Applications and Challenges of Task Mining: A Literature Review

Task mining is a technological innovation that combines current developments in process mining and data mining. Using task mining, the interactions of workers with their workstations can be recorded, processed, and linked with the business data of the organization. The approach can provide a holistic picture of the business processes and related tasks. Currently, there is no overview of application scenarios and the challenges of task mining. In our work, we reflect application scenarios as well as technological, legal, and organizational challenges of task mining using a structured literature review. The application areas include discovery of automation potentials, monitoring, as well as optimization of business processes. The challenges include the cleansing, collection, data protection, explainability, merging, organization, processing, and segmentation of task mining data

Measuring the impact of COVID-19 on hospital care pathways

Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospital’s new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted

Research Paper: Process Mining and Synthetic Health Data: Reflections and Lessons Learnt

Analysing the treatment pathways in real-world health data can provide valuable insight for clinicians and decision-makers. However, the procedures for acquiring real-world data for research can be restrictive, time-consuming and risks disclosing identifiable information. Synthetic data might enable representative analysis without direct access to sensitive data. In the first part of our paper, we propose an approach for grading synthetic data for process analysis based on its fidelity to relationships found in real-world data. In the second part, we apply our grading approach by assessing cancer patient pathways in a synthetic healthcare dataset (The Simulacrum provided by the English National Cancer Registration and Analysis Service) using process mining. Visualisations of the patient pathways within the synthetic data appear plausible, showing relationships between events confirmed in the underlying non-synthetic data. Data quality issues are also present within the synthetic data which reflect real-world problems and artefacts from the synthetic dataset’s creation. Process mining of synthetic data in healthcare is an emerging field with novel challenges. We conclude that researchers should be aware of the risks when extrapolating results produced from research on synthetic data to real-world scenarios and assess findings with analysts who are able to view the underlying data

Fraud detection in process aware systems

Nos últimos anos, algumas grandes empresas se envolveram em escândalos relacionados à má gestão financeira, o que representou um grande prejuízo financeiro para seus acionistas. Para recuperar a confiança do mercado, foram desenvolvidas certificações para as melhores práticas de governança e, em alguns casos, leis mais rígidas foram implementadas. As empresas aderiram a essas mudanças como uma resposta ao mercado, implantando sistemas de conhecimento de processos (PAS) e adotando as melhores práticas de governança. No entanto, as empresas demandam uma resposta rápida às mudanças estratégicas ou mudanças nos modelos de negócios entre os parceiros, o que pode impor sérios inconvenientes à adoção do PAS normativo para a competitividade dessas empresas. Assim, enquanto as empresas precisam de PAS flexível, a flexibilidade pode comprometer a segurança. Para reequilibrar o trade-off entre segurança e flexibilidade, apresentamos neste trabalho um algoritmo de detecção de anomalias para PAS. A identificação de eventos anômalos pode ajudar na adoção de PAS flexíveis sem a perda de propriedades de segurança.In the last years, some large companies have been involved in scandals related to financial mismanagement, which represented a large financial damage to their stockholders. To recover market confidence, certifications for best practices of governance were developed, and in some cases, harder laws were implemented. Companies adhered to these changes as a response to the market, deploying process aware systems (PAS) and adopting the best practices of governance. However, companies demand a rapid response to strategic changes or changes in business models between partners, which may impose serious drawbacks to the adoption of normative PAS to the competitiveness of these companies. Thus, while companies need flexible PAS, flexibility may compromise security. To re-balance the trade-off between security and flexibility, we present in this work an anomaly detection algorithm for PAS. The identification of anomalous events can help the adoption of flexible PAS without the loss of security properties

Fraud Detection In Process Aware Systems

In the last years, some large companies have been involved in scandals related to financial mismanagement, which represented a large financial damage to their stockholders. To recover market confidence, certifications for best practices of governance were developed, and in some cases, harder laws were implemented. Companies adhered to these changes as a response to the market, deploying process aware systems (PAS) and adopting the best practices of governance. However, companies demand a rapid response to strategic changes or changes in business models between partners, which may impose serious drawbacks to the adoption of normative PAS to the competitiveness of these companies. Thus, while companies need flexible PAS, flexibility may compromise security. To re-balance the trade-off between security and flexibility, we present in this work an anomaly detection algorithm for PAS. 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