Mining trauma care flows of patient cohorts

Abstract

BackgroundAccurate assessment of trauma in the least time and efficient and effective treatment is gaining momentum in traumatology. Mapping the real-world practice patterns is essential in identifying and improving the quality of care for emergent time-dependent medical states like trauma. MethodsThe data mining solutions are extended to the National Trauma Registry of Iran (NTRI) event data by incorporating process mining techniques to ease the analysis, of the associations between clinical pathways and patient cohorts in understanding their performance. A total of 4498 cases, 44,344 events, and 104 different activities within the years 2017–2021 constitute the statistical data. Based on clinically relevant attributes and derived process characteristics the K-means clustering is applied to cohorts followed by comparing the clustering results and treatment pathways. ResultsThe attributes influence treatment patterns in trauma care flows with the possibility of explaining the variations within cohorts' results. Although these attributes are not involved in the clustering algorithm, there exist meaningful correlations among the cohorts’ members in terms of type (final diagnostics) of injury, Injury Severity Score (minor: 1 < ISS<8; moderate: 9 < ISS<15; sever: 16 < ISS<24), Hospital Length of Stay (HLOS), and treatment activities. ConclusionOur findings provide more details on the existing process mining techniques and allow easy assessment of the quality of care at a given institution. This approach is an essential data analysis stage to improve complex care processes by proportioning the patient records into closely related groups applicable in target process-aware recommendation initiatives.