Diagnosis-related 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
operations-driven DRG classification. The topic of this monograph is operations-driven DRG
classification in which DRGs of inpatients are employed to improve contribution margin-based
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 hospital-wide 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.