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A Simulation Modelling Study of Referral Distribution Policies in a Centralized Intake System for Surgical Consultation.
Marshall, Deborah A; Tagimacruz, Toni; Cepoiu-Martin, Monica; Robert, Jill; Ring, Bernice; Burston, Michael; Higgins, Suzanne; Hess, Monica; White, Jonathan.
Affiliation
  • Marshall DA; Cumming School of Medicine, McCaig Bone and Joint Health Institute, University of Calgary, 3280 Hospital Drive NW, Calgary, AB, T2N 4Z, Canada. damarsha@ucalgary.ca.
  • Tagimacruz T; Cumming School of Medicine, McCaig Bone and Joint Health Institute, University of Calgary, 3280 Hospital Drive NW, Calgary, AB, T2N 4Z, Canada.
  • Cepoiu-Martin M; Department of Critical Care Medicine, Cumming School of Medicine, University of Calgary, Calgary, Canada.
  • Robert J; Surgery, Alberta Health Services, Bone & Joint Strategic Clinical NetworkTM, Alberta, Canada.
  • Ring B; Surgery Strategic Clinical NetworkTM, Alberta Health Services, Alberta, Canada.
  • Burston M; Alberta Health Services, Alberta, Canada.
  • Higgins S; Surgery Strategic Clinical NetworkTM, Alberta Health Services, Alberta, Canada.
  • Hess M; Alberta Health Services, Alberta, Canada.
  • White J; Surgery Strategic Clinical NetworkTM, Alberta Health Services, Alberta, Canada.
J Med Syst ; 47(1): 4, 2022 Dec 30.
Article in En | MEDLINE | ID: mdl-36585480
ABSTRACT
Delays beyond recommended wait times, especially for specialist services, are associated with adverse health outcomes. The Alberta Surgical Initiative aims to improve the referral wait time-the time between a referral is received at the central intake to the time a specialist sees the patient. Using the discrete event simulation modelling approach, we evaluated and compared the impact of four referral distribution policies in a central intake system on three system performance measures (number of consultations, referral wait time and surgeon utilization). The model was co-designed with clinicians and clinic staff to represent the flow of patients through the system. We used data from the Facilitated Access to Surgical Treatment (FAST) centralized intake referral program for General Surgery to parameterize the model. Four distribution policies were evaluated - next-available-surgeon, sequential, "blackjack," and "kanban." A sequential distribution of referrals for surgical consultation among the surgeons resulted in the worst performance in terms of the number of consultations, referral wait time and surgeon utilization. The three other distribution policies are comparable in performance. The "next available surgeon" model provided the most efficient and robust model, with approximately 1,000 more consultations, 100 days shorter referral time and a 14% increase in surgeon utilization. Discrete event simulation (DES) modelling can be an effective tool to illustrate and communicate the impact of the referral distribution policy on system performance in terms of the number of consultations, referral wait time and surgeon utilization.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Referral and Consultation / Waiting Lists Type of study: Prognostic_studies Limits: Humans Country/Region as subject: America do norte Language: En Journal: J Med Syst Year: 2022 Document type: Article Affiliation country: Canada

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Referral and Consultation / Waiting Lists Type of study: Prognostic_studies Limits: Humans Country/Region as subject: America do norte Language: En Journal: J Med Syst Year: 2022 Document type: Article Affiliation country: Canada