A Simulation--Based Optimization approach for analyzing the ambulance diversion phenomenon in an Emergency-Department network

Ambulance Diversion (AD) is one of the possible strategies for relieving the worldwide phenomenon of Emergency Department (ED) overcrowding. It can be carried out when an ED is overloaded and consists of redirecting incoming by ambulance patients to neighboring EDs. Properly implemented, AD should result in reducing delays of patient treatment, ensuring safety and rescue of life-threatening patients. From an operational point of view, AD corresponds to a resource pooling policy among EDs in a network. In this paper we propose a novel model for studying the effectiveness of AD strategies, based on the Simulation-Based Optimization (SBO) approach. In particular, we developed a discrete event simulation model for reproducing the ED network operation. Then, for each AD policy considered, we formulate and solve an optimal resources allocation problem consisting of a bi-objective SBO problem where the target is the minimization of the non-value added time spent by patients and the overall cost incurred by the ED network. A set of optimal points belonging to the Pareto frontier is obtained for each policy. To show the reliability of the proposed approach, a real case study consisting of six large EDs in the Lazio region of Italy is considered, analyzing the effects of adopting different AD policies.Comment: 22 page

A generic method to develop simulation models for ambulance systems

In this paper, we address the question of generic simulation models and their role in improving emergency care around the world. After reviewing the development of ambulance models and the contexts in which they have been applied, we report the construction of a reusable model for ambulance systems. Further, we describe the associated parameters, data sources, and performance measures, and report on the collection of information, as well as the use of optimisation to configure the service to best effect. Having developed the model, we have validated it using real data from the emergency medical system in a Brazilian city, Belo Horizonte. To illustrate the benefits of standardisation and reusability we apply the model to a UK context by exploring how different rules of engagement would change the performance of the system. Finally, we consider the impact that one might observe if such rules were adopted by the Brazilian system

Modeling the Emergency Care Delivery System Using a Queueing Approach

This thesis considers a regional emergency care delivery system that has a common emergency medical service (EMS) provider and two hospitals, each with a single emergency department (ED) and an inpatient department (ID). Patients arrive at one of the hospital EDs either by ambulance or self-transportation, and we assume that an ambulance patient has preemptive priority over a walk-in patient. Both types of patients can potentially be admitted into the ID or discharged directly from the ED. An admitted patient who cannot access the ID due to the lack of available inpatient beds becomes a boarding patient and blocks an ED server. An ED goes on diversion, e.g., requests the EMS provider to divert incoming ambulances to the neighboring facility, if the total number of its ambulance patients and boarding patients exceeds its capacity (the total number of its servers). The EMS provider will accept the diversion request if the neighboring ED is not on diversion. Both EDs choose its capacity as its diversion threshold and never change the threshold value strategically, and hence they never game. Although the network could be an idealized model of an actual operation, it can be thought of as the simplest network model that is rich enough to reproduce the variety of interactions among different system components. In particular, we aim to highlight the bottleneck effect of inpatient units on ED overcrowding and the network effects resulting from ED diversions. A continuous time Markov chain is introduced for the network model. We show that the chain is irreversible and hence its stationary distribution is difficult to characterize analytically. We identify an alternative solution that builds on queueing decomposition and matrix-analytic methods. We demonstrate through discrete-event simulations the effectiveness of this solution on deriving various performance measures of the original network model. Moreover, by conducting extensive numerical experiments, we provide potential explanations for the overcrowding and delays in a network of hospitals. We suggest remedies from a queueing perspective for the operational challenges facing emergency care delivery systems

