4 présentations

• 
  15h30 - 15h55

  A Patient Segmentation Fuzzy Logic Strategy to Allocate Standardization and Individualization in Health Services.

  • Zainab Almukhtar, prés.,

  The Triple Aim framework recognizes the improvement of patients’ experience, quality and managing the cost of healthcare as essential factors in healthcare design (Berwick et al., 2008). Donabedian, (2005) identified the main attributes of quality in healthcare as individualization and standardization. Individualization directs the individual characteristics of patients, and usually incurs more cost. While standardization applies general standards in healthcare (Ansmann & Pfaff, 2017), and supports cost management. Healthcare managers should provide (allocate resources) to those elements according to the needs of patients. Patients/patient groups give differs weights (values) to healthcare attributes according to personal, heath and environmental factors (patient singularities) (Cox, 1982). We propose a strategy for patient segmentation and a fuzzy framework incorporating the singularities of patients. The model allocates healthcare resources to individualized and standardized elements in the health services considering the needs and preferences of patients/patient groups. The features of fuzzy logic in assigning weights to different attributes and transferring to linguistic value proposes an interactive tool to healthcare design. Combined with patient segmentation and with analysis of patient preferences, the proposed study replies to the need for tools to understand and incorporate patient needs in healthcare design.
• 
  15h55 - 16h20

  Real-data simulation on the impact of abo-compatible liver allocation policy in Ontario

  • Suting Yang, prés., Data and Decision Sciences, Ontario Health
  • Saba Vahid, Consulting & Analytics, IQVIA
  • Shabnam Balamchi, Data and Decision Sciences, Ontario Health
  • Sophie Foxcroft , System and Infrastructure Planning, Ontario Health
  • Oren Jalon, Transplant Services, Ontario Health
  • Fareed Hameed, Transplant Services, Ontario Health

  This is a retrospective and data-driven study from 2017/1/1 to 2022/1/1. The objective is to compare different policy scenarios for ABO-compatible liver allocation in Ontario. As per the current policy, livers are only offered to ABO-compatible recipients after all high priority, ABO-identical, and ABO-incompatible paediatric recipients. As a result, very few ABO-compatible liver transplants were performed each year. Under the proposed candidate policies, recipients with certain blood types and severity scores greater than a threshold are eligible for ABO-compatible transplantations, with the aim to minimize the wait time disparities & mortality across blood groups. We first build survival models to understand key factors that impact the survival probabilities and predict the expected lifetime of each recipient given the characteristics. We then build Discrete Event Simulation models to represent waitlist changes and recipient-donor matching algorithms for different policies. Our results demonstrate that the optimal policy significantly reduces wait times for B (14%) and AB (24%) type recipients, with a slight increase of wait times for A (0.7%) and O (3%) recipients. It also balances the mortality rates across blood types. Overall, our results have demonstrated that the optimal policy improves access to waitlisted recipients, proving its feasibility and validity.
• 
  16h20 - 16h45

  Adaptation, Comparison and Practical Implementation of Fairness Schemes in Kidney Exchange Programs

  • William St-Arnaud, prés., Université de Montréal
  • Margarida Carvalho, Polytechnique Montréal
  • Golnoosh Farnadi, HEC Montréal, Mila

  In Kidney Exchange Programs (KEPs), exchange plans allow patients to find compatible kidney donors. The policies driving the selection of exchange plans in KEPs are deterministic. In this work, we consider instead lottery policies over exchange plans. Those lotteries are computed by adapting various multi-objective approaches to KEPs, where we include both a utilitarian and fairness objective. We test multiple fairness objectives by mathematically translating commonly known theories of justice and applying them to KEPs.

  Keywords: Kidney exchange programs, Fairness, Nash social welfare program, Conic programming, Integer programming
• 
  16h45 - 17h10

  Stochastic Casualty Response Planning with Operational Details

  • Pedram Farghadani-Chaharsooghi, prés.,
  • Hossein Hashemi Doulabi, Concordia University and Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT), Montreal, Canada
  • Walter Rei, ESG-UQAM
  • Michel Gendreau, Department of Mathematical and Industrial Engineering, University of Polytechnique Montreal

  In this study, we propose a two-stage stochastic programming model to consider patients with multiple injuries in casualty response planning problems with uncertain demands and hospital bed capacity. We concentrate on locating Alternative Care Facilities (ACFs) and aim to assign different types of resources in the first stage. Then, in the second stage, we allocate patients with multiple injuries to either ACFs or hospitals based on the availability of resources. We apply the L-shaped algorithm and its branch-and-cut (B&C) implementation to solve large-sized problems. To further enhance the efficiency of these algorithms, we incorporate several accelerator techniques, including Benders dual decomposition and lower bounding functional. Extensive computational results show that these features have led to a dramatic improvement in the B&C algorithm performance.