Pre-hospital Retrieval and Transport of Road Trauma Patients in Queensland

A Process Mining Analysis
  • Robert AndrewsEmail author
  • Moe T. Wynn
  • Kirsten Vallmuur
  • Arthur H. M. ter Hofstede
  • Emma Bosley
  • Mark Elcock
  • Stephen Rashford
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 342)


Existing process mining methodologies, while noting the importance of data quality, do not provide details on how to assess the quality of event data and how the identification of data quality issues can be exploited in the planning, data extraction and log building phases of any process mining analysis. To this end we adapt CRISP-DM [15] to supplement the Planning phase of the PM\(^2\) [6] process mining methodology to specifically include data understanding and quality assessment. We illustrate our approach in a case study describing the detailed preparation for a process mining analysis of ground and aero-medical pre-hospital transport processes involving the Queensland Ambulance Service (QAS) and Retrieval Services Queensland (RSQ). We utilise QAS and RSQ sample data to show how the use of data models and some quality metrics can be used to (i) identify data quality issues, (ii) anticipate and explain certain observable features in process mining analyses, (iii) distinguish between systemic and occasional quality issues, and, (iv) reason about the mechanisms by which identified quality issues may have arisen in the event log. We contend that this knowledge can be used to guide the extraction, pre-processing stages of a process mining case study.


Process mining Data quality Pre-hospital GEMS HEMS 



The work in this paper was funded from a grant from the Queensland Motor Accident Insurance Commission (MAIC).


  1. 1.
    van der Aalst, W.M.: Extracting event data from databases to unleash process mining. In: vom Brocke, J., Schmiedel, T. (eds.) BPM - Driving Innovation in a Digital World. MP, pp. 105–128. Springer, Cham (2015). Scholar
  2. 2.
    Andrews, R., Suriadi, S., Wynn, M., ter Hofstede, A.H.: Healthcare process analysis. In: Process Modelling and Management for HealthCare. CRC Press (2017)Google Scholar
  3. 3.
    Badakhshan, P., Alibabaei, A.: Using process mining for process analysis improvement in pre-hospital emergency. In: Middle East North Africa Conference for Information Systems, Paris, March 2018 (2018)Google Scholar
  4. 4.
    Batini, C., Cappiello, C., et al.: Methodologies for data quality assessment and improvement. ACM Comput. Surv. (CSUR) 41(3), 16 (2009)CrossRefGoogle Scholar
  5. 5.
    Bose, R.J.C., Mans, R.S., van der Aalst, W.M.: Wanna improve process mining results? In: CIDM 2013, pp. 127–134 (2013)Google Scholar
  6. 6.
    van Eck, M.L., Lu, X., Leemans, S.J.J., van der Aalst, W.M.P.: PM\(^2\): a process mining project methodology. In: Zdravkovic, J., Kirikova, M., Johannesson, P. (eds.) CAiSE 2015. LNCS, vol. 9097, pp. 297–313. Springer, Cham (2015). Scholar
  7. 7.
    Halpin, T., Morgan, T.: Information Modeling and Relational Databases. Morgan Kaufmann, San Francisco (2010)Google Scholar
  8. 8.
    Lamine, E., Fontanili, F., Di Mascolo, M., Pingaud, H.: Improving the management of an emergency call service by combining process mining and discrete event simulation approaches. In: Camarinha-Matos, L.M., Bénaben, F., Picard, W. (eds.) PRO-VE 2015. IAICT, vol. 463, pp. 535–546. Springer, Cham (2015). Scholar
  9. 9.
    Mans, R.S., van der Aalst, W.M.P., Vanwersch, R.J.B., Moleman, A.J.: Process mining in healthcare: data challenges when answering frequently posed questions. In: Lenz, R., Miksch, S., Peleg, M., Reichert, M., Riaño, D., ten Teije, A. (eds.) KR4HC/ProHealth 2012. LNCS (LNAI), vol. 7738, pp. 140–153. Springer, Heidelberg (2013). Scholar
  10. 10.
    Rebuge, Á., Ferreira, D.R.: Business process analysis in healthcare environments: a methodology based on process mining. Inf. Syst. 37(2), 99–116 (2012)CrossRefGoogle Scholar
  11. 11.
    Rojas, E., Munoz-Gama, J., Sepúlveda, M., Capurro, D.: Process mining in healthcare: a literature review. J. Biomed. Inform. 61, 224–236 (2016)CrossRefGoogle Scholar
  12. 12.
    Suriadi, S., Andrews, R., ter Hofstede, A., Wynn, M.: Event log imperfection patterns for process mining: towards a systematic approach to cleaning event logs. Inf. Syst. 64, 132–150 (2017)CrossRefGoogle Scholar
  13. 13.
    Wand, Y., Wang, R.: Anchoring data quality dimensions in ontological foundations. Commun. ACM 39(11), 86–95 (1996)CrossRefGoogle Scholar
  14. 14.
    Wang, R.Y., Strong, D.M.: Beyond accuracy: what data quality means to data consumers. J. Manag. Inf. Syst. 12(4), 5–33 (1996)CrossRefGoogle Scholar
  15. 15.
    Wirth, R., Hipp, J.: CRISP-DM: towards a standard process model for data mining. In: PAKDDM, pp. 29–39 (2000)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Robert Andrews
    • 1
    Email author
  • Moe T. Wynn
    • 1
  • Kirsten Vallmuur
    • 1
  • Arthur H. M. ter Hofstede
    • 1
  • Emma Bosley
    • 2
  • Mark Elcock
    • 3
  • Stephen Rashford
    • 2
  1. 1.Queensland University of Technology (QUT)BrisbaneAustralia
  2. 2.Queensland Ambulance Service (QAS)BrisbaneAustralia
  3. 3.Retrieval Services Queensland (RSQ)BrisbaneAustralia

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