000 | 03209nam a22005655i 4500 | ||
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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 |
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024 | 7 |
_a10.1007/978-3-319-04066-0 _2doi |
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050 | 4 | _aHD30.23 | |
072 | 7 |
_aKJT _2bicssc |
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_aKJMD _2bicssc |
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_aBUS049000 _2bisacsh |
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082 | 0 | 4 |
_a658.40301 _223 |
100 | 1 |
_aGartner, Daniel. _eauthor. |
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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. |
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300 |
_aXIV, 119 p. 22 illus. _bonline resource. |
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_atext _btxt _2rdacontent |
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_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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490 | 1 |
_aLecture Notes in Economics and Mathematical Systems, _x0075-8442 ; _v674 |
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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 |