Lecture Notes in Economics and Mathematical Systems

Optimizing Hospital-wide Patient Scheduling

Early Classification of Diagnosis-related Groups Through Machine Learning

Authors: Gartner, Daniel

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  • Introduces and evaluates a thorough examination of attribute selection techniques and classification approaches for early diagnosis-related group (DRG) classification
  • Formulates two hospital-wide patient scheduling models using mathematical programming in order to maximize contribution margin
  • Presents methods for a substantial improvement of classification accuracy and contribution margin as compared to current practice
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About this book

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.

About the authors

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.

Table of contents (5 chapters)

Table of contents (5 chapters)

Buy this book

eBook $64.99
price for USA in USD
  • ISBN 978-3-319-04066-0
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $84.99
price for USA in USD
  • ISBN 978-3-319-04065-3
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
  • Usually ready to be dispatched within 3 to 5 business days, if in stock
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Bibliographic Information

Bibliographic Information
Book Title
Optimizing Hospital-wide Patient Scheduling
Book Subtitle
Early Classification of Diagnosis-related Groups Through Machine Learning
Authors
Series Title
Lecture Notes in Economics and Mathematical Systems
Series Volume
674
Copyright
2014
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing Switzerland
eBook ISBN
978-3-319-04066-0
DOI
10.1007/978-3-319-04066-0
Softcover ISBN
978-3-319-04065-3
Series ISSN
0075-8442
Edition Number
1
Number of Pages
XIV, 119
Number of Illustrations
22 b/w illustrations
Topics