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Analysis of patient flow in radiation oncology department of central baptist hospital using discrete event simulations

  • Junwen Wang
  • , Jingshan Li
  • , Anne T. Campbell
  • , Kim Prather
  • , Barbara Johnson
  • , Kay Ross

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

Abstract

In this paper, we present a discrete event simulation study of patient flow in Radiation Oncology Department at Central Baptist Hospital, Lexington, KY, to improve patient satisfaction. Using the computer simulation model, we analyze the patient's length of stay and waiting times. Sensitivity analysis is introduced to identify the most critical operations, whose improvement can lead to the largest reduction of length of stay. In addition, what-if analysis is carried out to evaluate alternative improvement strategies and provide insights for operation management. It has been shown that collaborating with other departments to reduce the waiting time for surgeon or doctor from other departments in Cyberknife and Simulation/Treatment Center is the key to improve patient outcome.
Original languageEnglish
Title of host publication62nd IIE Annual Conference and Expo 2012
PublisherInstitute of Industrial Engineers
Pages1479-1488
Publication statusPublished - 2012
Externally publishedYes
Event62nd IIE Annual Conference and Expo 2012 - Orlando, FL, United States
Duration: 19 May 201223 May 2012

Publication series

Name62nd IIE Annual Conference and Expo 2012

Conference

Conference62nd IIE Annual Conference and Expo 2012
PlaceUnited States
CityOrlando, FL
Period19/05/1223/05/12

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Research Keywords

  • Discrete event simulation
  • Oncology department
  • Patient flow
  • What-if analysis

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