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...
| Publicado en: | Journal of Medical Systems Vol. 44; no. 1; pp. 1 - 13 |
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| Autores principales: | , , , |
| Formato: | equations & formulas tables/charts Journal Article |
| Publicado: |
Springer Nature
Jan2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=141026234&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141026234 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Jan2020 vid: 44 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 141026234 141026234 141026234 10.1007/s10916-019-1473-4 141026234 ppf: 1 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Private Hospital Workflow Optimization via Secure k-Means Clustering. aug: au: Spini, Gabriele van Heesch, Maran Veugen, Thijs Chatterjea, Supriyo affil: Unit ICT, TNO, The Hague, The Netherlands sug: subj: Hospitals, Private Administration Computer-Assisted Instruction Workflow Diffusion of Innovation Labor Unions Privacy and Confidentiality Data Security Geographic Factors Algorithms Computer Simulation ab: 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. pubtype: Academic Journal doctype: equations & formulas tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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