Pre-hospital Retrieval and Transport of Road Trauma Patients in Queensland
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  to supplement the Planning phase of the PM\(^2\)  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.
KeywordsProcess 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).
- 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.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
- 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
- 7.Halpin, T., Morgan, T.: Information Modeling and Relational Databases. Morgan Kaufmann, San Francisco (2010)Google Scholar
- 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). https://doi.org/10.1007/978-3-319-24141-8_50CrossRefGoogle Scholar
- 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). https://doi.org/10.1007/978-3-642-36438-9_10CrossRefGoogle Scholar
- 15.Wirth, R., Hipp, J.: CRISP-DM: towards a standard process model for data mining. In: PAKDDM, pp. 29–39 (2000)Google Scholar