Processes are ubiquitous, spanning diverse areas such as business, production, telecommunications and healthcare. They have been studied and modelled for many years in an attempt to increase understanding, improve efficiency and predict future pathways, events and outcomes. More recently, process mining has emerged with the intention of discovering, monitoring, and improving processes, typically using data extracted from event logs. This may include discovering the tasks within the overall processes, predicting future trajectories, or identifying anomalous tasks. We focus on using phase-type process modelling to measure compliance with known targets and, inversely, determine suitable targets given a threshold percentage required for satisfactory performance. We illustrate the ideas with an application to a stroke patient care process, where there are multiple outcomes for patients, namely discharge to normal residence, nursing home, or death. Various scenarios are explored, with a focus on determining compliance with given targets; such KPIs are commonly used in Healthcare as well as for Business and Industrial processes. We believe that this approach has considerable potential to be extended to include more detailed and explicit models that allow us to assess complex scenarios. Phase-type models have an important role in this work.
|Title of host publication||ICORES 2019: Proceedings of the 8th International Conference on Operations Research and Enterprise Systems|
|Editors||Greg H. Parlier, Federico Liberatore, Marc Demange|
|Number of pages||9|
|Publication status||Published - 15 Feb 2019|
|Event||8th International Conference on Operations Research and Enterprise Systems, ICORES 2019 - Prague, Czech Republic|
Duration: 19 Feb 2019 → 21 Feb 2019
|Name||ICORES 2019 - Proceedings of the 8th International Conference on Operations Research and Enterprise Systems|
|Conference||8th International Conference on Operations Research and Enterprise Systems, ICORES 2019|
|Period||19/02/2019 → 21/02/2019|
Bibliographical noteFunding Information:
This research is partly supported by BTIIC (BT Ireland Innovation Centre), funded by BT and Invest Northern Ireland and by the Natural Sciences and Engineering Research Council of Canada (NSERC).
Copyright © 2019 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.
Copyright 2020 Elsevier B.V., All rights reserved.
- Phase-type models
- Process mining
- Process modelling
ASJC Scopus subject areas
- Management Science and Operations Research
- Computational Theory and Mathematics
- Computer Science Applications
- Control and Systems Engineering