Norazura Ahmad

A framework for emergency department capacity planning using system dynamics approach and the theory of constraints philosophies

Patient waiting time and service delivery problems characterize health care services and are more acute in an emergency department (ED).With more patients needing care and fewer resources to care for them, ED that operates at or above capacity is inevitable. This paper is a review of work-in-progress of a study being conducted in a government hospital in Penang, Malaysia.This paper proposes a hybrid of System Dynamics (SD) approach and the Theory of Constraints (TOC) in solving health capacity planning. The potential combination of these methods will be reviewed that is hoped to reveal the synergy between the established methods in addressing the health capacity planning options for the long-term future.

Managing resource capacity using hybrid simulation

Due to the diversity of patient flows and interdependency of the emergency department (ED) with other units in hospital, the use of analytical models seems not practical for ED modeling. One effective approach to study the dynamic complexity of ED problems is by developing a computer simulation model that could be used to understand the structure and behavior of the system. Attempts to build a holistic model using DES only will be too complex while if only using SD will lack the detailed characteristics of the system. This paper discusses the combination of DES and SD in order to get a better representation of the actual system than using either modeling paradigm solely. The model is developed using AnyLogic software that will enable us to study patient flows and the complex interactions among hospital resources for ED operations. Results from the model show that patients’ length of stay is influenced by laboratories turnaround time, bed occupancy rate and ward admission rate.

Analysis of MCLP, Q-MALP, and MQ-MALP with Travel Time Uncertainty Using Monte Carlo Simulation

This paper compares the application of the Monte Carlo simulation in incorporating travel time uncertainties in ambulance location problem using three models: Maximum Covering Location Problem (MCLP), Queuing Maximum Availability Location Problem (Q-MALP), and Multiserver Queuing Maximum Availability Location Problem (MQ-MALP). A heuristic method is developed to site the ambulances. The models are applied to the 33-node problem representing Austin, Texas, and the 55-node problem. For the 33-node problem, the results show that the servers are less spatially distributed in Q-MALP and MQ-MALP when the uncertainty of server availability is considered using either the independent or dependent travel time. On the other hand, for the 55-node problem, the spatial distribution of the servers obtained by locating a server to the highest hit node location is more dispersed in MCLP and Q-MALP. The implications of the new model for the ambulance services system design are discussed as well as the limitations of the modeling approach.

A proposed simulation optimization model framework for emergency department problems in public hospital

The Emergency Department (ED) is a very complex system with limited resources to support increase in demand. ED services are considered as good quality if they can meet the patient’s expectation. Long waiting times and length of stay is always the main problem faced by the management. The management of ED should give greater emphasis on their capacity of resources in order to increase the quality of services, which conforms to patient satisfaction. This paper is a review of work in progress of a study being conducted in a government hospital in Selangor, Malaysia. This paper proposed a simulation optimization model framework which is used to study ED operations and problems as well as to find an optimal solution to the problems. The integration of simulation and optimization is hoped can assist management in decision making process regarding their resource capacity planning in order to improve current and future ED operations.

Modeling Emergency Department Using a Hybrid Simulation Approach

Within hospital, emergency department is one of the most important unit that involves complex patient movement flow and detailed operational activities. As an integrated system, the efficiency of emergency department depends on its interaction between inter-departmental units and intra-departmental elements. Over the years, with the rapid development of computer technology, there has been a rising trend of using simulation modeling to improve healthcare operations. Discrete-event simulation (DES) has become a popular and effective decision-making tool for modeling the stochastic operational activities in a system. However for a whole system approach, system dynamics (SD) has advantages over DES. SD does not require vast data and is able to capture the interdependency relations between different units in an integrated system. Both approaches have strengths and weaknesses that may support and complement each other. An integrated model of both approaches will provide a realistic view of a complex system. This chapter provides an overview of the hybrid simulation modeling applications to emergency department.

Understanding the dynamic effects of returning patients toward emergency department density

This paper presents the development of a dynamic hypothesis for the effect of returning patients to the emergency department (ED). A logical tree from the Theory of Constraint known as Current Reality Tree was used to identify the key variables. Then, a hypothetical framework portraying the interrelated variables and its influencing relationships was developed using causal loop diagrams (CLD). The conceptual framework was designed as the basis for the development of a system dynamics model.This paper presents the development of a dynamic hypothesis for the effect of returning patients to the emergency department (ED). A logical tree from the Theory of Constraint known as Current Reality Tree was used to identify the key variables. Then, a hypothetical framework portraying the interrelated variables and its influencing relationships was developed using causal loop diagrams (CLD). The conceptual framework was designed as the basis for the development of a system dynamics model.

