Reports Summarize Information Technology Findings from Department of Health Sciences (Hospital readmission risk prediction based on claims data available at admission: a pilot study in Switzerland)

2019 JUL 12 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Investigators publish new report on Information Technology. According to news reporting originating in Zurich, Switzerland, by NewsRx journalists, research stated, “Evaluating 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. Retrospective cohort study of hospital admissions with discharge dates between 1 January 2014 and 31 December 2016.”

The news reporters obtained a quote from the research from the Department of Health Sciences, “All-cause acute care hospital admissions in the general population of Switzerland, enrolled in the Helsana Group, a large provider of Swiss mandatory health insurance. The 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. The 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. We 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). Based 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.”

According to the news reporters, the research concluded: “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.”

For more information on this research see: Hospital readmission risk prediction based on claims data available at admission: a pilot study in Switzerland. Bmj Open, 2019;9(6):e028409. (BMJ Publishing Group - http://group.bmj.com/; Bmj Open - http://bmjopen.bmj.com/)

Our news correspondents report that additional information may be obtained by contacting B. Brungger, Dept. of Health Sciences, Helsana Group, Zurich, Switzerland.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1136/bmjopen-2018-028409. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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