Data-Driven Patient Segmentation Using K-Means Clustering: The Case Of Hip Fracture Care In Ireland

Presented at 10th ACM Workshop on Health Informatics and Knowledge Management (HIKM), 2017SummaryThe paper embraces a mere data-driven approach for the segmentation of patients with application to hip fracture care in Ireland. Using K-Means clustering, elderly patients are grouped based on the similarity of age, length of stay (LOS) and elapsed time to surgery. We utilise a dataset retrieved from the Irish Hip fracture Database (IHFD) covering the period of two years (2013–2014). Our results suggest the presence of three coherent clusters of patients. Through cluster analysis, possible correlations are explored in relation to patient characteristics, care-related factors, and patient outcomes.