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

Suriadi, Suriadi, Mans, Ronny S., Wynn, Moe T., Partington, Andrew, & Karnon, Jonathan (2014) Measuring patient flow variations : a cross-organisational process mining approach. Asia Pacific Business Process Management, 181, pp. 43-58.

View at publisher

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.

Impact and interest:

3 citations in Scopus
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 74810
Item Type: Journal Article
Refereed: Yes
Keywords: Process mining, data quality, patient flow, data mining
DOI: 10.1007/978-3-319-08222-6_4
ISBN: 978-3-319-08222-6
ISSN: 1865-1356
Divisions: Current > Schools > School of Information Systems
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: © Springer International Publishing Switzerland
Deposited On: 07 Aug 2014 05:12
Last Modified: 07 May 2015 03:23

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page