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- Publisher Website: 10.1109/BigDataCongress.2015.52
- Scopus: eid_2-s2.0-84959498729
- WOS: WOS:000380443700042
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Conference Paper: Embracing Big Data for Simulation Modelling of Emergency Department Processes and Activities
Title | Embracing Big Data for Simulation Modelling of Emergency Department Processes and Activities |
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Authors | |
Keywords | big data simulation modelling emergency department RFID |
Issue Date | 2015 |
Citation | Proceedings - 2015 IEEE International Congress on Big Data, BigData Congress 2015, 2015, p. 313-316 How to Cite? |
Abstract | © 2015 IEEE. Simulation has been demonstrated to be a powerful tool to mimic processes and activities in emergency departments. However, most applications only rely on the data that were manually input by the staff in the departments. First, this practice does not guarantee that the required data to build the simulation models are captured in the computer system, as some information about the processes of emergency departments are not electronically stored. Second, human errors and missing data are also common for manual inputs. A simulation model that is incapable of representing the actual system of the emergency department will deliver wrong conclusions to hospital administrators and may lead to negative consequences if they trust the simulation results. In this paper, we present a case study of developing a simulation model of an emergency department in Hong Kong and discuss the data challenges. Then we propose an RFID-enabled infrastructure to automatically capture large volumes of data regarding the patient activities in the ED in order to build simulation models of more details and a higher accuracy. |
Persistent Identifier | http://hdl.handle.net/10722/246831 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Kuo, Yong Hong | - |
dc.contributor.author | Leung, Janny M.Y. | - |
dc.contributor.author | Tsoi, Kelvin K.F. | - |
dc.contributor.author | Meng, Helen M. | - |
dc.contributor.author | Graham, Colin A. | - |
dc.date.accessioned | 2017-09-26T04:28:07Z | - |
dc.date.available | 2017-09-26T04:28:07Z | - |
dc.date.issued | 2015 | - |
dc.identifier.citation | Proceedings - 2015 IEEE International Congress on Big Data, BigData Congress 2015, 2015, p. 313-316 | - |
dc.identifier.uri | http://hdl.handle.net/10722/246831 | - |
dc.description.abstract | © 2015 IEEE. Simulation has been demonstrated to be a powerful tool to mimic processes and activities in emergency departments. However, most applications only rely on the data that were manually input by the staff in the departments. First, this practice does not guarantee that the required data to build the simulation models are captured in the computer system, as some information about the processes of emergency departments are not electronically stored. Second, human errors and missing data are also common for manual inputs. A simulation model that is incapable of representing the actual system of the emergency department will deliver wrong conclusions to hospital administrators and may lead to negative consequences if they trust the simulation results. In this paper, we present a case study of developing a simulation model of an emergency department in Hong Kong and discuss the data challenges. Then we propose an RFID-enabled infrastructure to automatically capture large volumes of data regarding the patient activities in the ED in order to build simulation models of more details and a higher accuracy. | - |
dc.language | eng | - |
dc.relation.ispartof | Proceedings - 2015 IEEE International Congress on Big Data, BigData Congress 2015 | - |
dc.subject | big data | - |
dc.subject | simulation modelling | - |
dc.subject | emergency department | - |
dc.subject | RFID | - |
dc.title | Embracing Big Data for Simulation Modelling of Emergency Department Processes and Activities | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/BigDataCongress.2015.52 | - |
dc.identifier.scopus | eid_2-s2.0-84959498729 | - |
dc.identifier.spage | 313 | - |
dc.identifier.epage | 316 | - |
dc.identifier.isi | WOS:000380443700042 | - |