Process mining based on patient waiting time: an application in health processes

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dc.authoridDogan, Onur/0000-0003-3543-4012
dc.authorwosidDogan, Onur/HPC-1959-2023
dc.contributor.authorDogan, Onur
dc.date.accessioned2023-03-22T19:47:34Z
dc.date.available2023-03-22T19:47:34Z
dc.date.issued2022
dc.departmentBelirleneceken_US
dc.description.abstractPurpose Similar to many business processes, waiting times are also essential for health care processes, especially in obstetrics and gynecology outpatient department (GOD), because pregnant women may be affected by long waiting times. Since creating process models manually presents subjective and nonrealistic flows, this study aims to meet the need of an objective and realistic method. Design/methodology/approach In this study, the authors investigate time-related bottlenecks in both departments for different doctors by process mining. Process mining is a pragmatic analysis to obtain meaningful insights through event logs. It applies data mining techniques to business process management with more comprehensive perspectives. Process mining in this study enables to automatically create patient flows to compare considering each department and doctor. Findings The study concludes that average waiting times in the GOD are higher than obstetrics outpatient department. However, waiting times in departments can change inversely for different doctors. Research limitations/implications The event log was created by expert opinions because activities in the processes had just starting timestamp. The ending time of activity was computed by considering the average duration of the corresponding activity under a normal distribution. Originality/value This study focuses on administrative (nonclinical) health processes in obstetrics and GOD. It uses a parallel activity log inference algorithm (PALIA) to produce process trees by handling duplicate activities. Infrequent information in health processes can have critical information about the patient. PALIA considers infrequent activities in the event log to extract meaningful information, in contrast to many discovery algorithms.en_US
dc.identifier.doi10.1108/IJWIS-02-2022-0027
dc.identifier.endpage254en_US
dc.identifier.issn1744-0084
dc.identifier.issn1744-0092
dc.identifier.issue5/6en_US
dc.identifier.scopus2-s2.0-85132298807en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage240en_US
dc.identifier.urihttps://doi.org/10.1108/IJWIS-02-2022-0027
dc.identifier.urihttps://hdl.handle.net/20.500.14034/769
dc.identifier.volume18en_US
dc.identifier.wosWOS:000812022300001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherEmerald Group Publishing Ltden_US
dc.relation.journalInternational Journal Of Web Information Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAdvanced Web applicationsen_US
dc.subjectE-business models and architecturesen_US
dc.subjectE-business application case studiesen_US
dc.subjectProcess miningen_US
dc.subjectObstetrics and gynecologyen_US
dc.subjectTime-related bottlenecksen_US
dc.subjectPatient behaviorsen_US
dc.subjectHealth care processesen_US
dc.subjectCareen_US
dc.subjectTrackingen_US
dc.titleProcess mining based on patient waiting time: an application in health processesen_US
dc.typeArticleen_US

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