Fourth International Conference on Health Care Systems Engineering

Montreal, 30 May — 1 June 2019

Schedule Authors My Schedule

Patient and bed management

May 31, 2019 01:00 PM – 02:30 PM

Location: Room: Marcel Lacoste

3 Presentations

  • 01:00 PM - 01:30 PM

    Non-emergency patient transfer scheduling and assignment

    • Travis Foster, presenter, Dalhousie University
    • Peter Vanberkel, Dalhousie University
    • Uday Venkatadri, Dalhousie University
    • Theresia van Essen, Delft University of Technology

    Emergency Medical Services organizations are responsible for providing paramedic crews, vehicles and equipment to transfer patients from one location to another in a non-emergency setting. They must solve difficult scheduling and assignment problems to ensure on-time arrival of patients and the efficient use of health care resources. The objective of this study is to develop a mathematical model that will assign Patient Transfer Units to non-emergency patient transfer requests and design a schedule that will minimize travel costs, balance workloads, and account for system congestion. This paper also proposes a framework to utilize historical patient transfer data in the scheduling process. The mathematical model provides decision support for the non-emergency patient transfer scheduling process.

  • 01:30 PM - 02:00 PM

    Non-emergency patients transportation with the consideration of user inconvenience

    • Jamal Nasir, presenter, The University of Hong Kong
    • Yong-Hong Kuo,
    • Reynold Cheng, The University of Hong Kong

    n this work, we study a Non-emergency Patients Transportation (NEPT) problem in the context of delivering such services in Hong Kong. The purpose of our work is to examine the user inconvenience (waiting time) with the goals of optimizing the vehicle utilization and operating costs. We developed a Mixed Integer Linear Programming (MILP) formulation for the NEPT problem and solved the mathematical model by CPLEX to get optimal results. Using a weighted objective function-based sensitivity analysis, the behaviour of the MILP model is analyzed regarding multiple performance measures, namely operating cost, underutilization level, and user waiting time. This study provides decision makers with the insights into the impacts of objective weights on different performance measures. Our solutions not only reduce the operating costs but also the patient inconvenience. Moreover, our computational experiments based on a case study demonstrate the effective implementation of the model and show the practicality of our methodology.

  • 02:00 PM - 02:30 PM

    Modelling hospital internal medicine wards to address patient complexity: a simulation-optimization approach

    • Paolo Landa, presenter, University of Exeter
    • Micaela La Regina, ASL 5 Spezzino
    • Elena Tànfani, University of Genova
    • Francesco Orlandini, ASL 4 Chiavarese
    • Mauro Campanini, Ospedale Maggiore della Carità
    • Andrea Fontanella, Ospedale del Buonconsiglio – Fatebenefratelli
    • Dario Manfellotto, Ospedale Fatebenefratelli Isola Tiberina
    • Angela Testi, Department of Economics and Quantitative Methods University of Genova

    In this paper we focus on patient flows inside Internal Medicine Departments, with the aim of supporting new organizational models taking into account the patient relevant characteristics such as complexity and frailty. The main contribution of this paper is to develop a Discrete Event Simulation model to describe in detail the pathways of complex patients through medical hospital wards. The model has been applied to reproduce a case study of an Italian middle size hospital. The first objective of our study is quantifying the impact on resource use and outcome of introducing a new organizational model for medical departments.
    The re-organization is mainly focused on changing the available beds assignment among the wards to better address the complexity of care of patients with comorbidities. Following a patient-centered approach, patients are clustered considering the clinical characteristics (i.e. the pathology, proxy of Diagnoses Related Groups classification) and sub-grouped considering other characteristics, such as comorbidities and ward of admission. Then, an optimization component embedded into the model chooses the best pooling strategy to reorganize medical wards, determining the corresponding number of beds able to improve process indicators, such as length of stay. The simulation model is presented and preliminary results areanalyzed and discussed.