The science of surge

Measures and Impact of Caseload Surge During the COVID-19 Pandemic: A Systematic Review

OBJECTIVES

COVID-19 pandemic surges strained hospitals globally. We performed a systematic review to examine measures of pandemic caseload surge and its impact on mortality of hospitalized patients.

DATA SOURCES

PubMed, Embase, and Web of Science.

STUDY SELECTION

English-language studies published between December 1, 2019, and November 22, 2023, which reported the association between pandemic "surge"-related measures and mortality in hospitalized patients.

DATA EXTRACTION

Three authors independently screened studies, extracted data, and assessed individual study risk of bias. We assessed measures of surge qualitatively across included studies. Given multidomain heterogeneity, we semiquantitatively aggregated surge-mortality associations.

DATA SYNTHESIS

Of 17,831 citations, we included 39 studies, 17 of which specifically described surge effects in ICU settings. The majority of studies were from high-income countries ( n = 35 studies) and included patients with COVID-19 ( n = 31). There were 37 different surge metrics which were mapped into four broad themes, incorporating caseloads either directly as unadjusted counts ( n = 11), nested in occupancy ( n = 14), including additional factors (e.g., resource needs, speed of occupancy; n = 10), or using indirect proxies (e.g., altered staffing ratios, alternative care settings; n = 4). Notwithstanding metric heterogeneity, 32 of 39 studies (82%) reported detrimental adjusted odds/hazard ratio for caseload surge-mortality outcomes, reporting point estimates of up to four-fold increased risk of mortality. This signal persisted among study subgroups categorized by publication year, patient types, clinical settings, and country income status.

CONCLUSIONS

Pandemic caseload surge was associated with lower survival across most studies regardless of jurisdiction, timing, and population. Markedly variable surge strain measures precluded meta-analysis and findings have uncertain generalizability to lower-middle-income countries (LMICs). These findings underscore the need for establishing a consensus surge metric that is sensitive to capturing harms in everyday fluctuations and future pandemics and is scalable to LMICs.

Understanding Pediatric Surge in the United States

The concepts of pediatric surge in the United States continue to evolve from a theoretic framework to practical implementation. As disasters become more frequent, ranging from natural to human-caused, children remain a vulnerable population. The coronavirus disease 2019 pandemic and the 2022 to 2023 tripledemic respiratory surge revealed advances and continued challenges in our ability to care for a large influx of pediatric patients. Understanding pediatric surge through the framework of the 4 S's (space, staff, stuff, and systems/structures) can identify gaps at multiple levels.

The 4 S's of Disaster Management Framework: A Case Study of the 2022 Pediatric Tripledemic Response in a Community Hospital

Most children in the United States present to community hospitals for emergency department (ED) care. Those who are acutely ill and require critical care are stabilized and transferred to a tertiary pediatric hospital with intensive care capabilities. During the fall of 2022 "tripledemic," with a marked increase in viral burden, there was a nationwide surge in pediatric ED patient volume. This caused ED crowding and decreased availability of pediatric hospital intensive care beds across the United States. As a result, there was an inability to transfer patients who were critically ill out, and the need for prolonged management increased at the community hospital level. We describe the experience of a Massachusetts community ED during this surge, including the large influx in pediatric patients, the increase in those requiring critical care, and the total number of critical care hours as compared with the same time period (September to December) in 2021. To combat these challenges, the pediatric ED leadership applied a disaster management framework based on the 4 S's of space, staff, stuff, and structure. We worked collaboratively with general emergency medicine leadership, nursing, respiratory therapy, pharmacy, local clinicians, our regional health care coalition, and emergency medical services (EMS) to create and implement the pediatric surge strategy. Here, we present the disaster framework strategy, the interventions employed, and the barriers and facilitators for implementation in our community hospital setting, which could be applied to other community hospital facing similar challenges.

