A Comparative Process Mining Analysis of Road Trauma Patient Pathways

Abstract

In this paper we report on key findings and lessons from a process mining case study conducted to analyse transport pathways discovered across the time-critical phase of pre-hospital care for persons involved in road traffic crashes in Queensland (Australia). In this study, a case is defined as being an individual patient’s journey from roadside to definitive care. We describe challenges in constructing an event log from source data provided by emergency services and hospitals, including record linkage (no standard patient identifier), and constructing a unified view of response, retrieval, transport and pre-hospital care from interleaving processes of the individual service providers. We analyse three separate cohorts of patients according to their degree of interaction with Queensland Health’s hospital system (C1: no transport required, C2: transported but no Queensland Health hospital, C3: transported and hospitalisation). Variant analysis and subsequent process modelling show high levels of variance in each cohort resulting from a combination of data collection, data linkage and actual differences in process execution. For Cohort 3, automated process modelling generated ’spaghetti’ models. Expert-guided editing resulted in readable models with acceptable fitness, which were used for process analysis. We also conduct a comparative performance analysis of transport segment based on hospital ‘remoteness’. With regard to the field of process mining, we reach various conclusions including (i) in a complex domain, the current crop of automated process algorithms do not generate readable models, however, (ii) such models provide a starting point for expert-guided editing of models (where the tool allows) which can yield models that have acceptable quality and are readable by domain experts, (iii) process improvement opportunities were largely suggested by domain experts (after reviewing analysis results) rather than being directly derived by process mining tools, meaning that the field needs to become more prescriptive (automated derivation of improvement opportunities).

Opportunities and Challenges for Process Mining in Organizations: Results of a Delphi Study

“…Moreover, domain expertise is deemed indispensable, both to obtain readable outcomes and to identify improvement options (Andrews et al 2020). Similar to Andrews et al (2020), Mahendrawathi et al (2015) also observe that the composition of an event log was far from trivial. By studying the customer fulfillment process at a telecommunication company, they especially highlight the challenge of integrating data which is dispersed over different systems (Mahendrawathi et al 2015).…”

“…Due to the profound analysis of the specific organizational setting, case studies can deliver rich insights regarding the opportunities and challenges of PM within this context. For instance, from an analysis of the patient transportation process after traffic accidents in Queensland, Andrews et al (2020) conclude that the composition of the event log was very challenging and that PM algorithms could not always handle the process complexity. Moreover, domain expertise is deemed indispensable, both to obtain readable outcomes and to identify improvement options (Andrews et al 2020).…”

“…For instance, from an analysis of the patient transportation process after traffic accidents in Queensland, Andrews et al (2020) conclude that the composition of the event log was very challenging and that PM algorithms could not always handle the process complexity. Moreover, domain expertise is deemed indispensable, both to obtain readable outcomes and to identify improvement options (Andrews et al 2020). Similar to Andrews et al (2020), Mahendrawathi et al (2015) also observe that the composition of an event log was far from trivial.…”

“…Recently, research attention has been devoted to topics such as predictive process monitoring (e.g., Kratsch et al 2020;Teinemaa et al 2019), data quality (e.g., Fischer et al 2020), and algorithms for specific domains such as healthcare (e.g., Chiudinelli et al 2020). While prior research has mainly focused on the development and improvement of PM algorithms, case studies reporting on the application of PM have also been published (e.g., Andrews et al 2020). These case studies typically provide rich insights into the use of one or more PM techniques in specific organizational contexts and draw lessons regarding points of attention for the use of PM in the organization (e.g., Reinkemeyer 2020).…”

Process mining applications in the healthcare domain: A comprehensive review

“…In fact, from the studies treated so far it emerged that the existence of local resource constraints can lead to differences between the models implemented in different hospitals, even when referring to the treatment of the same disease (and to the same guideline). In (Andrews et al, 2020), comparison and similarity of processes for different patient populations in the domain of breast cancer has been assessed in three different ways: (i) visual inspection, that is, human judgment, (ii) similarity measures on directed graphs extracted from the process model, and (iii) cross-log conformance checking by means the plug-in "Replay a Log on Petri Net for Conformance Analysis. "…”