Print Email Facebook Twitter Private Hospital Workflow Optimization via Secure k-Means Clustering Title Private Hospital Workflow Optimization via Secure k-Means Clustering Author Spini, G. van Heesch, M.P.P. Veugen, P.J.M. Chatterjea, S. Publication year 2019 Abstract Optimizing the workflow of a complex organization such as a hospital is a difficult task. An accurate option is to use a real-time locating system to track locations of both patients and staff. However, privacy regulations forbid hospital management to assess location data of their staff members. In this exploratory work, we propose a secure solution to analyze the joined location data of patients and staff, by means of an innovative cryptographic technique called Secure Multi-Party Computation, in which an additional entity that the staff members can trust, such as a labour union, takes care of the staff data. The hospital, owning location data of patients, and the labour union perform a two-party protocol, in which they securely cluster the staff members by means of the frequency of their patient facing times. We describe the secure solution in detail, and evaluate the performance of our proof-of-concept. This work thus demonstrates the feasibility of secure multi-party clustering in this setting. Subject ClusteringHospitalK-meansPrivacyReal-time locating systemSecure multi-party computationWorkflow optimizationAdultExploratory researchFeasibility studyHumanK means clusteringPrivate hospitalProof of conceptStaffTrade unionWorkflow To reference this document use: http://resolver.tudelft.nl/uuid:30ec8f93-f918-4822-9ffa-f4f3d4d453a5 TNO identifier 870498 Publisher NLM (Medline) ISSN 1573-689X Source Journal of medical systems, 44 (1) Document type article Files To receive the publication files, please send an e-mail request to TNO Library.