Selecting university undergraduate student activities via compromised-analytical hierarchy process and 0-1 integer programming to maximize SETARA points

Prioritizing and making decisions on what student activities to be selected and conducted to fulfill the aspiration of a university as translated in its strategic plan must be executed with transparency and accountability. It is becoming even more crucial, particularly for universities in Malaysia with the recent budget cut imposed by the Malaysian government. In this paper, we illustrated how 0-1 integer programming (0-1 IP) model was implemented to select which activities among the forty activities proposed by the student body of Universiti Utara Malaysia (UUM) to be implemented for the 2017/2018 academic year. Two different models were constructed. The first model was developed to determine the minimum total budget that should be given to the student body by the UUM management to conduct all the activities that can fulfill the minimum targeted number of activities as stated in its strategic plan. On the other hand, the second model was developed to determine which activities to be selected based on the...

Acute asthma severity identification of expert system flow in emergency department

Integration of computerized system in healthcare management help in smoothening the documentation of patient records, highly accesses of knowledge and clinical practices guideline, and advice on decision making. Exploit the advancement of artificial intelligent such as fuzzy logic and rule-based reasoning may improve the management of emergency department in terms of uncertainty condition and medical practices adherence towards clinical guideline. This paper presenting details of the emergency department flow for acute asthma severity identification with the embedding of acute asthma severity identification expert system (AASIES). Currently, AASIES is still in preliminary stage of system validation. However, the implementation of AASIES in asthma bay management is hope can reduce the usage of paper for manual documentation and be a pioneer for the development of a more complex decision support system to smoothen the ED management and more systematic.

A modelling framework for mitigating customers’ waiting time at a vehicle inspection centre

In Malaysia, an agency that is entrusted by the Government to perform mandatory vehicle inspection for public, commercial and private vehicles, receive many customers daily. Often complaints of problems received from the customers are associated with waiting time that leads to lost of business and dissatisfied customers. To address this issue, we propose a framework for modelling a vehicle inspection system using an integration of simulation and optimization approaches. The strengths of simulation and optimization are reviewed briefly that is hoped to reveal the synergy between the established methods in determining an appropriate customer’s waiting time for inspection at a vehicle inspection centre. Relevant concepts and preliminary results are also presented and discussed in this paper.

Minimizing patient waiting time in emergency department of public hospital using simulation optimization approach

Emergency department (ED) is the main unit of a hospital that provides emergency treatment. Operating 24 hours a day with limited number of resources invites more problems to the current chaotic situation in some hospitals in Malaysia. Delays in getting treatments that caused patients to wait for a long period of time are among the frequent complaints against government hospitals. Therefore, the ED management needs a model that can be used to examine and understand resource capacity which can assist the hospital managers to reduce patients waiting time. Simulation model was developed based on 24 hours data collection. The model developed using Arena simulation replicates the actual ED’s operations of a public hospital in Selangor, Malaysia. The OptQuest optimization in Arena is used to find the possible combinations of a number of resources that can minimize patients waiting time while increasing the number of patients served. The simulation model was modified for improvement based on results from OptQuest. The improvement model significantly improves ED’s efficiency with an average of 32% reduction in average patients waiting times and 25% increase in the total number of patients served.Emergency department (ED) is the main unit of a hospital that provides emergency treatment. Operating 24 hours a day with limited number of resources invites more problems to the current chaotic situation in some hospitals in Malaysia. Delays in getting treatments that caused patients to wait for a long period of time are among the frequent complaints against government hospitals. Therefore, the ED management needs a model that can be used to examine and understand resource capacity which can assist the hospital managers to reduce patients waiting time. Simulation model was developed based on 24 hours data collection. The model developed using Arena simulation replicates the actual ED’s operations of a public hospital in Selangor, Malaysia. The OptQuest optimization in Arena is used to find the possible combinations of a number of resources that can minimize patients waiting time while increasing the number of patients served. The simulation model was modified for improvement based on results from OptQues...