Balancing scarce hospital resources during the COVID-19 pandemic using discrete-event simulation

AbstractCOVID-19 has disrupted healthcare operations and resulted in large-scale cancellations of elective surgery. Hospitals throughout the world made life-altering resource allocation decisions and prioritised the care of COVID-19 patients. Without effective models to evaluate resource allocation strategies encompassing COVID-19 and non-COVID-19 care, hospitals face the risk of making sub-optimal local resource allocation decisions. A discrete-event-simulation model is proposed in this paper to describe COVID-19, elective surgery, and emergency surgery patient flows. COVID-19-specific patient flows and a surgical patient flow network were constructed based on data of 475 COVID-19 patients and 28,831 non-COVID-19 patients in Addenbrooke’s hospital in the UK. The model enabled the evaluation of three resource allocation strategies, for two COVID-19 wave scenarios: proactive cancellation of elective surgery, reactive cancellation of elective surgery, and ring-fencing operating theatre capacity. The results suggest that a ring-fencing strategy outperforms the other strategies, regardless of the COVID-19 scenario, in terms of total direct deaths and the number of surgeries performed. However, this does come at the cost of 50% more critical care rejections. In terms of aggregate hospital performance, a reactive cancellation strategy prioritising COVID-19 is no longer favourable if more than 7.3% of elective surgeries can be considered life-saving. Additionally, the model demonstrates the impact of timely hospital preparation and staff availability, on the ability to treat patients during a pandemic. The model can aid hospitals worldwide during pandemics and disasters, to evaluate their resource allocation strategies and identify the effect of redefining the prioritisation of patients.

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Balancing Scarce Hospital Resources During a Pandemic: A Case-Study of COVID-19 at Addenbrooke’s Hospital (UK)

10.21203/rs.3.rs-94739/v1 ◽

2020 ◽

Author(s):

George J. Melman ◽

Ajith K. Parlikad ◽

Ewen A.B. Cameron

Keyword(s):

Resource Allocation ◽

Elective Surgery ◽

Surgical Patient ◽

Healthcare Operations ◽

Specific Patient ◽

Surgery Patient ◽

The Impact ◽

Allocation Decisions ◽

Allocation Strategies ◽

Resource Allocation Decisions

Abstract COVID-19 has disrupted healthcare operations and resulted in large-scale cancellations of elective surgery. Hospitals throughout the world made life-altering resource allocation decisions and prioritised the care of COVID-19 patients. Without effective models to evaluate resource allocation strategies encompassing COVID-19 and non-COVID-19 care, hospitals face the risk of making sub-optimal local resource allocation decisions. A discrete-event-simulation model is proposed in this paper to describe COVID-19, elective surgery, and emergency surgery patient flows. COVID-19-specific patient flows and a surgical patient flow network were constructed based on data of 475 COVID-19 patients and 28,831 non-COVID-19 patients in Addenbrooke's hospital in the UK. The model enabled the evaluation of three resource allocation strategies, for two COVID-19 wave scenarios: proactive cancellation of elective surgery, reactive cancellation of elective surgery, and ring-fencing operating theatre capacity. The results suggest that a ring-fencing strategy outperforms the other strategies, regardless of the COVID-19 scenario, in terms of total direct deaths and the number of surgeries performed. However, this does come at the cost of 50% more critical care rejections. In terms of aggregate hospital performance, a reactive cancellation strategy prioritising COVID-19 is no longer favourable if more than 7.3% of elective surgeries can be considered life-saving. Additionally, the model demonstrates the impact of timely hospital preparation and staff availability, on the ability to treat patients during a pandemic. The model can aid hospitals worldwide during pandemics and disasters, to evaluate their resource allocation strategies and identify the effect of redefining the prioritisation of patients.

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MARKOV MODELING AND DISCRETE EVENT SIMULATION IN HEALTH CARE: A SYSTEMATIC COMPARISON

International Journal of Technology Assessment in Health Care ◽

10.1017/s0266462314000117 ◽

2014 ◽

Vol 30 (2) ◽

pp. 165-172 ◽

Cited By ~ 24

Author(s):

Lachlan Standfield ◽

Tracy Comans ◽

Paul Scuffham

Keyword(s):

