Models for Assessing Strategies for Improving Hospital Capacity for Handling Patients during a Pandemic

Abstract Objective: The aim of this study was to investigate the performance of key hospital units associated with emergency care of both routine emergency and pandemic (COVID-19) patients under capacity enhancing strategies. Methods: This investigation was conducted using whole-hospital, resource-constrained, patient-based, stochastic, discrete-event simulation models of a generic 200-bed urban U.S. tertiary hospital serving routine emergency and COVID-19 patients. Systematically designed numerical experiments were conducted to provide generalizable insights into how hospital functionality may be affected by the care of COVID-19 pandemic patients along specially designated care paths under changing pandemic situations from getting ready to turning all of its resources to pandemic care. Results: Several insights are presented. For example, each day of reduction in average ICU length of stay increases intensive care unit patient throughput by up to 24% for high COVID-19 daily patient arrival levels. The potential of five specific interventions and two critical shifts in care strategies to significantly increase hospital capacity is described. Conclusions: These estimates enable hospitals to repurpose space, modify operations, implement crisis standards of care, prepare to collaborate with other health care facilities, or request external support, increasing the likelihood that arriving patients will find an open staffed bed when one is needed.

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Seismic Performance of Health Care Facilities Using Discrete Event Simulation Models

Computational Methods, Seismic Protection, Hybrid Testing and Resilience in Earthquake Engineering - Geotechnical, Geological and Earthquake Engineering ◽

10.1007/978-3-319-06394-2_12 ◽

2014 ◽

pp. 203-215 ◽

Cited By ~ 4

Author(s):

Gian Paolo Cimellaro ◽

Marta Piqué

Keyword(s):

Health Care ◽

Discrete Event Simulation ◽

Seismic Performance ◽

Discrete Event ◽

Simulation Models ◽

Health Care Facilities ◽

Event Simulation ◽

Care Facilities

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Simulation modeling and analysis of primary health center operations

SIMULATION ◽

10.1177/00375497211030931 ◽

2021 ◽

pp. 003754972110309

Author(s):

Mohd Shoaib ◽

Varun Ramamohan

Keyword(s):

Resource Utilization ◽

Waiting Times ◽

Discrete Event ◽

Simulation Models ◽

Modeling And Analysis ◽

Healthcare Facilities ◽

Event Simulation ◽

Analytical Approximations ◽

Primary Health ◽

Generic Modeling

We present discrete-event simulation models of the operations of primary health centers (PHCs) in the Indian context. Our PHC simulation models incorporate four types of patients seeking medical care: outpatients, inpatients, childbirth cases, and patients seeking antenatal care. A generic modeling approach was adopted to develop simulation models of PHC operations. This involved developing an archetype PHC simulation, which was then adapted to represent two other PHC configurations, differing in numbers of resources and types of services provided, encountered during PHC visits. A model representing a benchmark configuration conforming to government-mandated operational guidelines, with demand estimated from disease burden data and service times closer to international estimates (higher than observed), was also developed. Simulation outcomes for the three observed configurations indicate negligible patient waiting times and low resource utilization values at observed patient demand estimates. However, simulation outcomes for the benchmark configuration indicated significantly higher resource utilization. Simulation experiments to evaluate the effect of potential changes in operational patterns on reducing the utilization of stressed resources for the benchmark case were performed. Our analysis also motivated the development of simple analytical approximations of the average utilization of a server in a queueing system with characteristics similar to the PHC doctor/patient system. Our study represents the first step in an ongoing effort to establish the computational infrastructure required to analyze public health operations in India and can provide researchers in other settings with hierarchical health systems, a template for the development of simulation models of their primary healthcare facilities.

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Facing COVID-19 Pandemic in a Tertiary Hospital in Milan: Prevalence of Burnout in Nursing Staff Working in Sub-Intensive Care Units

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18136684 ◽

2021 ◽

Vol 18 (13) ◽

pp. 6684

Author(s):

Alberto Bisesti ◽

Andrea Mallardo ◽

Simone Gambazza ◽

Filippo Binda ◽

Alessandro Galazzi ◽

...

