LEADER 02202oam  2200445zu 450 
001 9910142720403321
005 20241212215522.0
010   $a9781509086900
010   $a1509086900
035   $a(CKB)1000000000331073
035   $a(SSID)ssj0000451855
035   $a(PQKBManifestationID)12140694
035   $a(PQKBTitleCode)TC0000451855
035   $a(PQKBWorkID)10463377
035   $a(PQKB)11718263
035   $a(NjHacI)991000000000331073
035   $a(EXLCZ)991000000000331073
100   $a20160829d2007      uy 
101 0 $aeng
135   $aur|||||||||||
181   $ctxt
182   $cc
183   $acr
200 00$a20th IEEE International Symposium on Computer-Based Medical Systems (CBMS 2007): Maribor, Slovenia 20-22 June 2007
210 31$a[Place of publication not identified]$cIEEE Computer Society Press$d2007
215   $a1 online resource (750, 118 papers pages) $cillustrations
300   $aBibliographic Level Mode of Issuance: Monograph
311 08$a9780769529059 
311 08$a0769529054 
330   $aIn this paper, we present a modelling framework for patient flow in a healthcare system using semi-open queueing network models, which introduces a total bed constraint, above which new patients will be refused admission. Hence this model provides a realistic representation of a real system. This approach enables us to have access to a range of established methods that deals with queueing network models. We demonstrate the usefulness of the model in the context of a geriatric department and show that hospital managers can use this model to gain better understanding of the dynamics of patient flow and to study potential long-term impacts of policy changes.
606   $aArtificial intelligence$xMedical applications$vCongresses
606   $aMedicine$xData processing$vCongresses
615  0$aArtificial intelligence$xMedical applications
615  0$aMedicine$xData processing
676   $a610.28563
801  0$bPQKB
906   $aPROCEEDING
912   $a9910142720403321
996   $a20th IEEE International Symposium on Computer-Based Medical Systems (CBMS 2007): Maribor, Slovenia 20-22 June 2007$92415643
997   $aUNINA