Resource Allocation ◽

Cost Effectiveness ◽

Discrete Event Simulation ◽

Discrete Event ◽

Computational Time ◽

Markov Modeling ◽

Event Simulation ◽

The Cost ◽

Allocation Decisions ◽

Resource Allocation Decisions

Objectives: The aim of this study was to assess if the use of Markov modeling (MM) or discrete event simulation (DES) for cost-effectiveness analysis (CEA) may alter healthcare resource allocation decisions.Methods: A systematic literature search and review of empirical and non-empirical studies comparing MM and DES techniques used in the CEA of healthcare technologies was conducted.Results: Twenty-two pertinent publications were identified. Two publications compared MM and DES models empirically, one presented a conceptual DES and MM, two described a DES consensus guideline, and seventeen drew comparisons between MM and DES through the authors’ experience. The primary advantages described for DES over MM were the ability to model queuing for limited resources, capture individual patient histories, accommodate complexity and uncertainty, represent time flexibly, model competing risks, and accommodate multiple events simultaneously. The disadvantages of DES over MM were the potential for model overspecification, increased data requirements, specialized expensive software, and increased model development, validation, and computational time.Conclusions: Where individual patient history is an important driver of future events an individual patient simulation technique like DES may be preferred over MM. Where supply shortages, subsequent queuing, and diversion of patients through other pathways in the healthcare system are likely to be drivers of cost-effectiveness, DES modeling methods may provide decision makers with more accurate information on which to base resource allocation decisions. Where these are not major features of the cost-effectiveness question, MM remains an efficient, easily validated, parsimonious, and accurate method of determining the cost-effectiveness of new healthcare interventions.

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Evaluating the impact of optimization algorithms for patient transits dispatching using discrete event simulation

Operations Research for Health Care ◽

10.1016/j.orhc.2018.03.008 ◽

2018 ◽

Vol 19 ◽

pp. 134-155 ◽

Cited By ~ 3

Author(s):

N. Furian ◽

M. O’Sullivan ◽

C. Walker ◽

S. Vössner

Keyword(s):

Discrete Event Simulation ◽

Optimization Algorithms ◽

Discrete Event ◽

Event Simulation ◽

The Impact

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SMALLHOLDER FARMERS’ RESOURCE ALLOCATION DECISIONS IN A MAIZE-FARMING SYSTEM UNDER CLIMATE RISKS IN MALAWI

AGROFOR ◽

10.7251/agreng2101086m ◽

2021 ◽

Vol 6 (1) ◽

Author(s):

Ruth MAGRETA ◽

Henderson NG’ONG’OLA ◽

Julius MANGISONI ◽

Kennedy MACHILA ◽

Sika GBEGBELEGBE

Keyword(s):

Resource Allocation ◽

Smallholder Farmers ◽

Farming Systems ◽

The Body ◽

Research Station ◽

Climatic Data ◽

Climatic Risk ◽

The Impact ◽

Allocation Decisions ◽

Resource Allocation Decisions

Using household data from Lilongwe districts, along with crop phenology, agronomic management and climatic data from Chitedze Research Station, the Target-MOTAD and DSSAT-CSM models examined the resource allocation decisions of smallholder farmers in maize farming systems under climate risk in Malawi. Specific aims were to evaluate the ability of DSSAT to predict and collate DTM and non-DTM yields under climatic risk and to use a bio-economic procedure developed using DSSAT and Target-MOTAD to explore the impact of climatic risk on allocation of resources to DTM and non-DTM production. The paper argues that higher average yields observed from DTM varieties make it the most optimal maize production plan, in maximizing household incomes, food security, and minimizing deviations from the mean while meeting the set target incomes of farmers compared to non-DTM varieties. The multidisciplinary nature of this paper has contributed to the body of research by providing a powerful analytical procedure of modelling farmers’ resource allocation decisions in maize based farming systems in Malawi. This study necessitates the use of a combination of biophysical and economic procedures when evaluating promising lines prior to variety release in order to identify the high yielding variety that will continuously bring sustained profits to the farmers amidst climate change.

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Impact of Resource Allocation Rules on Marketing Investment-Level Decisions and Profitability

Journal of Marketing Research ◽

10.1177/002224379202900202 ◽

1992 ◽

Vol 29 (2) ◽

pp. 162-175 ◽

Cited By ~ 18

Author(s):

Murali K. Mantrala ◽

Prabhakant Sinha ◽

Andris A. Zoltners

Keyword(s):

Resource Allocation ◽

Response Functions ◽

Top Down ◽

Allocation Rules ◽

Response Data ◽

Investment Level ◽

Sales Response ◽

The Impact ◽

Allocation Decisions ◽

Resource Allocation Decisions

In many organizations, marketing investment-level decisions precede the associated resource allocation decisions and are based on market-level sales response data, often with no attention to the impact of rules used to allocate resources to submarkets. Such top-down budgeting is commonly based on a perception that aggregate sales and profitability are affected much more by the level than by the allocation of the investment. The authors analyze the effects of different resource allocation rules assuming alternative specifications of submarket sales response functions and show that allocation decisions significantly influence aggregate sales response functions, investment-level decisions based on these functions, and realized profit. The authors also show aggregate sales and profit are usually more sensitive to improvements in allocation rules than to increases in investment levels and conclude that resource allocation decisions warrant more attention in marketing budgeting.

