Unplanned readmissions within 30 days after discharge: improving quality through easy prediction.

Published on Apr 1, 2017in International Journal for Quality in Health Care1.957
· DOI :10.1093/INTQHC/MZX011
Francesca Casalini10
Estimated H-index: 10
(UniPi: University of Pisa),
Susanna Salvetti1
Estimated H-index: 1
(UniPi: University of Pisa)
+ 4 AuthorsGaetano Pierpaolo Privitera21
Estimated H-index: 21
(UniPi: University of Pisa)
Objective: To propose an easy predictive model for the risk of rehospitalization, built from hospital administrative data, in order to prevent repeated admissions and to improve transitional care. Design: Retrospective cohort study. Setting: Azienda Ospedaliero Universitaria Pisana (Pisa University Hospital). Participants: Patients residing in the territory of the province of Pisa (Tuscany Region) with at least one unplanned hospital admission leading to a medical Diagnosis-Related Group (DRG) in the calendar year 2012. Intervention: We compared two groups of patients: patients coded as 'RA30' (readmitted within 30 days after the previous discharge) and patients coded as 'NRA30' (either admitted only once or readmitted after 30 days since the latest discharge). Main Outcome Measures: The effect of age, sex, length of stay, number of diagnoses, normalized number of admissions and presence of diseases on the probability of rehospitalization within 30 days after discharge was evaluated. Results: The significant variables included in the predictive model were: age, odds ratio (OR) = 1.018, 95% confidence interval (CI) = 1.011-1.026; normalized number of admissions, OR = 1.257, CI = 1.225-1.290; number of diagnoses, OR = 1.306, CI = 1.174-1.452 and presence of cancer diagnosis, OR = 1.479, CI = 1.088-2.011. Conclusions: The model can be easily applied when discharging patients who have been hospitalized after an access to the Emergency Department to predict the risk of rehospitalization within 30 days. The prediction can be used to activate focused hospital-primary care transitional interventions. The model has to be validated first in order to be implemented in clinical practice.
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