Patient Flow Simulation Using Historically Informed Synthetic Data

Kenny, Ezra; Hassanzadeh, Hamed; Khanna, Sankalp; Boyle, Justin; Louise, Sandra

doi:10.3233/shti210007

Cited by 5 publications

(5 citation statements)

References 9 publications

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“…Given that the simulation model is trained on this actual data with its limitations, it is not practical to try and model scenarios to achieve higher performances. Although this is not practical in a retrospective analysis, it should be mentioned that a simulation with synthetic data enables implementing scenarios where higher performances can be tested 18 …”

Section: Discussionmentioning

confidence: 99%

“…Although this is not practical in a retrospective analysis, it should be mentioned that a simulation with synthetic data enables implementing scenarios where higher performances can be tested. 18 Simulation was based on historic data patterns of patient movements and guided by clinical flows which reflected actual lengths of stay in each ward (as opposed to arbitrarily generated time points) and a 1-year timeframe for simulation assessed in the present study is reasonable to capture underlying variation in these ward movements. However, care pathways within a hospital are dynamic (certainly the COVID-19 pandemic has changed many) and similar modelling is recommended for all hospitals using the latest data available.…”

Section: Limitationsmentioning

confidence: 99%

“…[2][3][4][5] Discrete event simulation (DES), agent-based simulation, system dynamics and hybrid models based on these are common simulation approaches in various domains. [6][7][8][9][10][11][12][13][14][15][16] In the healthcare domain, DES is among the most commonly used method of simulation, 4,11,[17][18][19][20][21][22] where a system is modelled with a number of resources (with defined capacities), queues, and a series of events. 23 The DES approach offers better representations than other afore-mentioned individual techniques to model complex systems involving individual movements between resources and queues.…”

Section: Introductionmentioning

confidence: 99%

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New bed configurations and discharge timing policies: A hospital‐wide simulation

Hassanzadeh

Khanna

Boyle

et al. 2022

Emerg Medicine Australasia

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Objective Optimising patient flow is becoming an increasingly critical issue as patient demand fluctuates in healthcare systems with finite capacity. Simulation provides a powerful tool to fine‐tune policies and investigate their impact before any costly intervention. Methods A hospital‐wide discrete event simulation is developed to model incoming flow from ED and elective units in a busy metropolitan hospital. The impacts of two different policies are investigated using this simulation model: (i) varying inpatient bed configurations and a load sharing strategy among a cluster of wards within a medical department and (ii) early discharge strategies on inpatient bed access. Several clinically relevant bed configurations and early discharge scenarios are defined and their impact on key performance metrics are quantified. Results Sharing beds between wards reduced the average and total ED length of stay (LOS) by 21% compared to having patients queue for individual wards. The current baseline performance level could be maintained by using fewer beds when the load sharing approach was imposed. Earlier discharge of inpatients resulted in reducing average patient ED LOS by approximately 16% and average patient waiting time by 75%. Specific time‐based discharge targets led to greater improvements in flow compared to blanket approaches of discharging all patients 1 or 2 hours earlier. Conclusions ED access performance for admitted patients can be improved by modifying downstream capacity or inpatient discharge times. The simulation model was able to quantify the potential impacts of such policies on patient flow and to provide insights for future strategic planning.

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Section: Discussionmentioning

confidence: 99%

Section: Limitationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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New bed configurations and discharge timing policies: A hospital‐wide simulation

Hassanzadeh

Khanna

Boyle

et al. 2022

Emerg Medicine Australasia

Self Cite

View full text Add to dashboard Cite

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“…Research predominantly focuses on utilizing Discrete Event Simulation (DES) and advances in the field of data science [6,[15][16][17][18]. Most studies aim to strategically determine how many additional resources are needed to alleviate the bottlenecks caused by patient congestion [19,20]. However, realistically, increasing resources is challenging due to financial constraints [21], necessitating research focused on optimizing existing personnel and equipment resources.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Patient Flow in Emergency Departments: A Machine Learning and Simulation-Based Resource Scheduling Approach

Kim

2024

Applied Sciences

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The efficient scheduling of resources within emergency departments (EDs) is crucial to minimizing patient length of stay (LoS) times and maximizing the utilization of limited resources. Reducing patient wait times can enhance the operation of emergency departments and improve patient satisfaction and the quality of medical care. This study develops a simulation model using Discrete Event Simulation (DES) methodology, examining six resource scheduling policies that consider different combinations of general and senior physicians. By leveraging six scheduling policies and machine learning techniques, this model dynamically identifies the most effective scheduling policy, based on a comprehensive dataset of ED visits in South Korea. The ED simulation achieves an accuracy rate of 90% and demonstrates that our proposed integrated machine learning approach reduces average length of stay (LoS) to approximately 322.91 min, compared to 327.10 min under traditional methods. This study underscores the potential of integrating DES and machine learning to enhance resource management in EDs.

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“…Generating synthetic samples through simulation is gradually becoming popular. For example, there has been some work done in generating synthetic samples using a discrete event simulator called SimPy [129]. It can also be corrected in the modeling stage by using a penalized model or by evaluating model performance using a different metric other than accuracy.…”

Section: Correct For Imbalancementioning

confidence: 99%

A Robust Discrete Event Method for the Design of Cyber-Physical Systems

Boi-Ukeme¹

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The technological advancement in Cyber-Physical Systems (CPS) has seen more sophisticated hardware, leading to systems that are complex, interconnected, and require automation. This trend has made modern CPS fragile and susceptible to faults. Traditional methods for fault detection and diagnosis are unable to adequately scale up to handle the faults that occur in CPS because of the tight interconnectivity between the physical and cyber parts of CPS. Also, real-time requirements present new challenges that are not sufficiently addressed by traditional fault-tolerant design approaches, therefore more intelligent methods are now needed to deal with these faults. To address these issues, we propose an approach to formally define faults, detect, and diagnose faults, and accommodate faults for fault tolerance.For fault detection and diagnosis, we propose a generic fault detection and diagnosis (FDD) scheme capable of diagnosing CPS faults in real-time. The scheme is developed to accommodate different modeling methods but for clarity of explanation, we adapt it to the DEVS formalism. To test the scheme, we implemented a library to store fault codes in a data structure and developed intelligent logic to ensure faults are correctly detected and isolated. We also propose a purely data-driven approach to detect faults in CPS where we have no control over the design of the control system. Our data-driven methods thrive on setting rigorous processes and workflows to ensure that representative data is collected for CPS to ensure that FDD techniques perform optimally.

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Patient Flow Simulation Using Historically Informed Synthetic Data

Cited by 5 publications

References 9 publications

New bed configurations and discharge timing policies: A hospital‐wide simulation

New bed configurations and discharge timing policies: A hospital‐wide simulation

Enhancing Patient Flow in Emergency Departments: A Machine Learning and Simulation-Based Resource Scheduling Approach

A Robust Discrete Event Method for the Design of Cyber-Physical Systems

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