Retrospective charts for reporting, analysing, and evaluating disaster emergency response: a systematic review

OBJECTIVE

Given the frequency of disasters worldwide, there is growing demand for efficient and effective emergency responses. One challenge is to design suitable retrospective charts to enable knowledge to be gained from disasters. This study provides comprehensive understanding of published retrospective chart review templates for designing and updating retrospective research.

METHODS

We conducted a systematic review and text analysis of peer-reviewed articles and grey literature on retrospective chart review templates for reporting, analysing, and evaluating emergency responses. The search was performed on PubMed, Cochrane, and Web of Science and pre-identified government and non-government organizational and professional association websites to find papers published before July 1, 2022. Items and categories were grouped and organised using visual text analysis. The study is registered in PROSPERO (374,928).

RESULTS

Four index groups, 12 guidelines, and 14 report formats (or data collection templates) from 21 peer-reviewed articles and 9 grey literature papers were eligible. Retrospective tools were generally designed based on group consensus. One guideline and one report format were designed for the entire health system, 23 studies focused on emergency systems, while the others focused on hospitals. Five papers focused specific incident types, including chemical, biological, radiological, nuclear, mass burning, and mass paediatric casualties. Ten papers stated the location where the tools were used. The text analysis included 123 categories and 1210 specific items; large heterogeneity was observed.

CONCLUSION

Existing retrospective chart review templates for emergency response are heterogeneous, varying in type, hierarchy, and theoretical basis. The design of comprehensive, standard, and practicable retrospective charts requires an emergency response paradigm, baseline for outcomes, robust information acquisition, and among-region cooperation.

Factors causing emergency medical care overload during heatwaves: A Delphi study

Heatwaves pose an important risk for population health and are associated with an increased demand for emergency care. To find factors causing such overload, an online Delphi study included 15 experts in emergency medicine, disaster medicine, or public health. One open-ended question was delivered in the first round. After content analysis, the obtained statements were sent to the experts in two rounds to be rated on a 7-point linear scale. Consensus was defined as a standard deviation ≤ 1.0. Thirty-one statements were obtained after content analysis. The experts agreed on 18 statements, mostly focusing on the input section of patient processing and identifying stakeholders, the population, and primary care as targets of potential interventions. Additional dedicated resources and bed capacity were deemed important as per throughput and output sections, respectively. These findings could be used in the future to implement and test solutions to increase emergency healthcare resilience during heatwaves and reduce disaster risk due to climatic change.

Hospital surge capacity preparedness in disasters and emergencies: a systematic review

BACKGROUND

Adequate and effective emergency preparedness for hospital surge capacity is a prerequisite to ensuring standard healthcare services for disaster victims. This study aimed to identify, review, and synthesize the preparedness activities for and the barriers to hospital surge capacity in disasters and emergencies.

METHODS

We systematically searched seven databases (PubMed, MEDLINE, CINAHL, Scopus, Embase, Ovid, and PsycINFO). We included all English peer-reviewed studies published in January 2016 and July 2022 on surge capacity preparedness in hospital settings. Two independent researchers screened titles and abstracts, reviewed the full texts, and conducted data extractions using CADIMA software. We assessed the rigor of the included studies using the NIH quality assessment tools for quantitative studies, the Noyes et al. guidelines for qualitative studies, and the MMAT tool for mixed methods studies and summarized findings using the narrative synthesis method. We also used PRISMA reporting guidelines.

RESULTS

From the 2560 studies identified, we finally include 13 peer-reviewed studies: 10 quantitative, one qualitative, and two mixed methods. Five studies were done in the USA, three in Iran (n = 3), and the remaining in Australia, Pakistan, Sweden, Taiwan, and Tanzania. The study identified various ways to increase hospital surge capacity preparedness in all four domains (staff, stuff, space, and system); among them, the use of the Hospital Medical Surge Preparedness Index and the Surge Simulation Tool for surge planning was noteworthy. Moreover, nine studies (69%) recognized several barriers to hospital surge capacity preparedness.

