scholarly journals COVID-19 Models for Hospital Surge Capacity Planning: A Systematic Review

Author(s):  
Michael G. Klein ◽  
Carolynn J. Cheng ◽  
Evonne Lii ◽  
Keying Mao ◽  
Hamza Mesbahi ◽  
...  

ABSTRACT Objective: Health system preparedness for coronavirus disease (COVID-19) includes projecting the number and timing of cases requiring various types of treatment. Several tools were developed to assist in this planning process. This review highlights models that project both caseload and hospital capacity requirements over time. Methods: We systematically reviewed the medical and engineering literature according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We completed searches using PubMed, EMBASE, ISI Web of Science, Google Scholar, and the Google search engine. Results: The search strategy identified 690 articles. For a detailed review, we selected 6 models that met our predefined criteria. Half of the models did not include age-stratified parameters, and only 1 included the option to represent a second wave. Hospital patient flow was simplified in all models; however, some considered more complex patient pathways. One model included fatality ratios with length of stay (LOS) adjustments for survivors versus those who die, and accommodated different LOS for critical care patients with or without a ventilator. Conclusion: The results of our study provide information to physicians, hospital administrators, emergency response personnel, and governmental agencies on available models for preparing scenario-based plans for responding to the COVID-19 or similar type of outbreak.

2007 ◽  
Vol 8 (3) ◽  
pp. 213-223 ◽  
Author(s):  
Ludwig Kuntz ◽  
Stefan Scholtes ◽  
Antonio Vera

2020 ◽  
Vol 54 (6) ◽  
pp. 1757-1773
Author(s):  
Elvan Gökalp

Accident and emergency departments (A&E) are the first place of contact for urgent and complex patients. These departments are subject to uncertainties due to the unplanned patient arrivals. After arrival to an A&E, patients are categorized by a triage nurse based on the urgency. The performance of an A&E is measured based on the number of patients waiting for more than a certain time to be treated. Due to the uncertainties affecting the patient flow, finding the optimum staff capacities while ensuring the performance targets is a complex problem. This paper proposes a robust-optimization based approximation for the patient waiting times in an A&E. We also develop a simulation optimization heuristic to solve this capacity planning problem. The performance of the approximation approach is then compared with that of the simulation optimization heuristic. Finally, the impact of model parameters on the performances of two approaches is investigated. The experiments show that the proposed approximation results in good enough solutions.


2017 ◽  
Vol 8 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Ki-Hwan Bae ◽  
Molly Jones ◽  
Gerald Evans ◽  
Demetra Antimisiaris

2015 ◽  
pp. 390-410
Author(s):  
Stavros T. Ponis ◽  
Angelos Delis ◽  
Sotiris P. Gayialis ◽  
Panagiotis Kasimatis ◽  
Joseph Tan

This paper highlights the opportunities and challenges of applying Discrete Event Simulation (DES) to support capacity planning of a network of outpatient facilities. Despite an abundance of studies using simulation techniques to examine the operation and performance of outpatient clinics, the problem of capacity allocation and planning of medical services within a network of outpatient healthcare facilities appears to be underexplored. Here, a case study of a health insurance provider that operates a network of six outpatient medical facilities in the US is used to illustrate and explore the synthesizing and adaptive, yet parsimonious nature of using DES methodology for network design and capacity planning. Results of this case study demonstrate that significant performance improvements for the network operator can be achieved with applying DES method to support the network facility capacity planning process.


2015 ◽  
Vol 4 (1) ◽  
pp. 1-30 ◽  
Author(s):  
Donna Lillian Namujju ◽  
Gönenç Yücel ◽  
Erik Pruyt ◽  
Richard Okou

Access to power is tied to a country's development. It facilitates improved social welfare, education, health and income generating opportunities. Uganda's economy is stifled by its low electrification rates - 16% nationally. This study builds a working theory on the internal setup of Uganda's power sector utilizing this theory to surface influential behavior modes as they pertain to power generation and supply and how these ultimately affect electricity access. Based on this working theory a System Dynamics simulation model is built. The model simulations show how Uganda's power sector is expected to evolve over 80 years in terms of power supply and demand given existing market structure and prevailing conditions. The study finds major problems in the nature of power accessed specifically an insufficient and unreliable power supply. The root cause is found in the nature of the existing capacity planning process in terms of how future capacity requirements are determined and the agreements made with generators as to how and when they fulfill their investment obligations.


2004 ◽  
Vol 100 (5) ◽  
pp. 1271-1276 ◽  
Author(s):  
Michael L. McManus ◽  
Michael C. Long ◽  
Abbot Cooper ◽  
Eugene Litvak

Background Allocation of scarce resources presents an increasing challenge to hospital administrators and health policy makers. Intensive care units can present bottlenecks within busy hospitals, but their expansion is costly and difficult to gauge. Although mathematical tools have been suggested for determining the proper number of intensive care beds necessary to serve a given demand, the performance of such models has not been prospectively evaluated over significant periods. Methods The authors prospectively collected 2 years' admission, discharge, and turn-away data in a busy, urban intensive care unit. Using queuing theory, they then constructed a mathematical model of patient flow, compared predictions from the model to observed performance of the unit, and explored the sensitivity of the model to changes in unit size. Results The queuing model proved to be very accurate, with predicted admission turn-away rates correlating highly with those actually observed (correlation coefficient = 0.89). The model was useful in predicting both monthly responsiveness to changing demand (mean monthly difference between observed and predicted values, 0.4+/-2.3%; range, 0-13%) and the overall 2-yr turn-away rate for the unit (21%vs. 22%). Both in practice and in simulation, turn-away rates increased exponentially when utilization exceeded 80-85%. Sensitivity analysis using the model revealed rapid and severe degradation of system performance with even the small changes in bed availability that might result from sudden staffing shortages or admission of patients with very long stays. Conclusions The stochastic nature of patient flow may falsely lead health planners to underestimate resource needs in busy intensive care units. Although the nature of arrivals for intensive care deserves further study, when demand is random, queuing theory provides an accurate means of determining the appropriate supply of beds.


2018 ◽  
Vol 33 (2) ◽  
pp. e403-e415 ◽  
Author(s):  
Katarzyna Dubas‐Jakóbczyk ◽  
Christoph Sowada ◽  
Alicja Domagała ◽  
Barbara Więckowska

1974 ◽  
Vol 4 (2) ◽  
pp. 353-364 ◽  
Author(s):  
James Meade

The basic premise explored in this paper is that patient flow in rural areas is based on the proximity to medical care. The hospital is defined as the center of care and hospital catchment areas are defined by patient movements. A methodology is described to analyze patient flow among an assemblage of hospitals. Finally, a model which mathematically replicates patient movement is introduced to act as an aid in the hospital planning process.


Author(s):  
Yung-Cheng Rex Lai ◽  
Mei-Cheng Shih

The demand for railway transportation is expected to be significantly increased worldwide; hence railway agencies are looking for better tools to allocate their capital investments on capacity planning in the best possible way. We presented a capacity planning process to help planners enumerate possible expansion options and determine the optimal network investment plan to meet the future demand. This process was applied to the conventional railway system in Taiwan to demonstrate its potential use. Using this capacity planning process will help railway agencies maximize their return from capacity expansion projects and thus be better able to provide reliable service to their customers, and return on shareholder investment.


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