Modeling of patient risk factors at discharge

Abstract

A medical system includes a modeling unit (10) which generates a plurality of tree structured classifiers based on a collection of demographic, socio-econometric, diagnoses, procedure, hospital, and logistical data elements, learns patient discharge risk factors based on the plurality of tree structured classifiers and data corresponding to prior patient discharges, and creates a predictive model of readmission based on the learned patient discharge risk factors which scores the identified patient discharge risk factors for one or more patient discharges.

Description

technical field [0001] The following generally relates to medical systems. It finds particular application in connection with making patient discharge decisions, developing discharge strategies and will be described with particular reference thereto. It will however be understood that it applies in other use cases as well and is not necessarily limited to the aforementioned applications. Background technique [0002] Inpatient services are a major portion of healthcare services that can include significant cost expenditures. Avoiding readmissions after a patient has been discharged can provide significant cost savings. Currently, 17.6% of acute care admissions result in readmission after discharge and cost $15B. Medical service providers receive financial incentives from reimbursement providers, such as Medicare and Medicaid, which include penalties for readmission above certain thresholds. For example, in September 2012, the Centers for Medicare and Medicaid Services be...

AI Technical Summary

Problems solved by technology

Current pathways do not facilitate development of hospital strategies to address readmissions

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