Optimizing Hospital-wide Patient Scheduling

Early Classification of Diagnosis-related Groups Through Machine Learning
2014
ISBN: 978-3-319-04065-3
Verlag: Springer International Publishing

Buch, Englisch, Band 674, 119 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 2175 g

Reihe: Lecture Notes in Economics and Mathematical Systems

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.

Zielgruppe

Research

Autoren/Hrsg.

Gartner, Daniel

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Diagnosis Related Groups (DRG), Medizinische Klassifikation, Medizinische Gutachten
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Krankenhausmanagement, Praxismanagement
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsinformatik, SAP, IT-Management

Inhaltsverzeichnis

Introduction.- Machine learning for early DRG classification.- Scheduling the hospital-wide flow of elective patients.- Experimental analyses.- Conclusion.

Über Autor(innen)

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.