Private Hospital Workflow Optimization via Secure k-Means Clustering

Private Hospital Workflow Optimization via Secure k-Means Clustering

Spini, G.
van Heesch, M.P.P.
Veugen, P.J.M.
Chatterjea, S.

2019

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.

Clustering
Hospital
K-means
Privacy
Real-time locating system
Secure multi-party computation
Workflow optimization
Adult
Exploratory research
Feasibility study
Human
K means clustering
Private hospital
Proof of concept
Staff
Trade union
Workflow

http://resolver.tudelft.nl/uuid:30ec8f93-f918-4822-9ffa-f4f3d4d453a5

870498

NLM (Medline)

1573-689X

Journal of medical systems, 44 (1)

article