Hospital readmissions are an important quality measure in healthcare, as they can indicate issues in treatment, rehabilitation, or discharge management. Furthermore, readmissions are often associated with increased costs resulting from penalties and regulations enforced by policy makers and insurers. Several studies have been conducted in order to identify patients at high risk of readmission, especially focusing on the initial diseases addressed in the Hospital Readmissions Reduction Program (HRRP), acute myocardial infarction (AMI), heart failure (HF), chronic obstructive pulmonary disease (COPD), and pneumonia (PN). Since elective primary total hip arthroplasty and total knee arthroplasty (THA/TKA) procedures are a added later to the HRRP, research on risk prediction in that area is still quite scarce. This study focuses on total hip arthroplasty and total knee arthroplasty procedures. Based on a dataset from a not-for-profit Australian healthcare group, 10,872 admissions from 2011 to 2015 are utilized to build several predictive models for readmissions after THA/TKA procedures. The structure and application of these models are presented and benchmarked against current hospital risk scores, resulting in a good prediction power to identify patients at 28-day risk of readmission.
Hovorka, Dirk and Boell, Sebastian, "Contribution in Information Systems: Insights from the Disciplinary Matrix" (2017). ACIS 2017 Proceedings. 86.