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Authors: Sally McClean 1 ; David Stanford 2 ; Lalit Garg 3 and Naveed Khan 4

Affiliations: 1 School of Computing, Ulster University, Coleraine, BT52 1SA, U.K. ; 2 Department of Statistical & Actuarial Sciences, Western University, London, ON N6A 5B7, Canada ; 3 Faculty of Information & Communication Technology, University of Malta, Msida, Malta ; 4 School of Computing, Ulster University, Jordanstown, BT37 0QB, U.K.

ISBN: 978-989-758-352-0

Keyword(s): Process Mining, Process Modelling, Phase-Type Models, Targets.

Abstract: 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 foc us 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. (More)

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Paper citation in several formats:
McClean, S.; Stanford, D.; Garg, L. and Khan, N. (2019). Using Phase-type Models to Monitor and Predict Process Target Compliance.In Proceedings of the 8th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES, ISBN 978-989-758-352-0, pages 82-90. DOI: 10.5220/0007362200820090

@conference{icores19,
author={Sally I. McClean. and David A. Stanford. and Lalit Garg. and Naveed Khan.},
title={Using Phase-type Models to Monitor and Predict Process Target Compliance},
booktitle={Proceedings of the 8th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,},
year={2019},
pages={82-90},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007362200820090},
isbn={978-989-758-352-0},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,
TI - Using Phase-type Models to Monitor and Predict Process Target Compliance
SN - 978-989-758-352-0
AU - McClean, S.
AU - Stanford, D.
AU - Garg, L.
AU - Khan, N.
PY - 2019
SP - 82
EP - 90
DO - 10.5220/0007362200820090

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