Modeling of healthcare systems: Past, current and future trends

Arisha, Amr; Rashwan, Wael

doi:10.1109/wsc.2016.7822203

Cited by 22 publications

(17 citation statements)

References 39 publications

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“…We combined two modeling methods, ABS and DES, to incorporate the process-driven features of a hospital environment with the individual details of patient-patient pathogen transmission. This is the first study to use a hybrid agent based/discrete event simulation model to investigate the impact of outbreak management interventions on the spread of pathogens throughout a hospital [6]. We created a flexible model capable of integrating a range of pathogen transmission features like environmental contamination, infection and mortality.…”

Section: Discussionmentioning

confidence: 99%

“…Hybrid modeling provides clinicians with the information to assist decision-making in clinical situations and justify the use of new strategies. Healthcare system modeling is trending towards increased use of hybrid modeling, as the focus shifts from operational and process questions (patient flow, logistics and healthcare operations) to resources and design issues (capacity planning, resource allocation and program evaluation) [6]. A holistic healthcare simulation model must incorporate four interacting perspectives: resource allocation; health diffusion; population dynamics; and individual behavior [30].…”

Section: Discussionmentioning

confidence: 99%

“…A systematic review on ABS models found seven studies involving interventions for controlling HAIs, 14 on methicillin-resistant Staphylococcus aureus transmission and three on Clostridium difficile [5]. Simulation studies within hospitals typically employ DES models [6]. Hybrid ABS/DES models have been sparsely used in hospital settings or outbreak scenarios.…”

Section: Introductionmentioning

confidence: 99%

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A hybrid simulation model approach to examine bacterial genome sequencing during a hospital outbreak

Elliott

Lee

Foeglein³

et al. 2020

BMC Infect Dis

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Background: Hospital infection control requires timely detection and identification of organisms, and their antimicrobial susceptibility. We describe a hybrid modeling approach to evaluate whole genome sequencing of pathogens for improving clinical decisions during a 2017 hospital outbreak of OXA-181 carbapenemase-producing Escherichia coli and the associated economic effects. Methods: Combining agent-based and discrete-event paradigms, we built a hybrid simulation model to assess hospital ward dynamics, pathogen transmission and colonizations. The model was calibrated to exactly replicate the real-life outcomes of the outbreak at the ward-level. Seven scenarios were assessed including genome sequencing (early or late) and no sequencing (usual care). Model inputs included extent of microbiology and sequencing tests, patient-level data on length of stay, hospital ward movement, cost data and local clinical knowledge. The main outcomes were outbreak size and hospital costs. Model validation and sensitivity analyses were performed to address uncertainty around data inputs and calibration. Results: An estimated 197 patients were colonized during the outbreak with 75 patients detected. The total outbreak cost was US$318,654 with 6.1% of total costs spent on sequencing. Without sequencing, the outbreak was estimated to result in 352 colonized patients costing US$531,109. Microbiology tests were the largest cost component across all scenarios. Conclusion: A hybrid simulation approach using the advantages of both agent-based and discrete-event modeling successfully replicated a real-life bacterial hospital outbreak as a foundation for evaluating clinical outcomes and efficiency of outbreak management. Whole genome sequencing of a potentially serious pathogen appears effective in containing an outbreak and minimizing hospital costs.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A hybrid simulation model approach to examine bacterial genome sequencing during a hospital outbreak

Elliott

Lee

Foeglein³

et al. 2020

BMC Infect Dis

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“…However, since then, there has been considerable growth for its interest. According to (Arisha and Rashwan 2017), this growth is strong evidence that the use of simulation provides better decisions in the management of health services, without compromising patient safety. This advantage has increasingly attracted the attention of hospitals and health authorities (Cheng et al 2017).…”

Section: Literature Backgroundmentioning

confidence: 99%

Lean healthcare integrated with discrete event simulation and design of experiments: an emergency department expansion

Gabriel

Campos

Magacho

et al. 2019

Preprint

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Background. Discrete Event Simulation (DES) and Lean Healthcare are management tools that are efficient and assist in the quality and efficiency of health services. In this sense, the purpose of the study is to use lean principles jointly with DES to plan the expansion of a Canadian emergency department and to the demand that comes from small closed care centers. Methods. For this, we used simulation and modeling method. We simulated the emergency department in FlexSim Healthcare® software and, with the Design of Experiments (DoE), we defined the optimal number of locations and resources for each shift. Results. The results show that the ED cannot meet expected demand in the current state. Only 17.2% of the patients were completed treated, and the Length of Stay (LOS), on average, was 2213.7, with a confidence interval of (2131.8 - 2295.6) minutes. However, after changing decision variables, the number of treated patients increased to 95.7% (approximately 600%). Average LOS decreased to 461.2, with a confidence interval of (453.7 - 468.7) minutes, about 79.0%. In addition, the study shows that emergency department staff are balanced, according to Lean principles.

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“…These systems considers a knowledge base and an inference mechanism that uses the clinical data collected generating specific recommendations for each particular case. In [Arisha and Rashwan 2016], the authors discuss different development phases of modeling approaches, presenting evidence that these methods can significantly improve decisions related to health care management at various levels. [Arshad et al 2016] argue challenges to the existing healthcare industry.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of the Tree Augmented Naïve Bayes Classifier for Knowledge Discovery in Healthcare Databases

Moreira¹,

Rodrigues²,

Oliveira³

et al. 2017

Anais Do Simpósio Brasileiro De Computação Aplicada À Saúde (SBCAS)

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Smart decision support systems (DSSs) have been successfully employed in several areas. In healthcare, these systems offer solutions for uncertain reliably acts and moments. Systems based on Bayesian networks (BNs) can generate predictions even in information lack situations. This paper proposes the modeling and presents a performance evaluation study of the Bayesian classifier named Tree Augmented Na¨ıve Bayes (TAN). Results show that the proposed algorithm obtained good performance for a pregnancy database, presenting F-measure 0.92, Kappa statistic 0.8932, and ROC area 0.993. The proposed method allows representing more complex connections between variables. Nevertheless, it requires major computational effort and time that are not needed in other Bayesian algorithms.

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Modeling of healthcare systems: Past, current and future trends

Cited by 22 publications

References 39 publications

A hybrid simulation model approach to examine bacterial genome sequencing during a hospital outbreak

A hybrid simulation model approach to examine bacterial genome sequencing during a hospital outbreak

Lean healthcare integrated with discrete event simulation and design of experiments: an emergency department expansion

Performance Evaluation of the Tree Augmented Naïve Bayes Classifier for Knowledge Discovery in Healthcare Databases

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