LEADER 04281nam  22008055  450 
001 9910298544003321
005 20200920005401.0
010   $a3-319-04065-0
010   $a3-319-04066-9
024 7 $a10.1007/978-3-319-04066-0
035   $a(CKB)2670000000618818
035   $a(EBL)2096710
035   $a(SSID)ssj0001500763
035   $a(PQKBManifestationID)11878154
035   $a(PQKBTitleCode)TC0001500763
035   $a(PQKBWorkID)11519123
035   $a(PQKB)10767067
035   $a(MiAaPQ)EBC2096710
035   $a(DE-He213)978-3-319-04066-0
035   $a(PPN)186030185
035   $a(EXLCZ)992670000000618818
100   $a20150523d2014      u|         0
101 0 $aeng
135   $aur|n|---|||||
181   $ctxt
182   $cc
183   $acr
200 10$aOptimizing Hospital-wide Patient Scheduling $eEarly Classification of Diagnosis-related Groups Through Machine Learning /$fby Daniel Gartner
205   $a1st ed. 2014.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2014.
215   $a1 online resource (132 p.)
225 1 $aLecture Notes in Economics and Mathematical Systems,$x0075-8442 ;$v674
300   $aDescription based upon print version of record.
311 08$aPrinted edition: 9783319040653 
320   $aIncludes bibliographical references.
327   $aIntroduction -- Machine learning for early DRG classification -- Scheduling the hospital-wide flow of elective patients -- Experimental analyses -- Conclusion.
330   $aDiagnosis-related groups (DRGs) are used in hospitals for the reimbursement of inpatient services. The assignment of a patient to a DRG can be distinguished into billing- and operations-driven DRG classification. The topic of this monograph is operations-driven DRG classification, in which DRGs of inpatients are employed to improve contribution margin-based patient scheduling decisions. In the first part, attribute selection and classification techniques are evaluated in order to increase early DRG classification accuracy. Employing mathematical programming, the hospital-wide flow of elective patients is modelled taking into account DRGs, clinical pathways and scarce hospital resources. The results of the early DRG classification part reveal that a small set of attributes is sufficient in order to substantially improve DRG classification accuracy as compared to the current approach of many hospitals. Moreover, the results of the patient scheduling part reveal that the contribution margin can be increased as compared to current practice.
410  0$aLecture Notes in Economics and Mathematical Systems,$x0075-8442 ;$v674
606   $aOperations research
606   $aDecision making
606   $aHealth informatics
606   $aManagement science
606   $aHealth care management
606   $aHealth services administration
606   $aOperations Research/Decision Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/521000
606   $aHealth Informatics$3https://scigraph.springernature.com/ontologies/product-market-codes/H28009
606   $aHealth Informatics$3https://scigraph.springernature.com/ontologies/product-market-codes/I23060
606   $aOperations Research, Management Science$3https://scigraph.springernature.com/ontologies/product-market-codes/M26024
606   $aHealth Care Management$3https://scigraph.springernature.com/ontologies/product-market-codes/527030
615  0$aOperations research.
615  0$aDecision making.
615  0$aHealth informatics.
615  0$aManagement science.
615  0$aHealth care management.
615  0$aHealth services administration.
615 14$aOperations Research/Decision Theory.
615 24$aHealth Informatics.
615 24$aHealth Informatics.
615 24$aOperations Research, Management Science.
615 24$aHealth Care Management.
676   $a330
676   $a36.210.681
676   $a502.85
676   $a519.6
676   $a658.40301
700   $aGartner$b Daniel$4aut$4http://id.loc.gov/vocabulary/relators/aut$01058004
906   $aBOOK
912   $a9910298544003321
996   $aOptimizing Hospital-wide Patient Scheduling$92496446
997   $aUNINA