Improving Patient Flows at St. Andrew’s War Memorial Hospital’s Emergency Department Through Process Mining

  • Robert AndrewsEmail author
  • Suriadi Suriadi
  • Moe Wynn
  • Arthur H. M. ter Hofstede
  • Sean Rothwell
Part of the Management for Professionals book series (MANAGPROF)


  1. (a)

    Situation faced: Improving Emergency Department (ED) patient flows in terms of processing time, resource use, costs, and patient outcomes is a priority for health service professionals and is vital to the delivery of safe, timely, and effective patient care. Poor patient flows manifest as overcrowding in the ED, prolonged length of stay (LoS), patients “boarding” in EDs and “access block” for admission to inpatient wards. Consequences include poor patient outcomes, reduced access for new patients who present at the ED, and negative effects on staff, including dissatisfaction and stress. Further motivation for improving patient flows in EDs arises because Commonwealth- and state-sponsored financial incentives for hospitals are tied to achieving targets for improved patient access to emergency services. One measure of such improved access is meeting nationally agreed targets for the percentage of patients who are physically discharged from the ED within 4 h of arrival.

  2. (b)

    Action taken: A key challenge in deriving evidence-based improvements for patient flows is that of gaining insight into the process factors and context factors that affect patient flows. The case study reported here adopted the BPM Lifecycle reference framework to improve patient flows. In particular we focused on the process identification, discovery, and analysis phases of the BPM Lifecycle. Process-oriented data-mining techniques were applied to real practices to discover models of current patient flows in the ED of St. Andrew’s War Memorial Hospital (SAWMH) in Queensland, Australia. The discovered models were used to evaluate the effect on patient flows of certain context factors of interest to stakeholders. Case histories of 1473 chest pain presentations at SAWMH between September 2011 and March 2013 were analyzed to determine process differences between ED patients with short stays (<4 h) and those with long stays (>4 h).

  3. (c)

    Results achieved: Process models were discovered for the hospital’s ED patient flow. From a control-flow perspective, only minor differences were observed between short- and long-stay patients at SAWMH, although there were timing differences in reaching specific milestone events. Waiting time in the ED following a request for hospital admission added significantly to overall ED LoS.

  4. (d)

    Lessons learned: This project demonstrated that process mining is applicable to complex, semi-structured processes like those found in the healthcare domain. Comparative process performance analysis yielded some insights into ED patient flows, including recognition of recurring data-quality issues in datasets extracted from hospital information systems. The templated recognition and resolution of such issues offers a research opportunity to develop a (semi-)automated data-cleaning approach that would alleviate the tedious manual effort required to produce high-quality logs. The project highlighted the importance of hospital information systems collecting both start and end times of activities for proper performance analysis (duration, wait time, bottlenecks). Additions to our process-mining toolset include novel comparative process-performance visualization techniques that highlight the similarities and differences among process cohorts.




The research for this article was supported by an Australian Centre for Health Services Innovation (AusHSI) Stimulus Grant. We gratefully acknowledge the contributions to this project of Dr. Kelly Foster, Annette Bailey, Cath Voysey, Dr. Raffaele Conforti, Dr. Anastasiia Pika, and Huang Huy Nguyen.


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Robert Andrews
    • 1
    Email author
  • Suriadi Suriadi
    • 1
  • Moe Wynn
    • 1
  • Arthur H. M. ter Hofstede
    • 1
  • Sean Rothwell
    • 2
  1. 1.Queensland University of TechnologyBrisbaneAustralia
  2. 2.UnitingCare HealthBrisbaneAustralia

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