; D010359:Patient Readmission / Q000706:statistics & numerical data; D016012:Poisson Distribution; D019984:Quality Indicators, Health Care; D012372:ROC Curve; D020379:Risk Adjustment; D012307:Risk Factors; D013557:Switzerland; D013997:Time Factors