A Decision Support System for Measuring and Modelling the Multi-Phase Nature of Patient Flow in Hospitals

Vasilakis, Christos; El‐Darzi, Elia; Chountas, Panagiotis

doi:10.1007/978-3-540-77623-9_12

Cited by 7 publications

(9 citation statements)

References 15 publications

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“…were estimated as k = 16.14 patients per day, 415 s = 5.5 days, and c = 150 beds -community hospitals (one-HES) database and concerns stroke patients[26,27]. The corre-420 sponding parameters were estimated as k = 286.2 patients per 421 day, s = 14.29 days, and c = 5587 beds -three-compartment system.…”

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confidence: 99%

Improving hospital bed occupancy and resource utilization through queuing modeling and evolutionary computation

Belciug

Gorunescu

2015

Journal of Biomedical Informatics

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Scarce healthcare resources require carefully made policies ensuring optimal bed allocation, quality healthcare service, and adequate financial support. This paper proposes a complex analysis of the resource allocation in a hospital department by integrating in the same framework a queuing system, a compartmental model, and an evolutionary-based optimization. The queuing system shapes the flow of patients through the hospital, the compartmental model offers a feasible structure of the hospital department in accordance to the queuing characteristics, and the evolutionary paradigm provides the means to optimize the bed-occupancy management and the resource utilization using a genetic algorithm approach. The paper also focuses on a "What-if analysis" providing a flexible tool to explore the effects on the outcomes of the queuing system and resource utilization through systematic changes in the input parameters. The methodology was illustrated using a simulation based on real data collected from a geriatric department of a hospital from London, UK. In addition, the paper explores the possibility of adapting the methodology to different medical departments (surgery, stroke, and mental illness). Moreover, the paper also focuses on the practical use of the model from the healthcare point of view, by presenting a simulated application.

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confidence: 99%

Improving hospital bed occupancy and resource utilization through queuing modeling and evolutionary computation

Belciug

Gorunescu

2015

Journal of Biomedical Informatics

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“…A number of models have been developed to tackle some of the challenges presented in the previous section (Harper and Shahani (2002), Gallivan et al (2002), Utley et al (2003), Vasilakis et al (2008)). Demand and capacity planning in healthcare, more specifically in hospitals, has been a topic in Operations Management (OM) and Operational Research (OR) context for many years.…”

Section: Review Of Literature and Modelling Methodologiesmentioning

confidence: 99%

Demand and capacity modelling for acute services using discrete event simulation

Demir

Günal

Southern³

2017

Health Systems

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Increasing demand for services in England with limited healthcare budget has put hospitals under immense pressure. Given that almost all National Health Service (NHS) hospitals have severe capacity constraints (beds and staff shortages) a decision support tool (DST) is developed for the management of a major NHS Trust in England. Acute activities are forecasted over a 5 year period broken down by age groups for 10 specialty areas. Our statistical models have produced forecast accuracies in the region of 90%. We then developed a discrete event simulation model capturing individual patient pathways until discharge (in A&E, inpatient and outpatients), where arrivals are based on the forecasted activity outputting key performance metrics over a period of time, e.g., future activity, bed occupancy rates, required bed capacity, theatre utilisations for electives and non-electives, clinic utilisations, and diagnostic/treatment procedures. The DST allows Trusts to compare key performance metrics for 1,000's of different scenarios against their existing service (baseline). The power of DST is that hospital decision makers can make better decisions using the simulation model with plausible assumptions which are supported by statistically validated data.

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“…Although [19] [20] are only applications of OLAP technologies to a special problem, these works demonstrate that a multidimensional database is suitable to store several hundreds of thousands of simulation results. Simulation models, DW and analysis tools with OLAP technologies were also involved in decision support systems or forecast systems [21] [22]. Not only their researches solve specific problems but they also demonstrated a promising use of gathering and analyzing simulation results by using data warehouse and OLAP technologies.…”

Section: Related Workmentioning

confidence: 99%

CFBM - A Framework for Data Driven Approach in Agent-Based Modeling and Simulation

Truong

Amblard

Gaudou

et al. 2016

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao. Abstract. Recently, there has been a shift from modeling driven approach to data driven approach in Agent Based Modeling and Simulation (ABMS). This trend towards the use of data-driven approaches in simulation aims at using more and more data available from the observation systems into simulation models [1,2]. In a data driven approach, the empirical data collected from the target system are used not only for the design of the simulation models but also in initialization, evaluation of the output of the simulation platform. That raises the question how to manage empirical data, simulation data and compare those data in such agentbased simulation platform.In this paper, we first introduce a logical framework for data driven approach in agent-based modeling and simulation. The introduced framework is based on the combination of Business Intelligence solution and a multi-agent based platform called CFBM (Combination Framework of Business intelligence and Multi-agent based platform). Secondly, we demonstrate the application of CFBM for data driven approach via the development of a Brown Plant Hopper Surveillance Models (BSMs), where CFBM is used not only to manage and integrate the whole empirical data collected from the target system and the data produced by the simulation model, but also to initialize and validate the models.The successful development of the CFBM consists not only in remedying the limitation of agent-based modeling and simulation with regard to data management but also in dealing with the development of complex simulation systems with large amount of input and output data supporting a data driven approach.

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A Decision Support System for Measuring and Modelling the Multi-Phase Nature of Patient Flow in Hospitals

Cited by 7 publications

References 15 publications

Improving hospital bed occupancy and resource utilization through queuing modeling and evolutionary computation

Improving hospital bed occupancy and resource utilization through queuing modeling and evolutionary computation

Demand and capacity modelling for acute services using discrete event simulation

CFBM - A Framework for Data Driven Approach in Agent-Based Modeling and Simulation

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