CONCLUSION

The review provides synthesized evidence of contemporary literature on strategies for and barriers to hospital surge capacity preparedness. Despite the risk of selection bias due to the omission of gray literature, the study findings could help hospital authorities, public health workers, and policymakers to develop effective plans and programs for improving hospital surge capacity preparedness with actions, such as enhancing coordination, new or adapted flows of patients, disaster planning implementation, or the development of specific tools for surge capacity.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42022360332.

Assessing healthcare capacity crisis preparedness: development of an evaluation tool by a Canadian health authority

Introduction

The COVID-19 pandemic presented health systems across the globe with unparalleled socio-political, ethical, scientific, and economic challenges. Despite the necessity for a unified, innovative, and effective response, many jurisdictions were unprepared to such a profound health crisis. This study aims to outline the creation of an evaluative tool designed to measure and evaluate the Vitalité Health Network's (New Brunswick, Canada) ability to manage health crises.

Methods

The methodology of this work was carried out in four stages: (1) construction of an evaluative framework; (2) validation of the framework; (3) construction of the evaluative tool for the Health Authority; and (4) evaluation of the capacity to manage a health crisis.

Results

The resulting evaluative tool incorporated 8 dimensions, 74 strategies, and 109 observable elements. The dimensions included: (1) clinical care management; (2) infection prevention and control; (3) governance and leadership; (4) human and logistic resources; (5) communication and technologies; (6) health research; (7) ethics and values; and (8) training. A Canadian Health Authority implemented the tool to support its future preparedness.

Conclusion

This study introduces a methodological strategy adopted by a Canadian health authority to evaluate its capacity in managing health crises. Notably, this study marks the first instance where a Canadian health authority has created a tool for emergency healthcare management, informed by literature in the field and their direct experience from handling the SARS-CoV-2 pandemic.

A Multisite Investigation of Areas for Improvement in COVID-19 Surge Capacity Management

The congressionally authorized National Disaster Medical System Pilot Program was created in December 2019 to strengthen the medical surge capability, capacity, and interoperability of affiliated healthcare facilities in 5 regions across the United States. The COVID-19 pandemic provided an unprecedented opportunity to learn how participating healthcare facilities handled medical surge events during an active public health emergency. We applied a modified version of the Barbisch and Koenig 4-S framework (staff, stuff, space, systems) to analyze COVID-19 surge management practices implemented by healthcare stakeholders at 5 pilot sites. In total, 32 notable practices were identified to increase surge capacity during the COVID-19 pandemic that have potential applications for other healthcare facilities. We found that systems was the most prevalent domain of surge capacity among the identified practices. Systems and staff were discussed across all 5 pilot sites and were the 2 domains co-occurring most often within each surge management practice. These results can inform strategies for scaling up and optimizing medical surge capability, capacity, and interoperability of healthcare facilities nationwide. This study also specifies areas of surge capacity worthy of strategic focus in the pilot's planning and implementation efforts while more broadly informing the US healthcare system's response to future large-scale, medical surge events.

Evaluating Efficacy of a COVID-19 Alternative Care Site Preparedness Assessment Tool for Catastrophic Healthcare Surge Capacity during Pandemic Response

During the COVID-19 pandemic, implementing catastrophic healthcare surge capacity required a network of facility infrastructure beyond the immediate hospital to triage the rapidly growing numbers of infected individuals and treat emerging disease cases. Providing regional continuity-of-care requires an assessment of buildings for alternative care sites (ACS) to extend healthcare operations into non-healthcare settings. The American Institute of Architects (AIA) appointed a COVID-19 ACS Task Force involving architects, engineers, public health, and healthcare professionals to conduct a charrette (i.e., intensive workshop) to establish guidance during the alert phase of the pandemic. The task force developed an ACS Preparedness Assessment Tool (PAT) for healthcare teams to assist with their rapid evaluation of building sites for establishing healthcare operations in non-healthcare settings. The tool was quickly updated (V2.0) and then translated into multiple languages. Subsequently, the authors of this manuscript reviewed the efficacy of the PAT V2.0 in the context of reported case studies from healthcare teams who developed a COVID-19 ACS in community settings. In summary, policy makers should re-examine the role of the built environment during emergency pandemic response and its impact on patients and health professionals. An updated ACS PAT tool should be established as part of the public health preparedness for implementing catastrophic healthcare surge capacity.

