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dc.contributor.authorMustafee, N
dc.contributor.authorPowell, JH
dc.contributor.authorHarper, A
dc.date.accessioned2019-01-07T09:56:22Z
dc.date.issued2019-02-04
dc.description.abstractRight Hospital – Right Time (RH-RT) is the conceptualization of the use of descriptive, predictive and prescriptive analytics with real-time data from Accident & Emergency (A&E)/Emergency Departments (ED) and centers for urgent care; its objective is to derive maximum value from wait time data by using data analytics techniques, and making them available to both patients and healthcare organizations. The paper presents an architecture for the implementation of RH-RT that is specific to the authors’ current work on a digital platform (NHSquicker) that makes available live waiting time from multiple centers of urgent care (e.g., A&E/ED, Minor Injury Units) in Devon and Cornwall. The focus of the paper is on the development of a Hybrid Systems Model (HSM) comprising of healthcare business intelligence, forecasting techniques and computer simulation. The contribution of the work is the conceptual RH-RT framework and its implementation architecture that relies on near real-time data from NHSquicker.en_GB
dc.description.sponsorshipTorbay Medical Research Funden_GB
dc.description.sponsorshipEconomic and Social Research Council (ESRC)en_GB
dc.description.sponsorshipTorbay Medical Research Funden_GB
dc.description.sponsorshipAcademic Health Science Networken_GB
dc.identifier.citation2018 Winter Simulation Conference, 9-12 December 2018, Gothenburg, Swedenen_GB
dc.identifier.doi10.1109/WSC.2018.8632378
dc.identifier.urihttp://hdl.handle.net/10871/35361
dc.language.isoenen_GB
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_GB
dc.rights© 2018 IEEE
dc.subjectReal-time systems
dc.subjectData analysis
dc.subjectPredictive models
dc.subjectAnalytical models
dc.subjectHospitals
dc.subjectForecasting
dc.titleRH-RT: A Data Analytics Framework for Reducing Wait Time at Emergency Departments and Centres for Urgent Careen_GB
dc.typeConference proceedingsen_GB
dc.date.available2019-01-07T09:56:22Z
dc.contributor.editorRabe, Men_GB
dc.contributor.editorJuan, AAen_GB
dc.contributor.editorMustafee, Nen_GB
dc.contributor.editorSkoogh, Aen_GB
dc.contributor.editorJain, Sen_GB
dc.contributor.editorJohansson, Ben_GB
dc.descriptionThis is the author accepted manuscript. The final version is available from IEEE via the DOI in this recorden_GB
dc.identifier.eissn1558-4305
dc.rights.urihttp://www.rioxx.net/licenses/all-rights-reserveden_GB
dcterms.dateAccepted2018-07-15
exeter.funder::Torbay Medical Research Funden_GB
exeter.funder::Economic and Social Research Council (ESRC)en_GB
exeter.funder::Torbay Medical Research Funden_GB
exeter.funder::Academic Health Science Networken_GB
rioxxterms.versionAMen_GB
rioxxterms.licenseref.startdate2018-07-15
rioxxterms.typeConference Paper/Proceeding/Abstracten_GB
refterms.dateFCD2019-01-06T17:16:52Z
refterms.versionFCDAM
refterms.dateFOA2019-05-15T10:59:14Z
refterms.panelCen_GB


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