Optimizing Hospitalwide Patient Scheduling: Early Classification of Diagnosisrelated Groups Through Machine Learning (Lecture ,Used

Optimizing Hospitalwide Patient Scheduling: Early Classification of Diagnosisrelated Groups Through Machine Learning (Lecture ,Used

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Diagnosisrelated groups (DRGs) are used in hospitals for the reimbursement of inpatient services. The assignment of a patient to a DRG can be distinguished into billing and operationsdriven DRG classification. The topic of this monograph is operationsdriven DRG classification, in which DRGs of inpatients are employed to improve contribution marginbased patient scheduling decisions. In the first part, attribute selection and classification techniques are evaluated in order to increase early DRG classification accuracy. Employing mathematical programming, the hospitalwide flow of elective patients is modelled taking into account DRGs, clinical pathways and scarce hospital resources. The results of the early DRG classification part reveal that a small set of attributes is sufficient in order to substantially improve DRG classification accuracy as compared to the current approach of many hospitals. Moreover, the results of the patient scheduling part reveal that the contribution margin can be increased as compared to current practice.

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