Is there an association between hospital staffing levels and inpatient-COVID-19 mortality rates?

Objective

This study aims to investigate the relationship between RNs and hospital-based medical specialties staffing levels with inpatient COVID-19 mortality rates.

Methods

We relied on data from AHA Annual Survey Database, Area Health Resource File, and UnitedHealth Group Clinical Discovery Database. In phase 1 of the analysis, we estimated the risk-standardized event rates (RSERs) based on 95,915 patients in the UnitedHealth Group Database 1,398 hospitals. We then used beta regression to analyze the association between hospital- and county- level factors with risk-standardized inpatient COVID-19 mortality rates from March 1, 2020, through December 31, 2020.

Results

Higher staffing levels of RNs and emergency medicine physicians were associated with lower COVID-19 mortality rates. Moreover, larger teaching hospitals located in urban settings had higher COVID-19 mortality rates. Finally, counties with greater social vulnerability, specifically in terms of housing type and transportation, and those with high infection rates had the worst patient mortality rates.

Conclusion

Higher staffing levels are associated with lower inpatient mortality rates for COVID-19 patients. More research is needed to determine appropriate staffing levels and how staffing levels interact with other factors such as teams, leadership, and culture to impact patient care during pandemics.

Hospital Surge Capacity Preparedness in Disasters and Emergencies: Protocol for a Systematic Review

Hospitals' medical surge preparedness or surge capacity preparedness plays a significant role in reducing mortalities and in the treatment of severe injuries in disasters and emergencies. Though actions or activities for surge capacity preparedness of hospitals are discussed in several studies, they remain fragmented and need to be compiled. This systematic review will provide a comprehensive synthesis of evidence of actions or steps taken to strengthen hospitals' medical surge preparedness in disasters and emergencies, which will eventually help develop surge capacity programs and relevant policies. All the studies published in peer-reviewed journals between 1 January 2016 and 30 July 2022, with full text available, will be included in this review. Seven electronic databases-PubMed, Scopus, MEDLINE, CINAHL, Embase, PsycINFO, and Ovid-will be searched. Two reviewers will independently screen the titles and abstracts using the eligibility criteria, review full-text articles, and extract data with the help of CADIMA software. A third reviewer will help resolve any discrepancies during the whole process. The extracted data will be narratively synthesized with the key characteristics and findings of the studies. The NIH quality assessment tools will be used to scale up the the quality of the retrieved quantitative studies. Moreover, the mixed methods appraisal tool (MMAT) and Noyes et al. guidelines will be used to assess the mixed methods studies and qualitative studies quality assessment, respectively.

A method for detailed determination of hospital surge capacity: a prerequisite for optimal preparedness for mass-casualty incidents

BACKGROUND

Defined goals for hospitals' ability to handle mass-casualty incidents (MCI) are a prerequisite for optimal planning as well as training, and also as base for quality assurance and improvement. This requires methods to test individual hospitals in sufficient detail to numerically determine surge capacity for different components of the hospitals. Few such methods have so far been available. The aim of the present study was with the use of a simulation model well proven and validated for training to determine capacity-limiting factors in a number of hospitals, identify how these factors were related to each other and also possible measures for improvement of capacity.

MATERIALS AND METHODS

As simulation tool was used the MACSIM® system, since many years used for training in the international MRMI courses and also successfully used in a pilot study of surge capacity in a major hospital. This study included 6 tests in three different hospitals, in some before and after re-organisation, and in some both during office- and non-office hours.

