dc.contributor.author | Mustafee, N | |
dc.contributor.author | Powell, JH | |
dc.contributor.author | Harper, A | |
dc.date.accessioned | 2019-01-07T09:56:22Z | |
dc.date.issued | 2019-02-04 | |
dc.description.abstract | Right 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.sponsorship | Torbay Medical Research Fund | en_GB |
dc.description.sponsorship | Economic and Social Research Council (ESRC) | en_GB |
dc.description.sponsorship | Torbay Medical Research Fund | en_GB |
dc.description.sponsorship | Academic Health Science Network | en_GB |
dc.identifier.citation | 2018 Winter Simulation Conference, 9-12 December 2018, Gothenburg, Sweden | en_GB |
dc.identifier.doi | 10.1109/WSC.2018.8632378 | |
dc.identifier.uri | http://hdl.handle.net/10871/35361 | |
dc.language.iso | en | en_GB |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_GB |
dc.rights | © 2018 IEEE | |
dc.subject | Real-time systems | |
dc.subject | Data analysis | |
dc.subject | Predictive models | |
dc.subject | Analytical models | |
dc.subject | Hospitals | |
dc.subject | Forecasting | |
dc.title | RH-RT: A Data Analytics Framework for Reducing Wait Time at Emergency Departments and Centres for Urgent Care | en_GB |
dc.type | Conference proceedings | en_GB |
dc.date.available | 2019-01-07T09:56:22Z | |
dc.contributor.editor | Rabe, M | en_GB |
dc.contributor.editor | Juan, AA | en_GB |
dc.contributor.editor | Mustafee, N | en_GB |
dc.contributor.editor | Skoogh, A | en_GB |
dc.contributor.editor | Jain, S | en_GB |
dc.contributor.editor | Johansson, B | en_GB |
dc.description | This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record | en_GB |
dc.identifier.eissn | 1558-4305 | |
dc.rights.uri | http://www.rioxx.net/licenses/all-rights-reserved | en_GB |
dcterms.dateAccepted | 2018-07-15 | |
exeter.funder | ::Torbay Medical Research Fund | en_GB |
exeter.funder | ::Economic and Social Research Council (ESRC) | en_GB |
exeter.funder | ::Torbay Medical Research Fund | en_GB |
exeter.funder | ::Academic Health Science Network | en_GB |
rioxxterms.version | AM | en_GB |
rioxxterms.licenseref.startdate | 2018-07-15 | |
rioxxterms.type | Conference Paper/Proceeding/Abstract | en_GB |
refterms.dateFCD | 2019-01-06T17:16:52Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2019-05-15T10:59:14Z | |
refterms.panel | C | en_GB |