Discovering health-care processes using DeciClareMiner

Abstract

Flexible, human-centric and knowledge-intensive processes occur in many service industries and are prominent in the health-care sector. Knowledge workers (e.g., doctors or other health-care personnel) are given the flexibility to address each process instance (i.e., episode of care) in the way that they deem most suitable. As a result, the knowledge of these processes is generally of a tacit nature, with many stakeholders lacking a clear view of a process. In this paper, we propose an algorithm called DeciClareMiner that combines process and decision mining to extract a process model and the corresponding knowledge from past executions of these processes. The algorithm was evaluated by applying it to a realistic health-care case and comparing the results to a complete search benchmark. In a relatively short time (10 min), DeciClareMiner was able to produce a DeciClare model that represents 93% of episodes of care with atomic constraints. Compared to the 50 h required to calculate the 100%-episode model via an exhaustive search approach, our result is considered a major improvement.

Enabling Process Management for Loosely Framed Knowledge-intensive Processes

“…This paper reports on the first two stages of an Action Design Research (ADR) cycle that is part of a design science project to develop a process-oriented system that manages and supports loosely framed knowledge-intensive processes (Mertens et al 2017(Mertens et al , 2018Mertens, Gailly, Van Sassenbroeck, et al 2019). ADR is a specific type of design science research The previous section discussed the problem statement: the lack of a process mining methodology for declarative process and decision mining techniques.…”

“…This paper reports on an Action Design Research (ADR) cycle that is part of a design science project to develop a process-oriented system that manages and supports loosely framed knowledgeintensive processes (Mertens et al 2017(Mertens et al , 2018Mertens, Gailly, Van Sassenbroeck, et al 2019). The research artifact of this paper is a method for process and decision discovery of loosely framed knowledge-intensive processes.…”

“…The research artifact of this paper is a method for process and decision discovery of loosely framed knowledge-intensive processes. This method consists of the preparation of process data to serve as input for a decision-aware declarative process mining tool called DeciClareMiner (Mertens et al 2018), the actual use of this tool and the feedback loops that are necessary to obtain a satisfactory model of a given process. The method was iteratively improved and validated by applying it to a practical problem.…”

Personalized Process and Decision Support in Emergency Care by Mining Electronic Health Records and Sensor Data

“…Data about the process can be fed to a process mining tool, which tries to automatically detect patterns in the data. DeciClareMiner was developed to do just that for the DeciClare modeling language [14]. It searches for patterns in the activity flows and the data contexts that can be reconstructed from the available electronic health records and sensor data of previously treated patients.…”

Recommendations for enhancing the usability and understandability of process mining in healthcare

“…Despite the predominant research focus on controlflow, some methods also allow for mining, for instance, resource-related models such as social networks [25,26], resource profiles [27], resource roles [28,29] and work prioritization patterns [30]. Another example involves the integrated discovery of decision and control-flow models, a technique which has been developed in the context of the clinical process at the emergency department [31].…”