Disease-dependent variations in the timing and causes of readmissions in Germany: A claims data analysis for six different conditions

Abstract

Background Hospital readmissions place a major burden on patients and health care systems worldwide, but little is known about patterns and timing of readmissions in Germany. Methods We used German health insurance claims (AOK, 2011–2016) of patients ≥ 65 years hospitalized 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, or osteoporosis to identify hospital readmissions within 30 or 90 days. Readmissions were classified into all-cause, specific, and non-specific and their characteristics were analyzed. Results Within 30 and 90 days, about 14–22% and 27–41% index admissions were readmitted for any reason, respectively. HF and S/AF contributed most index cases, and HF and COPD accounted for most all-cause readmissions. Distributions and ratios of specific to non-specific readmissions were disease-specific with highest specific readmissions rates among COPD and AMI. Conclusion German claims are well-suited to investigate readmission causes if longer periods than 30 days are evaluated. Conditions closely related with the primary disease are the most frequent readmission causes, but multiple comorbidities among readmitted cases suggest that a multidisciplinary care approach should be implemented vigorously addressing comorbidities already during the index hospitalization.

Predicting Hospital Readmissions from Health Insurance Claims Data: A Modeling Study Targeting Potentially Inappropriate Prescribing

“…In addition to inherent limitations attributed to claims data (some of which could be overcome, e.g., by linking to other data sources), the limited comparability with existing studies stems from sophisticated case definitions made in our preliminary work. 4 For example, partially overlapping definitions of index diseases or readmissions resulted in significantly correlated coefficients in the stacked all-cause model, a phenomenon also observed between some PIP criteria in the separate models. This makes intuitive interpretation of some estimates difficult and again suggests to use machine-learning techniques (e.g., random forests) for further analyses.…”

“…previous work. 4 All-cause readmission within 90 days was a secondary outcome in an exploratory analysis that integrated all specific prediction models.…”

“…To potentially avert readmissions, a preliminary but decisive step is to identify patients at high readmission risk already during their index hospitalization. [4][5][6] Then, the index hospitalization provides an opportunity to develop targeted interventions to minimize the risk of readmission. 7 Among modifiable risk factors, such as insufficient patient knowledge, for example, on self-management of chronic diseases or poor social support, 8,9 criteria for potentially inappropriate prescribing (PIP) represent a set of well-known drugrelated risks, 2,10-12 defining overuse, misuse, and underuse of medications in older people.…”

“…17 In our recent framework for readmissions in routine data, we classified specific readmission as situations directly related to the index hospitalization, that is, a typical complication, exacerbation, or sequel of the index case or a consequence of giving (e.g., adverse event) or withholding medicines (e.g., causing lack of effect) for its treatment. 4 We derived such a classification 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). 4 With this framework, readmissions can be principally predicted from preprocessed routine data of statutory health insurances in Germany.…”

“…4 We derived such a classification 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). 4 With this framework, readmissions can be principally predicted from preprocessed routine data of statutory health insurances in Germany.…”