Models of Emergency Departments for Reducing Patient Waiting Times

In this paper, we apply both agent-based models and queuing models to investigate patient access and patient flow through emergency departments. The objective of this work is to gain insights into the comparative contributions and limitations of these complementary techniques, in their ability to contribute empirical input into healthcare policy and practice guidelines. The models were developed independently, with a view to compare their suitability to emergency department simulation. The current models implement relatively simple general scenarios, and rely on a combination of simulated and real data to simulate patient flow in a single emergency department or in multiple interacting emergency departments. In addition, several concepts from telecommunications engineering are translated into this modeling context. The framework of multiple-priority queue systems and the genetic programming paradigm of evolutionary machine learning are applied as a means of forecasting patient wait times and as a means of evolving healthcare policy, respectively. The models' utility lies in their ability to provide qualitative insights into the relative sensitivities and impacts of model input parameters, to illuminate scenarios worthy of more complex investigation, and to iteratively validate the models as they continue to be refined and extended. The paper discusses future efforts to refine, extend, and validate the models with more data and real data relative to physical (spatial–topographical) and social inputs (staffing, patient care models, etc.). Real data obtained through proximity location and tracking system technologies is one example discussed

Early Information Access to Alleviate Emergency Department Congestion

Alleviating Emergency Department (ED) congestion results in shorter hospital stay which not only reduces the cost of medical procedure but also increase the hospital performance. Length of patient stay is used to determine the hospital performance. Organization Information Processing (OIPT) Theory is used to explain the impact of information access and availability on the information processing need and ability of a hospital. Technical devices such as RFID that works as “Auto Identification tags” is suggested to increase the information availability as well as the information processing capability of the hospitals. This study suggests that the OIPT needs to be further broken down into its entity form and then the impact of these entities is measured separately. On the other hand, institutional factors such as employee behavior towards the new technology is studied to analyze the impact of human factors in the implementation of these technical devices in the ED procedures. It can be implied from this study that early information access does increase the use of supporting EMR implementation. However, the importance of the use of EMR decreases with time on hospital performance. Moreover, other factors such as management policies related to IT positively moderates the relationship between information availability and the processing capability of a hospital ED

Developing a resource allocation model for the Scottish Patient Transport Service

The Patient Transport Service is a vital component of many healthcare systems. However, increasing demand and constrained resources impose great challenges, especially in Scotland where there is a substantial remote and rural population. This case study describes the development of a decision support tool for strategic resource allocation decisions. The tool had to be relevant to management's practical needs including transparency to a range of stakeholders and a flexible speedy response to help management explore various operational and policy options. However, the tool also had to demonstrate rigour and identify an efficient allocation of resources. In response to these requirements, the decision support tool was constructed from simple models, verified in comparisons with more rigorous and sophisticated approaches, notably a Dial-a-Ride genetic algorithm and an open vehicle routing simulation. Using this tool, management were able to: identify a more rational, strategic allocation of resources; quantify the remote and rural effect; examine trade-offs between service level and resource requirements within various scenarios for future demand

Integrated Planning in Hospitals: A Review

Efficient planning of scarce resources in hospitals is a challenging task for which a large variety of Operations Research and Management Science approaches have been developed since the 1950s. While efficient planning of single resources such as operating rooms, beds, or specific types of staff can already lead to enormous efficiency gains, integrated planning of several resources has been shown to hold even greater potential, and a large number of integrated planning approaches have been presented in the literature over the past decades. This paper provides the first literature review that focuses specifically on the Operations Research and Management Science literature related to integrated planning of different resources in hospitals. We collect the relevant literature and analyze it regarding different aspects such as uncertainty modeling and the use of real-life data. Several cross comparisons reveal interesting insights concerning, e.g., relations between the modeling and solution methods used and the practical implementation of the approaches developed. Moreover, we provide a high-level taxonomy for classifying different resource-focused integration approaches and point out gaps in the literature as well as promising directions for future research

A review of the healthcare-management (modeling) literature published at Manufacturing and Service Operations Management

Healthcare systems throughout the world are under pressure to widen access, improve efficiency and quality of care, and reduce inequity. Achieving these conflicting goals requires innovative approaches, utilizing new technologies, data analytics, and process improvements. The operations management community has taken on this challenge: more than 10% of articles published in M&SOM in the period from 2009 to 2018 has developed analytical models that aim to inform healthcare operational decisions and improve medical decision-making. This article presents a review of the research published in M&SOM on healthcare management since its inception 20 years ago and reflects on opportunities for further research