Key result
Predictive modeling identifies patient age as the strongest predictor of 30-day hospital readmission.
Why the study?
Predicting 30-day hospital readmissions can help hospitals better allocate resources, avoid CMS penalties, and reduce readmission rates.
Can predictive modeling using clustering, GLM, and LASSO regressions accurately predict 30-day hospital readmissions?
Observational (n=71,724)
No
Can predictive modeling using clustering, GLM, and LASSO regressions accurately predict 30-day hospital readmissions?
Patient age is the primary driver of 30-day hospital readmissions in this predictive modeling study of over 71,000 admissions.
Answers are grounded in this paper and cite their sources.
Age may inform readmission risk tools; leaves open prospective validation of model performance.
The 30-day hospital readmission rate is the percentage of patients who are readmitted within 30 days after the last hospital discharge. Hospitals with high readmission rates would have to pay penalties to the Centers for Medicare & Medicaid Services (CMS). Predicting the readmissions can help the hospital better allocate its resources to reduce the readmission rate. In this research, we use a data set from a hospital in North Carolina during the years from 2011 to 2016, including 71724 hospital admissions. We aim to provide a predictive model that can be helpful for related entities including hospitals, health insurance actuaries, and Medicare to reduce the cost and improve the clinical outcome of the healthcare system. We used R to process data and applied clustering, generalized linear model (GLM) and LASSO regressions to predict the 30-day readmissions. It turns out that the patient's age is the most important factor impacting hospital readmission. This research can help hospitals and CMS reduce costly readmissions.
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Xiong et al. (2021) conducted an observational in Hospital readmissions (n=71,724). Predictive modeling (clustering, GLM, LASSO) was evaluated on 30-day hospital readmission. Predictive modeling using clustering, GLM, and LASSO regressions on 71,724 hospital admissions identified patient age as the most important factor impacting 30-day hospital readmission.
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