Chantal Baril

Use of a discrete-event simulation in a Kaizen event: A case study in healthcare

To improve service delivery, healthcare facilities look toward operations research techniques, discrete event simulation and continuous improvement approaches such as Lean manufacturing. Lean management often includes a Kaizen event to facilitate the acceptance of the project by the employees. Business game is also used as a tool to increase understanding of Lean management concepts. In this paper, we study how a business game can be used jointly with discrete event simulation to test scenarios defined by team members during a Kaizen event. The aim is to allow a rapid and successful implementation of the solutions developed during the Kaizen. Our approach has been used to improve patients’ trajectory in an outpatient hematology–oncology clinic. Patient delays before receiving their treatment were reduced by 74 percent after 19 weeks.

Design and analysis of an outpatient orthopaedic clinic performance with discrete event simulation and design of experiments

This paper evaluates different patient flows (one, two or three-step trajectories).Our model is a more realistic representation than theoretical queuing models.The model shows that 2 exam rooms instead of 3 can be used for all patient flows.Modifying appointment scheduling rules reduces significantly patient lead time.Appointment scheduling rules must be determined according to patient flow types. Considering that demand for healthcare services is constantly increasing, outpatient services must improve their performance. Being able to satisfy the demand with a limited outpatient service capacity is an important operational challenge. The objective of our research consists in studying the relationships and interactions between patient flows, resource capacity (number of consulting rooms and number of nurses) and appointment scheduling rules in order to improve an outpatient orthopaedic clinic performance. Discrete event simulation is used to model outpatient flows. An experimental design was developed to test how to assign consulting rooms and nurses to each orthopedist considering four appointment scheduling rules and three patient flow types of varied complexity. Analysis of variance and the Tukey test are used to evaluate the simulation results. Our conclusion is that in order to improve the outpatient orthopaedic clinic performance, resources (consulting rooms, nurses) and appointment scheduling rules must be adapted to the different patient flows.

The importance of considering resource’s tasks when modeling healthcare services with discrete-event simulation: an approach using work sampling method

Discrete event simulation (DES) is increasingly used to model and analyse healthcare systems processes. Unlike the manufacturing industry, healthcare personnel benefits from a professional independence allowing them to choose the next task to accomplish. Because of this characteristic modelling healthcare systems with DES is more complex. This paper introduces a work sampling method to model nurses’ direct and indirect tasks in a haematology-oncology clinic. We show how this method helps to obtain a more realistic DES model.

Collaborative Decision-Making in Product Design: An Interactive Multiobjective Approach

This paper presents a new procedure to solve multiobjective problems, where the objectives are distributed to various working groups and the decision process is centralized. The approach is interactive and considers the preferences of the working groups. It is based on two techniques: an interactive technique that solves multi-objective problems based on goal programming, and a technique called “linear physical programming” which considers the preferences of the working groups. The approach generates Pareto-optimal solutions. It guides the director in the determination of target values for the objective functions. The approach was tested on two problems that present its capacity to generate Pareto-optimal solutions and to show the convergence to compromise solutions for all the working groups.

Heuristics to solve appointment scheduling in chemotherapy

This paper studies meta-heuristic methods in solving an appointment-scheduling problem in a hospital in Canada. Our paper proposes a two-step algorithm: The first step creates an initial solution with a Greedy Algorithm considering many strategies. The second step consists in choosing the best strategy and improving it with Tabu Search. Our algorithm performed well for the appointment-scheduling problem. The data used was collected from a hematology-oncology department in a hospital. It includes information about patients, nurses, treatments and fixed appointments. Patients must be assigned to nurses with specific treatment seats. We focus on 3 main objectives: maximize the number of patients scheduled over a planning horizon, minimize overtime and provide a more balanced workload between nurses. Our algorithm succeeds in scheduling more patients over a planning horizon without overtime and with a better balanced workload between nurses.

Iterated local search in nurse rostering problem

This paper presents how to solve a nurse rostering problem over the real datasets of Centre hospitalier régional de Trois-Rivières hospital in Canada. Due to the complexity of this problem with plenty of hard constraints, we propose an advanced Iterated Local Search, combining Tabu Search with 2 moves: Single Shift Move and Worst-Scheduled Nurse Swap. Greedy Shuffling with Steepest Descent is also used to improve the solution. Experimental results of our proposed algorithm on 5 real datasets improve the current schedules provided by the hospital. Our experimental results satisfy all of the hard constraints and objectives.

Studying nurse workload and patient waiting time in a hematology-oncology clinic with discrete event simulation

ABSTRACT Our study is performed in a hematology-oncology clinic in Québec. This clinic experienced a 20% increase for hematology treatments and a 131% increase for oncology treatments. Clinic managers and personnel felt that this increase led to higher patient waiting time and personnel workload. Clinic managers decided to examine the possibility of adding resources to alleviate nurse workload. Patient trajectories and lead times, appointment scheduling and nurse workload are analyzed with a discrete-event simulation model. It is shown that patient waiting time is not too long. A nurse overload problem is observed with a nurse occupancy rate of 86.98% in the morning and 64.48% in the afternoon. New schedule appointments taking into account nurse capacity are proposed. These result in a decrease of the difference in nurse occupancy rates in the morning and in the afternoon.