Private hospital workflow optimization via secure k-means clustering
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.
|Keywords||Secure multi-party computation, Hospital, Workflow optimization, Privacy, Real-time locating system, Clustering, k-means|
|Stakeholder||Data Science Group, Philips Research, Eindhoven, The Netherlands|
|Journal||Journal of Medical Systems|
Spini, G, van Heesch, M, Veugen, P.J.M, & Chatterjea, S. (2019). Private hospital workflow optimization via secure k-means clustering. Journal of Medical Systems, 44(1). doi:10.1007/s10916-019-1473-4