Private Hospital Workflow Optimization via Secure k-Means Clustering

Gabriele Spini*, Maran van Heesch, Thijs Veugen, Supriyo Chatterjea

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

7 Citations (Scopus)
4 Downloads (Pure)


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.

Original languageEnglish
Article number8
JournalJournal of medical systems
Issue number1
Publication statusPublished - 1 Jan 2020
Externally publishedYes


  • Clustering
  • Hospital
  • k-means
  • Privacy
  • Real-time locating system
  • Secure multi-party computation
  • Workflow optimization


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