The prevention of unplanned 30-day readmissions of patients discharged with a diagnosis of heart failure (HF) remains a profound challenge among hospital enterprises. Despite the many models and indices developed to predict which HF patients will readmit for any unplanned cause within 30 days, predictive success has been meager. Using simulations of HF readmission models and the diagnostics most often used to evaluate them (C-statistics, ROC curves), we demonstrate common factors that have contributed to the lack of predictive success among studies. We reveal a greater need for precision and alternative metrics such as partial C-statistics and precision-recall curves and demonstrate via simulations how those tools can be used to better gauge predictive success. We suggest how studies can improve their applicability to hospitals and call for a greater understanding of the uncertainty underlying 30-day all-cause HF readmission. Finally, using insights from sampling theory, we suggest a novel uncertainty-based perspective for predicting readmissions and non-readmissions.

Background: US hospital safety is routinely measured via patient safety indicators (PSIs). Receiving a score for most PSIs requires a minimum number of qualifying cases, which are partly determined by whether the associated diagnosis-related group (DRG) was surgical and whether the surgery was elective. While these criteria can exempt hospitals from PSIs, it remains to be seen whether exemption is driven by low volume, small numbers of DRGs, or perhaps, policies that determine how procedures are classified as elective. Methods: Using Medicare inpatient claims data from 4,069 hospitals between 2015 and 2017, we examined how percentages of elective procedures relate to numbers of surgical claims and surgical DRGs. We used a combination of quantile regression and machine learning based anomaly detection to characterize these relationships and identify outliers. We then used a set of machine learning algorithms to test whether outliers were explained by the DRGs they reported. Results: Average percentages of elective procedures generally decreased from 100% to 60% in relation to the number of surgical claims and the number of DRGs among them. Some providers with high volumes of claims had anomalously low percentages of elective procedures (5% to 40%). These low elective outliers were not explained by the particular surgical DRGs among their claims. However, among hospitals exempted from PSIs, those with the greatest volume of claims were always low elective outliers. Conclusion: Some hospitals with relatively high numbers of surgical claims may have classified procedures as non-elective in a way that ultimately exempted them from certain PSIs.