The ClearPath Method for Care Pathway Process Mining and Simulation

  • Owen A. JohnsonEmail author
  • Thamer Ba Dhafari
  • Angelina Kurniati
  • Frank Fox
  • Eric Rojas
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 342)


Process mining of routine electronic healthcare records can help inform the management of care pathways. Combining process mining with simulation creates a rich set of tools for care pathway improvement. Healthcare process mining creates insight into the reality of patients’ journeys through care pathways while healthcare process simulation can help communicate those insights and explore “what if” options for improvement. In this paper, we outline the ClearPath method, which extends the PM2 process mining method with a process simulation approach that address issues of poor quality and missing data and supports rich stakeholder engagement. We review the literature that informed the development of ClearPath and illustrate the method with case studies of pathways for alcohol-related illness, giant-cell arteritis and functional neurological symptoms. We designed an evidence template that we use to underpin the fidelity of our simulation models by tracing each model element back to literature sources, data and process mining outputs and insights from qualitative research. Our approach may be of benefit to others using process-oriented data science to improve healthcare.


Healthcare Care pathways Process mining Process simulation 



This work was supported by the cYorkshire Connected Health Cities (CHC) project. The case studies were developed by Luke Naylor, Sahar Salimi Avval Bejestani, Clea Southall and Samantha Haley at the University of Leeds. Case study 1 was supported by Anna Jenkins and the University of Liverpool CHC. Case study 2 was supported by Prof Ann Morgan and the TARGET Consortium for GCA. Case study 3 was supported by Dr Stefan Williams. The third author would also like to thank the Indonesia Endowment Fund for Education (LPDP) for the support given during this research.


  1. 1.
    Vanhaecht, K., Panella, M., Van Zelm, R.: An overview on the history and concept of care pathways as complex interventions. Int. J. Care Pathways 14(3), 117–123 (2010)CrossRefGoogle Scholar
  2. 2.
    European Pathway Association, “Care Pathways”. Accessed 30 May 2018
  3. 3.
    Mans, R.S., van der Aalst, W.M.P., Vanwersch, R.J.B.: Process Mining in Healthcare: Evaluating and Exploiting Operational Healthcare Processes. Springer, Heidelberg (2015). Scholar
  4. 4.
    Weiskopf, N.G., Bakken, S., Hripcsak, G., Weng, C.: A data quality assessment guideline for electronic health record data reuse. eGems (Generating Evid. Methods to Improv. Patient Outcomes) 5(1), 14 (2017)CrossRefGoogle Scholar
  5. 5.
    Johnson, O., Hall, P.S., Hulme, C.: NETIMIS: dynamic simulation of health economics outcomes using big data. PharmacoEconomics 34(2), 107–114 (2015)CrossRefGoogle Scholar
  6. 6.
    Mans, R., Reijers, H., Wismeijer, D., Van Genuchten, M.: A process-oriented methodology for evaluating the impact of IT: a proposal and an application in healthcare. Inf. Syst. 38(8), 1097–1115 (2013)CrossRefGoogle Scholar
  7. 7.
    Rojas, E., Munoz-Gama, J.: Process mining in healthcare: a literature review. J. Biomed. Inform. 61, 224–236 (2016)CrossRefGoogle Scholar
  8. 8.
    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
  9. 9.
    van der Aalst, W.: Data science in action. Process Mining, pp. 3–23. Springer, Heidelberg (2016). Scholar
  10. 10.
    van der Aalst, W.M.P., et al.: Process mining manifesto. Bus. Process Manag. Work. 99, 169–194 (2011)CrossRefGoogle Scholar
  11. 11.
    van Eck, M.L., Lu, X., Leemans, S.J.J., van der Aalst, W.M.P.: PM2: 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
  12. 12.
    Bozkaya, M., Gabriels, J., van der Werf, J.M.: Process diagnostics: a method based on process mining. In: International Conference on Information, Process, and Knowledge Management, eKNOW 2009, pp. 22–27. IEEE, February 2009Google Scholar
  13. 13.
    Rebuge, A., Ferreira, D.: Business process analysis in healthcare environments: a methodology based on process mining. Inf. Syst. 37, 99–116 (2012)CrossRefGoogle Scholar
  14. 14.
    Rojas, E., Sepúlveda, M., Munoz-Gama, J., Capurro, D., Traver, V., Fernandez-Llatas, C., et al.: Question-driven methodology for analyzing emergency room processes using process mining. Appl. Sci. 7(3), 302 (2017)CrossRefGoogle Scholar
  15. 15.
    van der Aalst, W.M.P.: Business process simulation revisited. Enterp. Organ. Model. Simul. 63, 1–14 (2010)Google Scholar
  16. 16.
    Rozinat, A., Mans, R.S., Song, M., van der Aalst, W.M.P.: Discovering simulation models. Inf. Syst. 34, 305–327 (2009)CrossRefGoogle Scholar
  17. 17.
    Brailsford, S., Carter, M.W., Jacobson, S.H.: Five decades of healthcare simulation. In: Winter Simulation Conference, vol. 9, no. 62, pp. 365–384 (2017)Google Scholar
  18. 18.
    Marshall, D.A., et al.: Selecting a dynamic simulation modeling method for health care delivery research - part 2: report of the ISPOR dynamic simulation modeling emerging good practices task force. Value Health 18(2), 147–160 (2015)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Weiskopf, N.G., Weng, C.: Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. JAMIA 20(1), 144–151 (2013)Google Scholar
  20. 20.
    Kahn, M.G., et al.: A harmonized data quality assessment terminology and framework for the secondary use of electronic health record data. EGEMS 4(1), 1244 (2016)CrossRefGoogle Scholar
  21. 21.
    Fox, F., Aggarwal, V., Whelton, H., Johnson, O.: A data quality framework for process mining of electronic health record data. In: Proceedings of the Sixth IEEE ICHI (2018)Google Scholar
  22. 22.
    Lekharaju, P., Thompson, E., Shawihdi, M., Pearson, M., Hood, S., Bodger, K., et al.: PTH-062 emergency admissions for alcohol related conditions: making sense of routine data. Gut 63(Suppl 1), A236–A236 (2014)CrossRefGoogle Scholar
  23. 23.
    Laskou, F., Fiona, C., Aung, T., Benerjee, S., Dasgupta, B.: 074 Fast track giant cell arteritis clinic and pathway for early management of suspected giant cell arteritis: an audit. Rheumatology 57(suppl_3), key075–298 (2018)Google Scholar
  24. 24.
    Mobini, S.: Psychology of medically unexplained symptoms: a practical review. Cogent Psychol. 2 (2015). Article no. 1033876Google Scholar
  25. 25.
    Stone, J., Carson, A., et al.: Symptoms ‘unexplained by organic disease’ in 1144 new neurology out-patients. Brain 132, 2878–2888 (2009)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Owen A. Johnson
    • 1
    • 2
    Email author
  • Thamer Ba Dhafari
    • 1
  • Angelina Kurniati
    • 1
    • 3
  • Frank Fox
    • 1
  • Eric Rojas
    • 4
  1. 1.University of LeedsLeedsUK
  2. 2.X-Lab Ltd.LeedsUK
  3. 3.Telkom UniversityBandungIndonesia
  4. 4.Pontificia Universidad Católica de ChileSantiagoChile

Personalised recommendations