Combining Business Process and Data Discovery Techniques for Analyzing and Improving Integrated Care Pathways

Abstract

Abstract. Hospitals increasingly use process models for structuring their care processes. Activities performed to patients are logged to a database but these data are rarely used for managing and improving the efficiency of care processes and quality of care. In this paper, we propose a synergy of process mining with data discovery techniques. In particular, we analyze a dataset consisting of the activities performed to 148 patients during hospitalization for breast cancer treatment in a hospital in Belgium. We expose multiple quality of care issues that will be resolved in the near future, discover process variations and best practices and we discover issues with the data registration system. For example, 25 % of patients receiving breast-conserving therapy did not receive the key intervention "revalidation''. We found this was caused by lowering the length of stay in the hospital over the years without modifying the care process. Whereas the process representations offered by Hidden Markov Models are easier to use than those offered by Formal Concept Analysis, this data discovery technique has proven to be very useful for analyzing process anomalies and exceptions in detail.

Process mining in oncology: A literature review

“…Declarative process mining involves identifying possible execution paths for bridging event data reflecting the clinical reality and clinical guidelines describing best-practice. Two papers concerned the analysis of process anomalies and exceptions [33], [34], and some others concerned common pathways [35]- [38]. Problems and challenges of process characteristic issues and event log quality issues were well discussed in three papers [39]- [41].…”

“…Data limitations were identified in ten papers [20], [22], [33], [36], [37], [44], [48], [57], [60] and were mostly related to limited access to the data, data quality problems, attributes not available in the data being extracted, or the dataset available in inappropriate level of details. Technique limitations were identified in 13 papers [3], [21], [28], [31], [35], [38]- [40], [48], [50], [52], [63], [64] specifically if the study undertaken was done by implementing plug-ins/ functionalities available in tools.…”

Process Mining in Healthcare: Data Challenges When Answering Frequently Posed Questions

“…In total, 12 scholarly publications have been identified in which an application of process mining in healthcare was described. That is, in [12,16] the gynaecological oncology healthcare process within an university hospital has been analyzed; in [17] several processes within an emergency department have been investigated; in [9] all Computer Tomography (CT), Magnetic Resonance Imaging (MRI), ultrasound, and X-ray appointments within a radiology workflow have been analyzed; in [21] the focus is upon the process that is followed by patients su↵ering from rheumatoid arthritis; in [6] the treatment of patients within an intensive care unit has been investigated; in [6,11] process mining has been applied to di↵erent datasets for stroke patients; in [14] the activities that are performed for patients during hospitalization for breast cancer treatment are investigated; in [13] the journey through multiple wards has been discovered for inpatients; in [18] the processes for mamma care patients and diabetes foot patients has been investigated, in [2], the workflow of a laparascopic surgery has been analyzed, and finally in [5] process mining has been applied to the event logs of more than thousand X-ray machines all over the world.…”

Concept Relation Discovery and Innovation Enabling Technology (Cordiet)

“…Traditional FCA is mainly using data attributes for concept analysis. We also used process activities (events) as attributes (Poelmans et al 2010c). Typically, coherent data attributes were clustered to reduce the computational complexity of FCA.…”