Analyzing Healthcare Processes with Incremental Process Discovery: Practical Insights from a Real-World Application.

Abstract: Most process mining techniques are primarily automated, meaning that process analysts input information and receive output. As a result, process mining techniques function like black boxes with limited interaction options for analysts, such as simple sliders for filtering infrequent behavior. Recent research tries to break these black boxes by allowing process analysts to provide domain knowledge and guidance to process mining techniques, i.e., hybrid intelligence. Especially, in process discovery-a critical type of process mining-interactive approaches emerged. However, little research has investigated the practical application of such interactive approaches. This paper presents a case study focusing on using incremental and interactive process discovery techniques in the healthcare domain. Though healthcare presents unique challenges, such as high process execution variability and poor data quality, our case study demonstrates that an interactive process mining approach can effectively address these challenges.

How Can Interactive Process Discovery Address Data Quality Issues in Real Business Settings? Evidence from a Case Study in Healthcare.

The focus of this paper is on how data quality can affect business process discovery in real complex environments, which is a major factor determining the success in any data-driven Business Process Management project. Many real-life event logs, especially healthcare ones, can suffer from several data quality issues, some of which cannot be solved by pre-processing or data cleaning techniques, leading to inaccurate results. We take an innovative Process Mining (PM) approach, termed Interactive Process Discovery (IPD), which combines domain knowledge with available data. This approach can overcome the limitations of noisy and incomplete event logs by putting "humans in the loop", leading to improved business process modelling. This is particularly valuable in healthcare, where physicians have a tacit domain knowledge not available in the event log, and, thus, difficult to elicit. We conducted a two-step approach based on a controlled experiment and a case study in an Italian hospital. At each step, we compared IPD with traditional PM techniques to assess the extent to which domain knowledge helps to improve the accuracy of process models. The case study tests the effectiveness of IPD to uncover knowledge-intensive processes extracted from noisy real-life event logs. The evaluation has been carried out by exploiting a real dataset of an Italian hospital, involving the medical staff. IPD can produce an accurate process model that is fully compliant with the clinical guidelines by addressing data quality issues. Accurate and reliable process models can support healthcare organizations in detecting process-related issues and in taking decisions related to capacity planning and process re-design.

Process mining for healthcare: Characteristics and challenges.

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.

Why enterprise resource planning initiatives do succeed in the long run: A case-based causal network.

Despite remarkable academic efforts, why Enterprise Resource Planning (ERP) post-implementation success occurs still remains elusive. A reason for this shortage may be the insufficient addressing of an ERP-specific interior boundary condition, i.e., the multi-stakeholder perspective, in explaining this phenomenon. This issue may entail a gap between how ERP success is supposed to occur and how ERP success may actually occur, leading to theoretical inconsistency when investigating its causal roots. Through a case-based, inductive approach, this manuscript presents an ERP success causal network that embeds the overlooked boundary condition and offers a theoretical explanation of why the most relevant observed causal relationships may occur. The results provide a deeper understanding of the ERP success causal mechanisms and informative managerial suggestions to steer ERP initiatives towards long-haul success.