书目名称 | Optimizing Hospital-wide Patient Scheduling |
副标题 | Early Classification |
编辑 | Daniel Gartner |
视频video | |
概述 | Introduces and evaluates a thorough examination of attribute selection techniques and classification approaches for early diagnosis-related group (DRG) classification.Formulates two hospital-wide pati |
丛书名称 | Lecture Notes in Economics and Mathematical Systems |
图书封面 |  |
描述 | 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. |
出版日期 | Book 2014 |
关键词 | DRG Classification; Diagnosis-related groups; Hospital management; Machine learning; Patient scheduling |
版次 | 1 |
doi | https://doi.org/10.1007/978-3-319-04066-0 |
isbn_softcover | 978-3-319-04065-3 |
isbn_ebook | 978-3-319-04066-0Series ISSN 0075-8442 Series E-ISSN 2196-9957 |
issn_series | 0075-8442 |
copyright | Springer International Publishing Switzerland 2014 |