Faculty, Staff and Student Publications
Publication Date
9-13-2023
Journal
Healthcare
Abstract
Mining process models of medical behavior from electronic medical records is an effective way to optimize clinical pathways. However, clinical medical behavior is an extremely complex field with high nonlinearity and variability, and thus we need to adopt a more effective method. In this study, we developed a fuzzy process mining method for complex clinical pathways. Firstly, we designed a multi-level expert classification system with fuzzy values to preserve finer details. Secondly, we categorized medical events into long-term and temporary events for more specific data processing. Subsequently, we utilized electronic medical record (EMR) data of acute pancreatitis spanning 9 years, collected from a large general hospital in China, to evaluate the effectiveness of our method. The results demonstrated that our modeling process was simple and understandable, allowing for a more comprehensive representation of medical intricacies. Moreover, our method exhibited high patient coverage (>0.94) and discrimination (>0.838). These findings were corroborated by clinicians, affirming the accuracy and effectiveness of our approach.
Keywords
healthcare, process mining, clinical pathway, acute pancreatitis, electronic medical record
DOI
10.3390/healthcare11182529
PMID
37761726
PMCID
PMC10531471
PubMedCentral® Posted Date
9-13-2023
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
