Optimizing Hospital-wide Patient Scheduling: Early Classification of Diagnosis-related Groups Through Machine Learning: 674 (Lecture Notes in Economics and Mathematical Systems) - Tapa blanda

Gartner, Daniel

 
9783319040653: Optimizing Hospital-wide Patient Scheduling: Early Classification of Diagnosis-related Groups Through Machine Learning: 674 (Lecture Notes in Economics and Mathematical Systems)
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ISBN 10: 3319040650 ISBN 13: 9783319040653
Editorial: Springer, 2015

Sinopsis

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.

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Acerca del autor

Daniel Gartner earned his doctoral degree in Operations Management at the TUM School of Management, Technische Universität München, Germany. His research examines optimization problems in health care and machine learning techniques to improve hospital-wide scheduling decisions. Prior to joining TUM he received his university diploma (Master's equivalent) in medical informatics from the University of Heidelberg, Germany, and a M.Sc. in Networks and Information Systems from the Université Claude Bernard Lyon, France.

De la contraportada

?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.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
Springer
Año de publicación
2015
Idioma
Inglés
ISBN 10 
3319040650
ISBN 13 
9783319040653
Encuadernación
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Número de páginas
136
Contacto del fabricante
no disponible
Persona responsable
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Otras ediciones populares con el mismo título

9783319040677: Optimizing Hospital-wide Patient Scheduling: Early Classification of Diagnosis-related Groups Through Machine Learning

Edición Destacada

ISBN 10:  3319040677 ISBN 13:  9783319040677
Editorial: Springer, 2015
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