Keyword(s):

Intensive Care ◽

Respiratory Failure ◽

Intensive Care Units ◽

Healthcare Workers ◽

Maslach Burnout Inventory ◽

Tertiary Hospital ◽

Personal Accomplishment ◽

Preventive Strategies ◽

Common Condition ◽

Hospital Units

In early March 2020, Italy became the epicenter of the Coronavirus Disease 2019 (COVID-19) pandemic in Europe. A different organization of hospital units was required to take care of patients affected by acute respiratory failure caused by COVID-19. This study aimed to evaluate the prevalence of burnout in two sub-intensive care units (SICUs) of the COVID-19 hub center of the Lombardia region in Milan (Italy). All nurses and healthcare assistants working in the SICUs during June 2020 were included in the study. Burnout was assessed via the Maslach Burnout Inventory questionnaire. One hundred and five (84%) SICU staff participated in the study. The prevalence of high burnout for nurses and healthcare assistants was 61.9% for emotional exhaustion, 47.6% for depersonalization and 34.3% for personal accomplishment. Depersonalization was significantly more frequent in younger nurses (p = 0.009). Nurses were 4.5 times more likely to have burnout than healthcare assistants. Burnout was a common condition among healthcare workers operating in SICUs during the pandemic. Urgent actions are needed, especially for nurses, as well as preventive strategies for future pandemic scenarios.

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Assessing asset monitoring levels for maintenance operations

Journal of Manufacturing Technology Management ◽

10.1108/jmtm-01-2013-0003 ◽

2015 ◽

Vol 26 (5) ◽

pp. 632-659 ◽

Cited By ~ 7

Author(s):

Abdullah A Alabdulkarim ◽

Peter Ball ◽

Ashutosh Tiwari

Keyword(s):

Asset Management ◽

Business Models ◽

Discrete Event ◽

Dynamic Modelling ◽

Simulation Models ◽

Content Type ◽

Event Simulation ◽

Product Service ◽

Asset Monitoring ◽

The Impact

Purpose – Asset management has recently gained significance due to emerging business models such as Product Service Systems where the sale of asset use, rather than the sale of the asset itself, is applied. This leaves the responsibility of the maintenance tasks to fall on the shoulders of the manufacturer/supplier to provide high asset availability. The use of asset monitoring assists in providing high availability but the level of monitoring and maintenance needs to be assessed for cost effectiveness. There is a lack of available tools and understanding of their value in assessing monitoring levels. The paper aims to discuss these issues. Design/methodology/approach – This research aims to develop a dynamic modelling approach using Discrete Event Simulation (DES) to assess such maintenance systems in order to provide a better understanding of the behaviour of complex maintenance operations. Interviews were conducted and literature was analysed to gather modelling requirements. Generic models were created, followed by simulation models, to examine how maintenance operation systems behave regarding different levels of asset monitoring. Findings – This research indicates that DES discerns varying levels of complexity of maintenance operations but that more sophisticated asset monitoring levels will not necessarily result in a higher asset performance. The paper shows that it is possible to assess the impact of monitoring levels as well as make other changes to system operation that may be more or less effective. Practical implications – The proposed tool supports the maintenance operations decision makers to select the appropriate asset monitoring level that suits their operational needs. Originality/value – A novel DES approach was developed to assess asset monitoring levels for maintenance operations. In applying this quantitative approach, it was demonstrated that higher asset monitoring levels do not necessarily result in higher asset availability. The work provides a means of evaluating the constraints in the system that an asset is part of rather than focusing on the asset in isolation.

