Optimizing Hospital-wide Patient Scheduling: Early Classification of Diagnosis-related Groups Through Machine Learning

132 Pages·2015·2.308 MB·English

by  Daniel Gartner

Lecture Notes in Economics and Mathematical Systems 674
Daniel Gartner
Optimizing
Hospital-
wide Patient
Scheduling
Early Classification of Diagnosis-
related Groups Through Machine
Learning
Lecture Notes in Economics
and Mathematical Systems 674
FoundingEditors:
M.Beckmann
H.P.Künzi
ManagingEditors:
Prof.Dr.G.Fandel
FachbereichWirtschaftswissenschaften
FernuniversitätHagen
Hagen,Germany
Prof.Dr.W.Trockel
MuratSertelInstituteforAdvancedEconomicResearch
IstanbulBilgiUniversity
Istanbul,Turkey
and
InstitutfürMathematischeWirtschaftsforschung(IMW)
UniversitätBielefeld
Bielefeld,Germany
EditorialBoard:
H.Dawid,D.Dimitrov,A.Gerber,C-J.Haake,C.Hofmann,T.Pfeiffer,
R.Slowin´ski,W.H.M.Zijm
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Daniel Gartner
Optimizing Hospital-wide
Patient Scheduling
Early Classification of Diagnosis-related
Groups Through Machine Learning
123
DanielGartner
TUMSchoolofManagement
TechnischeUniversitätMünchen
München
Germany
DissertationattheTechnischeUniversitätMünchen,TUMSchoolofManagement,submitted
onJune12th,2013andacceptedonJuly15th,2013
ISSN0075-8442
ISBN978-3-319-04065-3 ISBN978-3-319-04066-0(eBook)
DOI10.1007/978-3-319-04066-0
SpringerChamHeidelbergNewYorkDordrechtLondon
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Acknowledgements
First and foremost, I would like to thank Prof. Dr. Rainer Kolisch, who offered
me a researchposition at the TUM SchoolofManagement,providedme a highly
interesting research topic and supported me in every aspect of my research.
Furthermore, my gratitude goes to Prof. Rema Padman, PhD, and Prof. Daniel
Bertrand Neill, PhD, who offered me the opportunity to collaborate on research
at the Heinz College, Carnegie Mellon University, USA. I would like to thank in
particular Prof. Rema Padman, PhD, for joining the dissertation committee and
Prof.Dr.GuntherFriedlforchairingit.
I amverygratefultoDr. DirkLastandMartinKornhaasofthecountyhospital
Erding for contributing their clinical experienceto my research and for providing
thedataforthecasestudies.
I would like to offermy thanksto former and currentcolleaguesat the depart-
ment: Claus Henning Brech, Prof. Dr. Jens Otto Brunner, Dr. André Dahlmann,
Jia Yan Du, Thomas Fliedner, Markus Matthäus Frey, Dr. Andreas Fügener,
Dr.ChristianHeimerl,FerdinandKiermaier,Dr.PhilippMelchiors,AnularkNaber,
PhD,SebastianSchiffelsandDr.Hans-JörgSchützformanyvaluablediscussions.
Moreover,IamgratefultoStephenStarck,PhD,fromtheTUMlanguagecenterfor
hisvaluablefeedbackonacademicwriting.
Finally,IwanttothankInesVerenaArnoldsandmyfamilyfortheirinvaluable
support.
Munich,Germany DanielGartner
June2013
v