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Assessment of the Impact of Teledermatology Using Discrete Event Simulation

2019 Winter Simulation Conference (WSC) ◽

10.1109/wsc40007.2019.9004782 ◽

2019 ◽

Author(s):

Afafe Zehrouni ◽

Vincent Augusto ◽

Xiaolan Xie ◽

Tu-Anh Duong

Keyword(s):

Discrete Event Simulation ◽

Discrete Event ◽

Event Simulation ◽

The Impact

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A Methodology to Evaluate Complex Manufacturing Systems through Discrete-Event Simulation Models

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.502.7 ◽

2012 ◽

Vol 502 ◽

pp. 7-12 ◽

Cited By ~ 1

Author(s):

L.P. Ferreira ◽

E. Ares ◽

G. Peláez ◽

M. Marcos ◽

M. Araújo

Keyword(s):

Discrete Event Simulation ◽

Manufacturing Systems ◽

Discrete Event ◽

Simulation Models ◽

Network Configuration ◽

Event Simulation ◽

Automobile Assembly ◽

Loop Network ◽

Complex Manufacturing Systems ◽

The Impact

This paper proposes a methodology to analyze complex manufacturing systems, based on discrete-event simulation models. The methodology was validated by performing different simulation experiments and will be applied to a multistage multiproduct production line, based on a real case, with a closed-loop network configuration of machines and intermediate buffers consisting of conveyors, which is very common in the automobile sector. A simulation model in an Arena environment was developed, which allowed for an analysis of the important aspects not yet studied in specialized literature, namely the assessment of the impact of the production sequence on the automobile assembly line. Various sequence rules were analyzed and the performance of each of the corresponding simulation models was registered.

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The Impact of One-Dose Package of Medicines on Patient Waiting Time in Dispensing Pharmacy: Application of a Discrete Event Simulation Model

Biological and Pharmaceutical Bulletin ◽

10.1248/bpb.b17-00781 ◽

2018 ◽

Vol 41 (3) ◽

pp. 409-418

Author(s):

Daisuke Furushima ◽

Hiroshi Yamada ◽

Michiko Kido ◽

Yuko Ohno

Keyword(s):

Simulation Model ◽

Discrete Event Simulation ◽

Waiting Time ◽

Discrete Event ◽

Event Simulation ◽

Discrete Event Simulation Model ◽

Patient Waiting Time ◽

The Impact ◽

Patient Waiting

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Designing optimal COVID-19 testing stations locally: A discrete event simulation model applied on a university campus

PLoS ONE ◽

10.1371/journal.pone.0253869 ◽

2021 ◽

Vol 16 (6) ◽

pp. e0253869

Author(s):

Michael Saidani ◽

Harrison Kim ◽

Jinju Kim

Keyword(s):

Simulation Model ◽

Discrete Event Simulation ◽

Discrete Event ◽

Daily Basis ◽

Health Crisis ◽

Event Simulation ◽

Discrete Event Simulation Model ◽

Rapid Result ◽

The Usa ◽

The Impact

Providing sufficient testing capacities and accurate results in a time-efficient way are essential to prevent the spread and lower the curve of a health crisis, such as the COVID-19 pandemic. In line with recent research investigating how simulation-based models and tools could contribute to mitigating the impact of COVID-19, a discrete event simulation model is developed to design optimal saliva-based COVID-19 testing stations performing sensitive, non-invasive, and rapid-result RT-qPCR tests processing. This model aims to determine the adequate number of machines and operators required, as well as their allocation at different workstations, according to the resources available and the rate of samples to be tested per day. The model has been built and experienced using actual data and processes implemented on-campus at the University of Illinois at Urbana-Champaign, where an average of around 10,000 samples needed to be processed on a daily basis, representing at the end of August 2020 more than 2% of all the COVID-19 tests performed per day in the USA. It helped identify specific bottlenecks and associated areas of improvement in the process to save human resources and time. Practically, the overall approach, including the proposed modular discrete event simulation model, can easily be reused or modified to fit other contexts where local COVID-19 testing stations have to be implemented or optimized. It could notably support on-site managers and decision-makers in dimensioning testing stations by allocating the appropriate type and quantity of resources.

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