A decision support system for demand and capacity modelling of an accident and emergency department

© 2019 Operational Research Society.Accident and emergency (A&E) departments in England have been struggling against severe capacity constraints. In addition, A&E demands have been increasing year on year. In this study, our aim was to develop a decision support system combining discrete event simulation and comparative forecasting techniques for the better management of the Princess Alexandra Hospital in England. We used the national hospital episodes statistics data-set including period April, 2009 – January, 2013. Two demand conditions are considered: the expected demand condition is based on A&E demands estimated by comparing forecasting methods, and the unexpected demand is based on the closure of a nearby A&E department due to budgeting constraints. We developed a discrete event simulation model to measure a number of key performance metrics. This paper presents a crucial study which will enable service managers and directors of hospitals to foresee their activities in future and form a strategic plan well in advance.Peer reviewe

An Optimisation-based Framework for Complex Business Process: Healthcare Application

The Irish healthcare system is currently facing major pressures due to rising demand, caused by population growth, ageing and high expectations of service quality. This pressure on the Irish healthcare system creates a need for support from research institutions in dealing with decision areas such as resource allocation and performance measurement. While approaches such as modelling, simulation, multi-criteria decision analysis, performance management, and optimisation can – when applied skilfully – improve healthcare performance, they represent just one part of the solution. Accordingly, to achieve significant and sustainable performance, this research aims to develop a practical, yet effective, optimisation-based framework for managing complex processes in the healthcare domain. Through an extensive review of the literature on the aforementioned solution techniques, limitations of using each technique on its own are identified in order to define a practical integrated approach toward developing the proposed framework. During the framework validation phase, real-time strategies have to be optimised to solve Emergency Department performance issues in a major hospital. Results show a potential of significant reduction in patients average length of stay (i.e. 48% of average patient throughput time) whilst reducing the over-reliance on overstretched nursing resources, that resulted in an increase of staff utilisation between 7% and 10%. Given the high uncertainty in healthcare service demand, using the integrated framework allows decision makers to find optimal staff schedules that improve emergency department performance. The proposed optimum staff schedule reduces the average waiting time of patients by 57% and also contributes to reduce number of patients left without treatment to 8% instead of 17%. The developed framework has been implemented by the hospital partner with a high level of success

Cost comparison of orthopaedic fracture pathways using discrete event simulation in a Glasgow hospital

Objective: Healthcare faces the continual challenge of improving outcome whilst aiming to reduce cost. The aim of this study was to determine the micro cost differences of the Glasgow non-operative trauma virtual pathway in comparison to a traditional pathway. Design:  Discrete event simulation was used to model and analyse cost and resource utilisation with an activity based costing approach. Data for a full comparison before the process change was unavailable so we utilised a modelling approach, comparing a Virtual Fracture Clinic (VFC) to a simulated Traditional Fracture Clinic (TFC). Setting:  The orthopaedic unit VFC pathway pioneered at Glasgow Royal Infirmary has attracted significant attention and interest and is the focus of this cost study. Outcome measures: Our study focused exclusively on non-operative trauma patients attending Emergency Department or the minor injuries unit and the subsequent step in the patient pathway. Retrospective studies of patient outcomes as a result of the protocol introductions for specific injuries in association with activity costs from the models.ResultsPatients are satisfied with the new pathway, the information provided and the outcome of their injuries (Evidence Level IV). There was a 65% reduction in the number of first outpatient face-to-face attendances in orthopaedics. In the VFC pathway, the resources required per day were significantly lower for all staff groups (p=<0.001). The overall cost per patient of the VFC pathway was £22.84 (95% CI: 21.74, 23.92) per patient compared with £36.81 (95% CI: 35.65, 37.97) for the TFC pathway.  Conclusions:  Our results give a clearer picture of the cost comparison of the virtual pathway over a wholly traditional face-to-face clinic system. The use of simulation-based stochastic costings in healthcare economic analysis has been limited to date, but this study provides evidence for adoption of this method as a basis for its application in other healthcare settings

Simulation-based Framework to Improve Patient Experience in an Emergency Department

The global economic crisis has a significant impact on healthcare resource provision worldwide. The management of limited healthcare resources is further challenged by the high level of uncertainty in demand, which can lead to unbalanced utilisation of the available resources and a potential deterioration of patient satisfaction in terms of longer waiting times and perceived reduced quality of services. Therefore, healthcare managers require timely and accurate tools to optimise resource utility in a complex and ever-changing patient care process. An interactive simulation-based decision support framework is presented in this paper for healthcare process improvement. Complexity and different levels of variability within the process are incorporated into the process modelling phase, followed by developing a simulation model to examine the impact of potential alternatives. As a performance management tool, balanced scorecard (BSC) is incorporated within the framework to support continual and sustainable improvement by using strategic-linked performance measures and actions. These actions are evaluated by the simulation model developed, whilst the trade-off between objectives, though somewhat conflicting, is analysed by a preference model. The preference model is designed in an interactive and iterative process considering decision makers preferences regarding the selected key performance indicators (KPIs). A detailed implementation of the framework is demonstrated on an emergency department (ED) of an adult teaching hospital in north Dublin, Ireland. The results show that the unblocking of ED outflows by in-patient bed management is more effective than increasing only the ED physical capacity or the ED workforce

