Mining is a key industry for economic growth and industrial development. Its operations involve complex processes, and analyzing their performance is essential for identifying optimization opportunities. Process mining is a discipline that allows conducting analyses based on historical event data to discover how processes are executed. Despite its potential, process mining remains underexplored in the mining sector, where research has only focused on machine behavior using sensor data. This work aims to show the applicability of process mining for exploratory analysis of historical data from mining machinery, but from the perspective of the process where the machines are used, and how this affects process performance. Specifically, state data recorded by drilling machine operators is used to analyze their life cycle from both a high-level and a process-driven perspective. Preliminary findings reveal the underlying process structure and identify bottlenecks potentially hindering performance. This work in progress highlights the value of process mining in assessing and enhancing mining process efficiency.

Over the years, patient satisfaction has become a key factor when evaluating the quality of healthcare. There is a constant desire to further analyze patients' needs and expectations, with the aim of improving their healthcare experience. With tools such as customer journey maps (CJM), two objectives are addressed: a) identify how patients interact through the phases of the care cycle and b) execute multiple analyses to deliver a wide variety of outcomes focused on improving patient experience, through the combination of these tools with emerging disciplines such as process mining. We propose a new method based on a predefined framework to optimize the creation of healthcare indicators through process mining. Our method focuses on identifying touchpoints, defining, calculating, validating, and visualizing Key Performance Indicators (KPIs) in a clinical process. The proposed method was applied to analyze an emergency room process as a case study. Results demonstrate the usefulness of the method to discover the main process interaction points, to create key indicators, and generate dashboards that support the goal of understanding and optimizing patient care.

Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.

Prevalence of type 2 diabetes mellitus (T2DM) has almost doubled in recent decades and commonly presents comorbidities and complications. T2DM is a multisystemic disease, requiring multidisciplinary treatment provided by teams working in a coordinated and collaborative manner. The application of social network analysis techniques in the healthcare domain has allowed researchers to analyze interaction between professionals and their roles inside care teams. We studied whether the structure of care teams, modeled as complex social networks, is associated with patient progression. For this, we illustrate a data-driven methodology and use existing social network analysis metrics and metrics proposed for this research. We analyzed appointment and HbA1c blood test result data from patients treated at three primary health care centers, representing six different practices. Patients with good metabolic control during the analyzed period were treated by teams that were more interactive, collaborative and multidisciplinary, whereas patients with worsening or unstable metabolic control were treated by teams with less collaboration and more continuity breakdowns. Results from the proposed metrics were consistent with the previous literature and reveal relevant aspects of collaboration and multidisciplinarity.

Global growth in participation in higher education has helped to increase diversity of students, and traditionally underrepresented minorities on gender, income and math skills have expanded their presence in engineering education. Nevertheless, late dropout has increased and the number of engineering graduates remains low in western world. The analysis of educational trajectories using process mining techniques can help to explain the relationship between a sequence of academic results and late dropout. This case study seeks to answer how gender, income and entry math skills may explain differences on educational trajectories of engineering students in high-failure rate courses that lead to late dropout. Academic records for 794 engineering students at Universidad Austral de Chile that belongs to cohorts 2007 to 2009, were extracted and analyzed using process mining discovery techniques. Models of educational trajectories on high-failure rate courses were created and then analyzed using the Investment Model as a reference framework. Findings reveal the following: late dropout is related to the number of consecutive semesters that a student maintain pending failed courses; low-income students and those with low entry math skills tend to be more persistent, even if they have unsatisfactory trajectories; female students tend to be more risk-averse when they have unsatisfactory results. Understanding the educational trajectories of students who end in late dropout can help managers and policy makers to improve the curriculum, entry conditions and programs that support disadvantaged students.