RESULTS

The primary capacity-limiting factor in all hospitals was the capacity to handle severely injured patients (major trauma) in the emergency department. The load of such patients followed in all the tests a characteristic pattern with "peaks" corresponding to ambulances return after re-loading. Already the first peak exceeded the hospitals capacity for major trauma, and the following peaks caused waiting times for such patients leading to preventable mortality according to the patient-data provided by the system. This emphasises the need of an immediate and efficient coordination of the distribution of casualties between hospitals. The load on surgery came in all tests later, permitting either clearing of occupied theatres (office hours) or mobilising staff (non-office hours) sufficient for all casualties requiring immediate surgery. The final capacity-limiting factors in all tests was the access to intensive care, which also limited the capacity for surgery. On a scale 1-10, participating staff evaluated the accuracy of the methodology for test of surge capacity to MD 8 (IQR 2), for improvement of disaster plans to MD 9 (IQR 2) and for simultaneous training to MD 9 (IQR 3).

CONCLUSIONS

With a simulation system including patient data with a sufficient degree of detail, it was possible to identify and also numerically determine the critical capacity-limiting factors in the different phases of the hospital response to MCI, to serve as a base for planning, training, quality control and also necessary improvement to rise surge capacity of the individual hospital.

Using a national level cross-sectional study to develop a Hospital Preparedness Index (HOSPI) for Covid-19 management: A case study from India

Background

We developed a composite index–hospital preparedness index (HOSPI)–to gauge preparedness of hospitals in India to deal with COVID-19 pandemic.

Methods

We developed and validated a comprehensive survey questionnaire containing 63 questions, out of which 16 critical items were identified and classified under 5 domains: staff preparedness, effects of COVID-19, protective gears, infrastructure, and future planning. Hospitals empaneled under Ayushman Bharat Yojana (ABY) were invited to the survey. The responses were analyzed using weighted negative log likelihood scores for the options. The preparedness of hospitals was ranked after averaging the scores state-wise and district-wise in select states. HOSPI scores for states were classified using K-means clustering.

Findings

Out of 20,202 hospitals empaneled in ABY included in the study, a total of 954 hospitals responded to the questionnaire by July 2020. Domains 1, 2, and 4 contributed the most to the index. The overall preparedness was identified as the best in Goa, and 12 states/ UTs had scores above the national average score. Among the states which experienced high COVID-19 cases during the first pandemic wave, we identified a cluster of states with high HOSPI scores indicating better preparedness (Maharashtra, Tamil Nadu, Karnataka, Uttar Pradesh and Andhra Pradesh), and a cluster with low HOSPI scores indicating poor preparedness (Chhattisgarh, Delhi, Uttarakhand).

Interpretation

Using this index, it is possible to identify areas for targeted improvement of hospital and staff preparedness to deal with the COVID-19 crisis.

Identifying indicators influencing emergency department performance during a medical surge: A consensus-based modified fuzzy Delphi approach

During a medical surge, resource scarcity and other factors influence the performance of the healthcare systems. To enhance their performance, hospitals need to identify the critical indicators that affect their operations for better decision-making. This study aims to model a pertinent set of indicators for improving emergency departments' (ED) performance during a medical surge. The framework comprises a three-stage process to survey, evaluate, and rank such indicators in a systematic approach. The first stage consists of a survey based on the literature and interviews to extract quality indicators that impact the EDs' performance. The second stage consists of forming a panel of medical professionals to complete the survey questionnaire and applying our proposed consensus-based modified fuzzy Delphi method, which integrates text mining to address the fuzziness and obtain the sentiment scores in expert responses. The final stage ranks the indicators based on their stability and convergence. Here, twenty-nine potential indicators are extracted in the first stage, categorized into five healthcare performance factors, are reduced to twenty consentaneous indicators monitoring ED's efficacy. The Mann-Whitney test confirmed the stability of the group opinions (p < 0.05). The agreement percentage indicates that ED beds (77.8%), nurse staffing per patient seen (77.3%), and length of stay (75.0%) are among the most significant indicators affecting the ED's performance when responding to a surge. This research proposes a framework that helps hospital administrators determine essential indicators to monitor, manage, and improve the performance of EDs systematically during a surge event.