Contents
1 Introduction .................................................................. 1
1.1 DRG-SystemsandtheEconomicSituationinHospitals.............. 1
1.2 NecessityofaHolisticPlanningApproach............................ 4
1.3 Strategic,TacticalandOperationalProblemsinHospitals............ 5
1.4 TopicofThisDissertation .............................................. 6
1.5 Outline................................................................... 8
2 MachineLearningforEarlyDRGClassification......................... 9
2.1 MachineLearningforHealthCare:ALiteratureReview............. 9
2.1.1 SelectionCriteriaandSearchforRelevantLiterature....... 10
2.1.2 ClassificationofRelevantLiterature ......................... 11
2.2 AttributeRankingandSelectionTechniquesEmployed
forEarlyDRGClassification........................................... 15
2.2.1 InformationGainAttributeRanking ......................... 15
2.2.2 Relief-FAttributeRanking ................................... 17
2.2.3 MarkovBlanketAttributeSelection.......................... 20
2.2.4 Correlation-BasedFeatureSelection......................... 24
2.2.5 WrapperAttributeSelection.................................. 24
2.3 Classification Techniques Employed for Early
DRGClassification ..................................................... 25
2.3.1 NaiveBayes ................................................... 26
2.3.2 BayesianNetworks............................................ 26
2.3.3 ClassificationTrees............................................ 27
2.3.4 Voting-BasedCombinedClassification ...................... 30
2.3.5 ProbabilityAveragingtoCombinetheDRG
GrouperwithMachineLearningApproaches................ 31
2.3.6 DecisionRule-BasedMappingofAttribute
ValuestoDRGs................................................ 31
3 SchedulingtheHospital-WideFlowofElectivePatients ................ 33
3.1 Mathematical Programming Applied to Patient
SchedulinginHospitals................................................. 33
vii
viii Contents
3.1.1 SelectionCriteriaandSearchforRelevantLiterature....... 34
3.1.2 ClassificationofRelevantLiterature ......................... 34
3.2 ThePatientFlowProblemwithFixedAdmissionDates.............. 41
3.3 ThePatientFlowProblemwithVariableAdmissionDates........... 46
3.4 AnExampleofthePatientFlowProblemwithFixed
andVariableAdmissionDates.......................................... 48
3.5 A Rolling Horizon Approach for Scheduling
theHospital-WideFlowofElectivePatients .......................... 51
4 ExperimentalAnalyses...................................................... 55
4.1 ExperimentalEvaluationoftheEarlyDRGClassification ........... 55
4.1.1 DatafromPatientsThatContacttheHospital
BeforeAdmission(ElectivePatients) ........................ 55
4.1.2 DatafromAllPatientsAvailableatAdmission
(ElectiveandNon-electivePatients).......................... 57
4.1.3 ResultsoftheAttributeRankingandSelection.............. 60
4.1.4 EvaluationTechniquesfortheClassificationPart ........... 63
4.1.5 ComputationTimes ........................................... 64
4.1.6 ParameterOptimizationfortheClassificationTree ......... 68
4.1.7 ResultsoftheClassificationTechniques..................... 70
4.1.8 InvestigationonMajorDiagnosticCategories............... 76
4.1.9 InvestigationonSelectedDRGs.............................. 79
4.1.10 EvaluationofExpectedRevenueEstimates.................. 82
4.2 ComputationalandEconomicAnalysisofScheduling
theHospital-WideFlowofElectivePatients .......................... 84
4.2.1 DataandInstanceGeneration................................. 84
4.2.2 ComputationTime Analysis of the Static
Approaches.................................................... 87
4.2.3 EconomicAnalysisoftheStaticApproaches................ 87
4.2.4 EconomicAnalysisoftheRollingHorizonApproach ...... 88
5 Conclusion.................................................................... 93
5.1 Summary ................................................................ 93
5.2 MainResearchContributions........................................... 95
5.3 FutureResearch......................................................... 95
A NotationandListofAbbreviations........................................ 97
B AttributesAssessedandRankingResultsfortheEarly
DRGClassification........................................................... 101
Bibliography...................................................................... 109
List of Figures
Fig.1.1 HierarchicalDRG-groupingprocess(See
Schreyöggetal.[195])................................................ 3
Fig.1.2 Ascheduleforasurgical(a)andtherapeutical
clinicalpathway(b) ................................................... 5
Fig.2.1 Three-levelstructureofthe searchqueryincluding
relevanttasks (a), machine learning methods(b)
andfieldofresearch(c) ............................................... 10
Fig.2.2 MarkovblanketofvertexD........................................... 20
Fig.2.3 Causal networkofthe example(see Scutari[199])
includingtheMarkovblanketofattributeA(dashed
grayrectangle)......................................................... 23
Fig.2.4 DifferentstepsduringtheMarkovblanketsearch ................... 23
Fig.2.5 Sampleclassificationtree ............................................. 28
Fig.2.6 Selected part of the classification tree beforethe
additionoffurthernodes.............................................. 29
Fig.2.7 Finalclassificationtree................................................ 30
Fig.3.1 Twoexamplerevenue,costandcontribution(contrib.)
margin functions.(a) Revenue includingper day
surchargeforreachingthefirstandtheseconddayafter
thehighLOStrimpoint.(b)Constantrevenueafter
reachingthelowLOStrimpoint...................................... 43
Fig.3.2 ClinicalpathwaysforthePFP-FA(a)andthePFP-VA(b).......... 49
Fig.3.3 OptimalsolutionforthePFP-FA(a)andthePFP-VA(b)........... 50
Fig.3.4 Rollinghorizonprocedure ............................................ 52
Fig.3.5 Timewindowadaptionsub-routine................................... 53
Fig.4.1 DRGfrequencydistributionforelectivepatients .................... 56
Fig.4.2 DRGfrequencydistributionforallpatients.......................... 58
Fig.4.3 Assignmentofadmissionvs.dischargediagnosestoallpatients... 59
ix