Using decision trees to manage hospital readmission risk for acute myocardial infarction, heart failure, and pneumonia
| Autor: | Scott Zasadil, John P. Hilbert, Donna J. Keyser, Pamela B. Peele |
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| Rok vydání: | 2014 |
| Předmět: |
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Economics and Econometrics medicine.medical_specialty Databases Factual Psychological intervention Decision tree Myocardial Infarction Comorbidity Patient Readmission Risk Assessment California Health administration Age Distribution Health care medicine Humans Economics Hospital Sex Distribution Intensive care medicine Aged Quality of Health Care Heart Failure Inpatients Risk Management Health economics business.industry Health Policy Financial risk Patient Protection and Affordable Care Act Decision Trees General Medicine Decision rule Pneumonia Predictive analytics Length of Stay Middle Aged medicine.disease Patient Discharge United States ROC Curve Area Under Curve Female Medical emergency business |
| Zdroj: | Applied health economics and health policy. 12(6) |
| ISSN: | 1179-1896 |
| Popis: | To improve healthcare quality and reduce costs, the Affordable Care Act places hospitals at financial risk for excessive readmissions associated with acute myocardial infarction (AMI), heart failure (HF), and pneumonia (PN). Although predictive analytics is increasingly looked to as a means for measuring, comparing, and managing this risk, many modeling tools require data inputs that are not readily available and/or additional resources to yield actionable information. This article demonstrates how hospitals and clinicians can use their own structured discharge data to create decision trees that produce highly transparent, clinically relevant decision rules for better managing readmission risk associated with AMI, HF, and PN. For illustrative purposes, basic decision trees are trained and tested using publically available data from the California State Inpatient Databases and an open-source statistical package. As expected, these simple models perform less well than other more sophisticated tools, with areas under the receiver operating characteristic (ROC) curve (or AUC) of 0.612, 0.583, and 0.650, respectively, but achieve a lift of at least 1.5 or greater for higher-risk patients with any of the three conditions. More importantly, they are shown to offer substantial advantages in terms of transparency and interpretability, comprehensiveness, and adaptability. By enabling hospitals and clinicians to identify important factors associated with readmissions, target subgroups of patients at both high and low risk, and design and implement interventions that are appropriate to the risk levels observed, decision trees serve as an ideal application for addressing the challenge of reducing hospital readmissions. |
| Databáze: | OpenAIRE |
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