A framework for the estimation and reduction of hospital readmission penalties using predictive analytics

Abstract

Abstract Background Recent US legislation imposes financial penalties on hospitals with excessive patient readmissions. Predictive analytics for hospital readmissions have seen an increase in research due to the passage of this legislation. However, many current systems ignore the formulas used by the Centers for Medicare and Medicaid Services for imposing penalties. This research expands upon current methodologies and directly incorporates federal penalization formulas when selecting patients for which to dedicate resources. Methods Hospital discharge summaries are structured using clinical natural language processing techniques. Naïve Bayes classifiers are then used to assign a probability of readmission to each patient. Hospital Readmission Reductions Program formulas and probability of readmission are applied using four readmission scenarios to estimate the cost of readmission. The highest cost patients are identified and readmission mitigation efforts are attempted. Results The results show that the average penalty savings over currently employed binary classification to be 51.93%. Binary classification is also shown to select more patients than necessary for readmission intervention. Additionally, intervening in only high-risk patients saved an average of 90.07% compared to providing all patients with costly aftercare. Conclusion Focusing resources toward the potentially most expensive patients offers considerably better results than unfocused efforts. Utilizing direct calculation to estimate readmission costs has shown to be a more efficient use of resources than current readmission reduction methods.

Keywords

• Scientific algorithms of big data
• Big data applications
• Big data tools
• Natural language processing
• Naïve Bayes classification