000 03209nam a22005655i 4500
001 978-3-319-04066-0
003 DE-He213
005 20200420211745.0
007 cr nn 008mamaa
008 150523s2014 gw | s |||| 0|eng d
020 _a9783319040660
_9978-3-319-04066-0
024 7 _a10.1007/978-3-319-04066-0
_2doi
050 4 _aHD30.23
072 7 _aKJT
_2bicssc
072 7 _aKJMD
_2bicssc
072 7 _aBUS049000
_2bisacsh
082 0 4 _a658.40301
_223
100 1 _aGartner, Daniel.
_eauthor.
245 1 0 _aOptimizing Hospital-wide Patient Scheduling
_h[electronic resource] :
_bEarly Classification of Diagnosis-related Groups Through Machine Learning /
_cby Daniel Gartner.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2014.
300 _aXIV, 119 p. 22 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Economics and Mathematical Systems,
_x0075-8442 ;
_v674
505 0 _aIntroduction -- Machine learning for early DRG classification -- Scheduling the hospital-wide flow of elective patients -- Experimental analyses -- Conclusion.
520 _aDiagnosis-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.
650 0 _aBusiness.
650 0 _aOperations research.
650 0 _aDecision making.
650 0 _aHealth care management.
650 0 _aHealth services administration.
650 0 _aHealth informatics.
650 0 _aManagement science.
650 1 4 _aBusiness and Management.
650 2 4 _aOperation Research/Decision Theory.
650 2 4 _aHealth Informatics.
650 2 4 _aHealth Informatics.
650 2 4 _aOperations Research, Management Science.
650 2 4 _aHealth Care Management.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319040653
830 0 _aLecture Notes in Economics and Mathematical Systems,
_x0075-8442 ;
_v674
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-04066-0
912 _aZDB-2-SBE
942 _cEBK
999 _c50930
_d50930