Health and Healthcare Variables Associated with Italy's Excess Mortality during the First Wave of the COVID-19 pandemic: An Ecological Study

Background

Healthcare factors have strongly influenced the propagation of COVID-19. This study aims to examine whether excess mortality during the first phase of the COVID-19 outbreak in Italy was associated with health, healthcare, demographic, and socioeconomic, provincial-level indicators.

Methods

This ecological study concerns the raw number of deaths reported from February 1 to April 30, 2020 and the mean number of deaths occurred during the same months from 2015 to 2019, per province. Information on socioeconomic factors and healthcare settings was extracted from updated databases on the Italian National Institute of Statistics (ISTAT) website. A multivariate model and four multilevel models were constructed to test the association between excess mortality and the analysed indicators across 107 Italian provinces.

Results

The hospitalization rate in long-term care wards and the cardiovascular disease mortality rate correlate positively with excess mortality (p <0.05), while higher densities of licensed physicians and of general practitioners are associated with lower excess mortality (p <0.05). After controlling for the COVID-19 cumulative incidence in each province, only the density of licensed physicians remains negatively associated with excess mortality (p <0.01).

Conclusion

Some health and healthcare variables (in particular, the density of physicians) are strongly associated with excess mortality during the first wave of the COVID-19 pandemic in Italy and should be targeted to increase the resilience of health systems.

The Application of a Hospital Medical Surge Preparedness Index to Assess National Pandemic and Other Mass Casualty Readiness

EXECUTIVE SUMMARY

This article describes the use and findings of the Hospital Medical Surge Preparedness Index (HMSPI) tool to improve the understanding of hospitals' ability to respond to mass casualty events such as the COVID-19 pandemic. For this investigation, data from the U.S. Census Bureau, the Dartmouth Atlas Project, and the 2005 to 2014 annual surveys of the American Hospital Association (AHA) were analyzed. The HMSPI tool uses variables from the AHA survey and the other two sources to allow facility, county, and referral area index calculations. Using the three data sets, the HMSPI also allows for an index calculation for per capita ratios and by political (state or county) boundaries. In this use case, the results demonstrated increases in county and state HMSPI scores through the period of analysis; however, no statistically significant difference was found in HMSPI scores between 2013 and 2014. The HMSPI builds on the limited scientific foundation of medical surge preparedness and could serve as an objective and standardized measure to assess the nation's medical readiness for crises such as the COVID-19 pandemic and other large-scale emergencies such as mass shootings. Future studies are encouraged to refine the score, assess the validity of the HMSPI, and evaluate its relevance in response to future legislative and executive policies that affect preparedness measures.

Preparing for the Maximum Emergency with a Simulation: A Table-Top Test to Evaluate Bed Surge Capacity and Staff Compliance with Training

Introduction

The sudden increase in the number of critically ill patients following a disaster can be overwhelming.

Study Objective

The main objective of this study was to assess the real number of available and readily freeable beds (“bed surge capacity”) and the availability of emergency operating rooms (OR) in a maximum emergency using a theoretical simulation.

Patients and Methods

The proportion of dismissible patients in four areas (Medical Area, Surgical Area, Sub-intensive Care Units, Intensive Care Units) and three emergency OR was assessed at 2 and 24 hours after a simulated maximum emergency. Four scenarios were modeled. Hospitalization and surgical capacities were assessed on weekdays and holidays. The creation of new beds was presumed by the possibility of moving patients to a lower level of care than that provided at the time of detection, of dislocation of patients to a discharge room, with care transferred to lower-intensity hospitals, rehabilitation, or discharge facilities. The Phase 1 table-top simulations were conducted during the weekday morning hours. In particular, the 24-hour table-top simulations of a hypothetical event lasted about 150 minutes compared to those conducted at 2 hours, which were found to be longer (about 195 minutes). Phase 2 was conducted on two public holidays and a quick response time was observed within the first 40 minutes of the start of the test (about 45% of departments).

