dc.contributor.author | Kargar, ZS | |
dc.contributor.author | Khanna, S | |
dc.contributor.author | Sattar, A | |
dc.contributor.editor | Khanna,S., Sattar,A., Hansen, D. | |
dc.date.accessioned | 2017-10-11T12:30:36Z | |
dc.date.available | 2017-10-11T12:30:36Z | |
dc.date.issued | 2012 | |
dc.date.modified | 2013-07-12T01:29:58Z | |
dc.identifier.issn | 1613-0073 | |
dc.identifier.refuri | http://www.aehrc.com/centre-events/icalrepeat.detail/2012/12/04/33/-/second-australian-workshop-on-artificial-intelligence-in-health-aih-2012 | |
dc.identifier.uri | http://hdl.handle.net/10072/52402 | |
dc.description.abstract | Stochastic activity durations, uncertainty in the arrival process of patients, and coordination of multiple activities are some key features of surgery planning and scheduling. In this paper we provide an overview of challenges around elective surgery scheduling and propose a predictive model for elective surgery scheduling to be evaluated in a major tertiary hospital in Queensland. The proposed model employs waiting lists, peri-operative information, workload predictions, and improved procedure time estimation models, to optimise surgery scheduling. It is expected that the resulting improvement in scheduling processes will lead to more efficient use of surgical suites, higher productivity, and lower labour costs, and ultimately improve patient outcomes. | |
dc.description.peerreviewed | Yes | |
dc.description.publicationstatus | Yes | |
dc.format.extent | 597160 bytes | |
dc.format.mimetype | application/pdf | |
dc.language | English | |
dc.publisher | Redaktion Sun SITE, Informatik V | |
dc.publisher.place | Germany | |
dc.publisher.uri | http://ceur-ws.org/Vol-941/ | |
dc.relation.ispartofstudentpublication | N | |
dc.relation.ispartofconferencename | AIH 2012 | |
dc.relation.ispartofconferencetitle | CEUR Workshop Proceedings | |
dc.relation.ispartofdatefrom | 2012-12-04 | |
dc.relation.ispartofdateto | 2012-12-04 | |
dc.relation.ispartoflocation | Sydney, Australia | |
dc.relation.ispartofpagefrom | 83 | |
dc.relation.ispartofpageto | 87 | |
dc.relation.ispartofvolume | 941 | |
dc.rights.retention | Y | |
dc.subject.fieldofresearch | Artificial intelligence not elsewhere classified | |
dc.subject.fieldofresearchcode | 460299 | |
dc.title | Using Prediction to Improve Elective Surgery Scheduling | |
dc.type | Conference output | |
dc.type.description | E1 - Conferences | |
dc.type.code | E - Conference Publications | |
gro.faculty | Griffith Sciences, School of Information and Communication Technology | |
gro.rights.copyright | © The Editors 2012. The attached file is reproduced here in accordance with the copyright policy of the publisher. For information about this conference please refer to the conference’s website or contact the authors. | |
gro.date.issued | 2012 | |
gro.hasfulltext | Full Text | |
gro.griffith.author | Sattar, Abdul | |