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

Process mining based on patient waiting time: an application in health processes 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/approachIn 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.FindingsThe 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/implicationsThe 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/valueThis 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Web Information Systems Emerald Publishing

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

International Journal of Web Information Systems , Volume 18 (5/6): 15 – Dec 12, 2022

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References (47)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1744-0084
eISSN
1744-0084
DOI
10.1108/ijwis-02-2022-0027
Publisher site
See Article on Publisher Site

Abstract

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/approachIn 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.FindingsThe 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/implicationsThe 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/valueThis 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.

Journal

International Journal of Web Information SystemsEmerald Publishing

Published: Dec 12, 2022

Keywords: Advanced Web applications; E-business models and architectures; E-business application case studies; Process mining; Obstetrics and gynecology; Time-related bottlenecks; Patient behaviors; Health care processes

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