Hospital readmission risk prediction based on claims data available at admission: a pilot study in Switzerland

Abstract

ObjectivesEvaluating whether future studies to develop prediction models for early readmissions based on health insurance claims data available at the time of a hospitalisation are worthwhile.DesignRetrospective cohort study of hospital admissions with discharge dates between 1 January 2014 and 31 December 2016.SettingAll-cause acute care hospital admissions in the general population of Switzerland, enrolled in the Helsana Group, a large provider of Swiss mandatory health insurance.ParticipantsThe mean age of 138 222 hospitalised adults included in the study was 60.5 years. Patients were included only with their first index hospitalisation. Patients who deceased during the follow-up period were excluded, as well as patients admitted from and/or discharged to nursing homes or rehabilitation clinics.MeasuresThe primary outcome was 30-day readmission rate. Area under the receiver operating characteristic curve (AUC) was used to measure the discrimination of the developed logistic regression prediction model. Candidate variables were theory based and derived from a systematic literature search.ResultsWe observed a 30-day readmission rate of 7.5%. Fifty-five candidate variables were identified. The final model included pharmacy-based cost group (PCG) cancer, PCG cardiac disease, PCG pain, emergency index admission, number of emergency visits, costs specialists, costs hospital outpatient, costs laboratory, costs therapeutic devices, costs physiotherapy, number of outpatient visits, sex, age group and geographical region as predictors. The prediction model achieved an AUC of 0.60 (95% CI 0.60 to 0.61).ConclusionsBased on the results of our study, it is not promising to invest resources in large-scale studies for the development of prediction tools for hospital readmissions based on health insurance claims data available at admission. The data proved appropriate to investigate the occurrence of hospitalisations and subsequent readmissions, but we did not find evidence for the potential of a clinically helpful prediction tool based on patient-sided variables alone.