rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Optimizing Hospital-wide Patient Scheduling
    sub title: Early Classification of Diagnosis-related Groups Through Machine Learning
  name:
    name part: Gartner, Daniel.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2014
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XIV, 119 p. 22 illus. online resource.
  abstract: 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.
  table of contents: Introduction -- Machine learning for early DRG classification -- Scheduling the hospital-wide flow of elective patients -- Experimental analyses -- Conclusion.
  note: by Daniel Gartner.
  subject:
    topic: Business
  subject:
    topic: Operations research
  subject:
    topic: Decision making
  subject:
    topic: Health care management
  subject:
    topic: Health services administration
  subject:
    topic: Health informatics
  subject:
    topic: Management science
  subject:
    topic: Business and Management
  subject:
    topic: Operation Research/Decision Theory
  subject:
    topic: Health Informatics
  subject:
    topic: Health Informatics
  subject:
    topic: Operations Research, Management Science
  subject:
    topic: Health Care Management
  classification: HD30.23
  classification: 658.40301
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Lecture Notes in Economics and Mathematical Systems, 674
  identifier: 9783319040660
  identifier: http://dx.doi.org/10.1007/978-3-319-04066-0
  record info:
    record creation date: 150523
    record change date: 20151204160721.0
    record identifier: 978-3-319-04066-0