Results

The availability of simulated beds was greater than that indicated in the maximum emergency plans (which was based solely on the census of beds). Patients admitted to Intensive Care and The Sub-Intensive Area may be more difficult to move than those in low-intensity care. The availability of emergency OR was not problematic. Age influenced the possibility of remitting/transferring patients.

Conclusion

Simulation in advance of a maximum emergency is helpful in designing an efficient response plan.

Predicted Effects of Stopping COVID-19 Lockdown on Italian Hospital Demand

Objectives:

Italy has been one of the first countries to implement mitigation measures to curb the coronavirus disease 2019 (COVID-19) pandemic. There is currently a debate on when and how such measures should be loosened. To forecast the demand for hospital intensive care unit (ICU) and non-ICU beds for COVID-19 patients from May to September, we developed 2 models, assuming a gradual easing of restrictions or an intermittent lockdown.

Methods:

We used a compartmental model to evaluate 2 scenarios: (A) an intermittent lockdown; (B) a gradual relaxation of the lockdown. Predicted ICU and non-ICU demand was compared with the peak in hospital bed use observed in April 2020.

Results:

Under scenario A, while ICU demand will remain below the peak, the number of non-ICU will substantially rise and will exceed it (133%; 95% confidence interval [CI]: 94-171). Under scenario B, a rise in ICU and non-ICU demand will start in July and will progressively increase over the summer 2020, reaching 95% (95% CI: 71-121) and 237% (95% CI: 191-282) of the April peak.

Conclusions:

Italian hospital demand is likely to remain high in the next months. If restrictions are reduced, planning for the next several months should consider an increase in health-care resources to maintain surge capacity across the country.

Translating COVID-19 Pandemic Surge Theory to Practice in the Emergency Department: How to Expand Structure

Multiple professional societies, nongovernment and government agencies have studied the science of sudden onset disaster mass casualty incidents to create and promote surge response guidelines. The COVID-19 pandemic has presented the health-care system with challenges that have limited science to guide the staff, stuff, and structure surge response.This study reviewed the available surge science literature specifically to guide an emergency department's surge structural response using a translational science approach to answer the question: How does the concept of sudden onset mass casualty incident surge capability apply to the process to expand COVID-19 pandemic surge structure response?The available surge structural science literature was reviewed to determine the application to a pandemic response. The on-line ahead of print and print COVID-19 scientific publications, as well as gray literature were studied to learn the best available COVID-19 surge structural response science. A checklist was created to guide the emergency department team's COVID-19 surge structural response.

Development of a Hospital Medical Surge Preparedness Index using a national hospital survey

To generate a Hospital Medical Surge Preparedness Index that can be used to evaluate hospitals across the United States in regard to their capacity to handle patient surges during mass casualty events. Data from the American Hospital Association’s annual survey, conducted from 2005 to 2014. Our sample comprised 6239 hospitals across all 50 states, with an annual average of 5769 admissions. An extensive review of the American Hospital Association survey was conducted and relevant variables applicable to hospital inpatient services were extracted. Subject matter experts then categorized these items according to the following subdomains of the “Science of Surge” construct: staff, supplies, space, and system. The variables within these categories were then analyzed through exploratory and confirmatory factor analyses, concluding with the evaluation of internal reliability. Based on the combined results, we generated individual (by hospital) scores for each of the four metrics and an overall score. The exploratory factor analysis indicated a clustering of variables consistent with the “Science of Surge” subdomains, and this finding was in agreement with the statistics generated through the confirmatory factor analysis. We also found high internal reliability coefficients, with Cronbach’s alpha values for all constructs exceeding 0.9. A novel Hospital Medical Surge Preparedness Index linked to hospital metrics has been developed to assess a health care facility’s capacity to manage patients from mass casualty events. This index could be used by hospitals and emergency management planners to assess a facility’s readiness to provide care during disasters.

Could we employ the queueing theory to improve efficiency during future mass causality incidents?

