Measuring patient flow variations: a cross-organisational process mining approach

Suriadi Suriadi, Ronny Mans, Moe T. Wynn, Andrew Partington, Jonathon Karnon

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

34 Citations (Scopus)

Abstract

Variations that exist in the treatment of patients (with similar symptoms) across different hospitals do substantially impact the quality and costs of healthcare. Consequently, it is important to understand the similarities and differences between the practices across different hospitals. This paper presents a case study on the application of process mining techniques to measure and quantify the differences in the treatment of patients presenting with chest pain symptoms across four South Australian hospitals. Our case study focuses on cross-organisational benchmarking of processes and their performance. Techniques such as clustering, process discovery, performance analysis, and scientific workflows were applied to facilitate such comparative analyses. Lessons learned in overcoming unique challenges in cross-organisational process mining, such as ensuring population comparability, data granularity comparability, and experimental repeatability are also presented.
Original languageEnglish
Title of host publicationAsia Pacific Business Process Management
Subtitle of host publicationSecond Asia Pacific Conference, AP-BPM 2014 Brisbane, QLD, Australia, July 3-4, 2014 : Proceedings
EditorsChun Quyang, Jae-Yoon Jung
Place of PublicationCham
PublisherSpringer, Springer Nature
Pages43-58
Number of pages16
ISBN (Electronic)9783319082226
ISBN (Print)9783319082219
DOIs
Publication statusPublished - 2014
Externally publishedYes

Publication series

NameLecture Notes in Business Information Processing
PublisherSpringer, Springer Nature
Volume181
ISSN (Print)1865-1348
ISSN (Electronic)1865-1356

Keywords

  • Patient flow
  • Data mining
  • Data quality
  • Process Mining

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