The ClearPath Method for Care Pathway Process Mining and Simulation
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.
KeywordsHealthcare 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.
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