University of Heidelberg Reports Findings in Health and Medical Informatics (Predicting Hospital Readmissions from Health Insurance Claims Data: A Modeling Study Targeting Potentially Inappropriate Prescribing): Information Technology – Health and Medical Informatics

2022 FEB 22 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- New research on Information Technology - Health and Medical Informatics is the subject of a report. According to news reporting originating from Heidelberg, Germany, by NewsRx correspondents, research stated, “ Numerous prediction models for readmissions are developed from hospital data whose predictor variables are based on specific data fields that are often not transferable to other settings. In contrast, routine data from statutory health insurances (in Germany) are highly standardized, ubiquitously available, and would thus allow for automatic identification of readmission risks.”

Financial support for this research came from German Innovation Funds according to 92a (2) Volume V of the Social Insurance Code.

Our news editors obtained a quote from the research from the University of Heidelberg, “To develop and internally validate prediction models for readmissions based on potentially inappropriate prescribing (PIP) in six diseases from routine data. In a large database of German statutory health insurance claims, we detected disease-specific readmissions after index admissions for acute myocardial infarction (AMI), heart failure (HF), a composite of stroke, transient ischemic attack or atrial fibrillation (S/AF), chronic obstructive pulmonary disease (COPD), type-2 diabetes mellitus (DM), and osteoporosis (OS). PIP at the index admission was determined by the STOPP/START criteria (Screening Tool of Older Persons’ Prescriptions/Screening Tool to Alert doctors to the Right Treatment) which were candidate variables in regularized prediction models for specific readmission within 90 days. The risks from disease-specific models were combined (‘stacked’) to predict all-cause readmission within 90 days. Validation performance was measured by the c-statistics. While the prevalence of START criteria was higher than for STOPP criteria, more single STOPP criteria were selected into models for specific readmissions. Performance in validation samples was the highest for DM (c-statistics: 0.68 [95% confidence interval (CI): 0.66-0.70]), followed by COPD (c-statistics: 0.65 [95% CI: 0.64-0.67]), S/AF (c-statistics: 0.65 [95% CI: 0.63-0.66]), HF (c-statistics: 0.61 [95% CI: 0.60-0.62]), AMI (c-statistics: 0.58 [95% CI: 0.56-0.60]), and OS (c-statistics: 0.51 [95% CI: 0.47-0.56]). Integrating risks from disease-specific models to a combined model for all-cause readmission yielded a c-statistics of 0.63 [95% CI: 0.63-0.64]. PIP successfully predicted readmissions for most diseases, opening the possibility for interventions to improve these modifiable risk factors.”

According to the news editors, the research concluded: “Machine-learning methods appear promising for future modeling of PIP predictors in complex older patients with many underlying diseases.”

This research has been peer-reviewed.

For more information on this research see: Predicting Hospital Readmissions from Health Insurance Claims Data: A Modeling Study Targeting Potentially Inappropriate Prescribing. Methods of Information in Medicine, 2022. Methods of Information in Medicine can be contacted at: Georg Thieme Verlag Kg, Rudigerstr 14, D-70469 Stuttgart, Germany.

The news editors report that additional information may be obtained by contacting Carmen Ruff, Dept. of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg, Germany. Additional authors for this research include Alexander Gerharz, Lucas Wirbka, Felicitas Stoll, Walter E. Haefeli, Andreas Groll and Andreas D. Meid.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1055/s-0042-1742671. 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.

Publisher contact information for the journal Methods of Information in Medicine is: Georg Thieme Verlag Kg, Rudigerstr 14, D-70469 Stuttgart, Germany.

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