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Antibiotic Resistance Profiling ofStaphylococcus aureusIsolated from Clinical Specimens in a Tertiary Hospital from 2010 to 2012

Interdisciplinary Perspectives on Infectious Diseases ◽

10.1155/2014/898457 ◽

2014 ◽

Vol 2014 ◽

pp. 1-4 ◽

Cited By ~ 8

Author(s):

Alain C. Juayang ◽

Gemma B. de los Reyes ◽

April Joy G. de la Rama ◽

Christine T. Gallega

Keyword(s):

Health Care ◽

Tertiary Hospital ◽

Drug Susceptibility ◽

Health Care Facilities ◽

Penicillin G ◽

Inducible Clindamycin Resistance ◽

Clinical Specimens ◽

Drug Of Choice ◽

Care Facilities ◽

Mrsa Infection

MRSA infection can affect a wide array of individuals that may lead to treatment failure. Also, the infection has the potential to spread from one area to another particularly health care facilities or communities eventually causing minor outbreaks. With this premise, the study aimed to describe MRSA infections using the hospital-based data of a tertiary hospital in Bacolod City, Philippines, from 2010 to 2012. Specifically, this study aimed to evaluate the antimicrobial resistance ofS. aureusisolated from clinical specimens and to put emphasis on the prevalence of MRSA and Inducible Clindamycin Resistance. A total of 94 cases from 2010 to 2012 were diagnosed to haveS. aureusinfection using conventional bacteriologic methods. From these cases, 38 (40.6%) were identified as MRSA and 37 (39.4%) were inducible clindamycin resistant. Wounds and abscesses were considered to be the most common specimens with MRSA infections having 71.05% while blood was the least with 5.3%. For drug susceptibility, out of the 94S. aureuscases, including MRSA, 100% were susceptible to linezolid making it the drug of choice for this study. It was then followed by tetracycline having a mean susceptibility of 95%;, while penicillin G was ineffective with 94 cases having 0% susceptibility.

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Time from triage to initial physician assessment: A 5-year retrospective analysis

Emergency Care Journal ◽

10.4081/ecj.2019.8307 ◽

2019 ◽

Vol 15 (3) ◽

Author(s):

Mor Saban ◽

Nadav Armoni ◽

Heli Patito ◽

Tal Shachar ◽

Aziz Darawsha ◽

...

Keyword(s):

Waiting Times ◽

Tertiary Hospital ◽

Time Lags ◽

Physician Assessment ◽

Evaluation Time ◽

Patient Arrival ◽

Emergency Department Triage ◽

Urgency Rating ◽

Physician Adherence ◽

Triage Classification

The aim of the study is to examine whether physicians adhere to the urgency classification as determined by the Canadian Triage and Acuity Scale. A retrospective-archive study was conducted in a tertiary hospital from January 2011 to December 2015. For each patient, we examined the relation between the urgency rating set by the triage nurse and the waiting time for the physician. Additionally, we explored the relationships between waiting times for physicians and several subgroups: patient arrival time, season of the year, assigned care area, and first consultant to examine the patient, using Analysis of Variance (ANOVA) analysis. There were 392,687 unique visits during the study period. The distribution of the classification was heterogeneous: 7,133 (1.8%) patients were classified as Priority (P) P1; 17,318 as P2 (4.4%); 148,657 as P3 (37.8%); 113,502 as P4 (28.9%); and 106,077 as P5 (27%). Median and interquartile ranges for time from triage until physician assessment, by triage group, were: P1, 0.7 minutes (0.2-24); P2, 35 minutes (13-76); P3, 44 minutes (21-88); P4, 45 minutes (20-87); and P5, 46 minutes (22-88). Percentages of visits that met the evaluation time goals, by triage classification, were: P1, 61%; P2, 27%; P3, 37%; P4, 61%; and P5, 85%. ANOVA test for the four subgroups revealed statistically significant differences (P<.001). In conclusion, the standard goals for time to physician evaluation are not being met, and there is little difference in time to evaluation between the P3, P4, and P5 classifications. Initiation of system-wide changes in physician workflow and awareness may improve physician adherence to triage classification, shorten time lags, and improve patient evaluation. Further research may allow for better understanding of the factors influencing triage adherence and reinforce teamwork among Emergency Department triage nurses and physicians.

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