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Real-time prediction of unplanned 30-day hospital readmissions


Peng Zhao






Leadership Auditorium, 2501 Student Center

Hospital readmissions are frequent and costly. It has been estimated that unplanned readmissions account for $17.4 billion in Medicare expenditures annually. Since the fiscal year 2013, the Hospital Readmissions Reduction Program (HRRP) has been established to financially penalize hospitals with excessive readmissions after initial admissions for particular conditions and procedures. In recent years, numerous hospital readmission predictive models have been reported and most of them rely on attributes that are only available near or post-discharge of the current encounter, such as the length of stay, discharge disposition, diagnosis codes. By incorporating these attributes, it is impossible to perform real-time readmission prediction during an inpatient encounter. However, early prediction of readmission can help deliver timely interventions to reduce the readmission risk. In this work, a machine learning predictive model has been built based on the Health Facts data. Because this model focuses more on patients’ medical history and requires less information from the current encounter, it allows real-time readmission prediction. I will discuss more details during the seminar.