Key result
Institution-specific readmission risk prediction models for heart failure, acute myocardial infarction, and pneumonia yielded better prediction accuracy than existing models in the literature.
Why the study?
Do institution-specific prediction models improve readmission risk prediction accuracy in HF, AMI, and PN patients compared to generic models from the literature?
Do institution-specific prediction models improve readmission risk prediction accuracy in HF, AMI, and PN patients compared to generic models from the literature?
Institution-specific statistical models improve the accuracy of predicting hospital readmissions for HF, AMI, and pneumonia compared to generic models.
Institution-specific models may improve readmission risk stratification for HF, AMI, and pneumonia; extends evidence favoring tailored over generic prediction approaches.
The ability to predict patient readmission risk is extremely valuable for hospitals, especially under the Hospital Readmission Reduction Program (HRRP) of the Center for Medicare and Medicaid Services (CMS) which went into effect starting October 1, 2012. There is a plethora of work in the literature that deals with developing readmission risk prediction models, but most of them do not have sufficient prediction accuracy to be deployed in a clinical setting, partly because different hospitals may have different characteristics in their patient populations. In this work we experimented with a generic framework for institution-specific readmission risk prediction, which takes patient data from a single institution and produces a statistical risk prediction model optimized for that particular institution and optionally condition specific. This provides great flexibility in model building, and is also able to provide institution-specific insights in its readmitted patient population. We showcase some initial results at three institutions for Heart Failure (HF), Acute Myocardial Infarction (AMI) and Pneumonia (PN) patients. The developed models yield better prediction accuracy than the ones present in the literature.
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Yu et al. (2013) studied Heart Failure, Acute Myocardial Infarction, and Pneumonia. Institution-specific readmission risk prediction models vs. Models present in the literature was evaluated on Prediction accuracy for readmission risk. Institution-specific readmission risk prediction models for heart failure, acute myocardial infarction, and pneumonia yielded better prediction accuracy than existing models in the literature.
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