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

04 Feb 2017


Abstract
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.

Click here to view the full article which appeared in International Journal for Quality in Heath Care