Emergency Department Patient Flow Simulation at HealthAlliance

One of the challenges faced by the healthcare industry is the growing number of patients, which has caused an increase in wait time and staff utilization. These effects greatly influence patient flow, resulting in unnecessary costs. For this project, we used discrete event simulation to study and improve processes within the Emergency Department at HealthAlliance Hospital in Leominster, Massachusetts

Implementation of an Emergency Department Split-Flow Process at Saint Vincent Hospital

Emergency Departments (ED) are faced with the challenges of increasing demand and constrained capacity, resulting in the need for hospitals to improve efficiency and patient care. In response, some have implemented a concept known as a split-flow process in their ED. The purpose of our project was to develop recommendations for the implementation of a split-flow process at Saint Vincent’s ED in Worcester, MA. We analyzed current ED processes and developed a discrete-event simulation to project the effect of split-flow on ED performance metrics. We present recommendations for staffing assignments, physical layouts, and resources required for a successful implementation. To our knowledge, this is the first simulation model used to guide the implementation of a split-flow process in an ED

Improving Patient Safety, Patient Flow and Physician Well-Being in Emergency Departments

Over 151 million people visit US Emergency Departments (EDs) annually. The diverse nature and overwhelming volume of patient visits make the ED one of the most complicated settings in healthcare to study. ED overcrowding is a recognized worldwide public health problem, and its negative impacts include patient safety concerns, increased patient length of stay, medical errors, patients left without being seen, ambulance diversions, and increased health system expenditure. Additionally, ED crowding has been identified as a leading contributor to patient morbidity and mortality. Furthermore, this chaotic working environment affects the well-being of all ED staff through increased frustration, workload, stress, and higher rates of burnout which has a direct impact on patient safety. This research takes a step-by-step approach to address these issues by first forecasting the daily and hourly patient arrivals, including their Emergency Severity Index (ESI) levels, to an ED utilizing time series forecasting models and machine learning models. Next, we developed an agent-based discrete event simulation model where both patients and physicians are modeled as unique agents for capturing activities representative of ED. Using this model, we develop various physician shift schedules, including restriction policies and overlapping policies, to improve patient safety and patient flow in the ED. Using the number of handoffs as the patient safety metric, which represents the number of patients transferred from one physician to another, patient time in the ED, and throughput for patient flow, we compare the new policies to the current practices. Additionally, using this model, we also compare the current patient assignment algorithm used by the partner ED to a novel approach where physicians determine patient assignment considering their workload, time remaining in their shift, etc. Further, to identify the optimal physician staffing required for the ED for any given hour of the day, we develop a Mixed Integer Linear Programming (MILP) model where the objective is to minimize the combined cost of physician staffing in the ED, patient waiting time, and handoffs. To develop operations schedules, we surveyed over 70 ED physicians and incorporated their feedback into the MILP model. After developing multiple weekly schedules, these were tested in the validated simulation model to evaluate their efficacy in improving patient safety and patient flow while accounting for the ED staffing budget. Finally, in the last phase, to comprehend the stress and burnout among attending and resident physicians working in the ED for the shift, we collected over 100 hours of physiological responses from 12 ED physicians along with subjective metrics on stress and burnout during ED shifts. We compared the physiological signals and subjective metrics to comprehend the difference between attending and resident physicians. Further, we developed machine learning models to detect the early onset of stress to assist physicians in decision-making

Taxonomic classification of planning decisions in health care: a review of the state of the art in OR/MS

We provide a structured overview of the typical decisions to be made in resource capacity planning and control in health care, and a review of relevant OR/MS articles for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making

HCD-SIM: Healthcare Clinic Design Simulator With Application to a Bone Marrow Transplant Clinic

The treatment of patients in outpatient healthcare clinics is continually growing as technology improves and recovery benefits are recognized. In this thesis, a simulation framework is developed to model the operational aspects of clinics with the goal of providing a method to understand the impact of clinic design decisions relative to productivity, efficiency, and quality of patient care. The healthcare clinic design simulator (HCD-Sim) is designed to study the dynamic system behavior of clinics and to analyze alternative outpatient healthcare clinic designs. Additionally, the simulation framework is created using a data-driven structure that can represent a large class of outpatient healthcare clinics through the specification of clinic data relative to patient flows, work flows, and resource requirements. To demonstrate capability, the framework is applied to a representative general clinic to analyze capacity and investigate important resources that impact the clinic’s performance. Lastly, the framework is applied to a Bone Marrow Transplant (BMT) clinic application to examine the system and provide design recommendations