Background

Preparation for a disaster or accident-related mass casualty events is often based on experience. The objective measures or tools for evaluating decision-making and effectiveness during such events are underdeveloped. Queueing theory has been suggested to evaluate the effectiveness of mass causality incidents (MCI) plans.

Objective

Using different types of real MCI, we aimed to determine if a queueing network model could be used as a tool to assist in preparing plans to address mass causality incidents.

Methods

We collected information from two types of mass casualty events: a motor vehicle accident and a dust explosion. Patient characteristics, time intervals of every working station, numbers of physicians and nurses attending, and time required by physicians and nurses during these two MCIs were collected and used for calculation in a queueing network model. Balanced efficiency was determined by calculating the numbers of server, i.e., nurses and physicians, in the two MCIs.

Results

Efficient patient flows were found in both MCIs. However, excessive medical manpower supply was revealed when the queueing network model was applied to assess the MCIs. The best fitting result, i.e., the most efficient man power utilization, can be calculated by the queueing network models. Furthermore, balanced efficiency may be a more suitable condition than the highest efficiency man power utilization when faced with MCIs.

Conclusion

The queueing network model is a flexible tool that could be used in different types of MCIs to observe the degree of efficiency when handling MCIs.

Electronic supplementary material

The online version of this article (10.1186/s13049-019-0620-8) contains supplementary material, which is available to authorized users.

Hospital Surge Capacity: A Web-Based Simulation Tool for Emergency Planners

The National Center for the Study of Preparedness and Catastrophic Event Response (PACER) has created a publicly available simulation tool called Surge (accessible at http://www.pacerapps.org) to estimate surge capacity for user-defined hospitals. Based on user input, a Monte Carlo simulation algorithm forecasts available hospital bed capacity over a 7-day period and iteratively assesses the ability to accommodate disaster patients. Currently, the tool can simulate bed capacity for acute mass casualty events (such as explosions) only and does not specifically simulate staff and supply inventory. Strategies to expand hospital capacity, such as (1) opening unlicensed beds, (2) canceling elective admissions, and (3) implementing reverse triage, can be interactively evaluated. In the present application of the tool, various response strategies were systematically investigated for 3 nationally representative hospital settings (large urban, midsize community, small rural). The simulation experiments estimated baseline surge capacity between 7% (large hospitals) and 22% (small hospitals) of staffed beds. Combining all response strategies simulated surge capacity between 30% and 40% of staffed beds. Response strategies were more impactful in the large urban hospital simulation owing to higher baseline occupancy and greater proportion of elective admissions. The publicly available Surge tool enables proactive assessment of hospital surge capacity to support improved decision-making for disaster response. (Disaster Med Public Health Preparedness. 2018;12:513–522)

Identifying Factors That May Influence Decision-Making Related to the Distribution of Patients During a Mass Casualty Incident

OBJECTIVE

We aimed to identify and seek agreement on factors that may influence decision-making related to the distribution of patients during a mass casualty incident.

METHODS

A qualitative thematic analysis of a literature review identified 56 unique factors related to the distribution of patients in a mass casualty incident. A modified Delphi study was conducted and used purposive sampling to identify peer reviewers that had either (1) a peer-reviewed publication within the area of disaster management or (2) disaster management experience. In round one, peer reviewers ranked the 56 factors and identified an additional 8 factors that resulted in 64 factors being ranked during the two-round Delphi study. The criteria for agreement were defined as a median score greater than or equal to 7 (on a 9-point Likert scale) and a percentage distribution of 75% or greater of ratings being in the highest tertile.

RESULTS

Fifty-four disaster management peer reviewers, with hospital and prehospital practice settings most represented, assessed a total of 64 factors, of which 29 factors (45%) met the criteria for agreement.

CONCLUSIONS

Agreement from this formative study suggests that certain factors are influential to decision-making related to the distribution of patients during a mass casualty incident. (Disaster Med Public Health Preparedness. 2018;12:101-108).

Disaster Preparedness and Response for the Burn Mass Casualty Incident in the Twenty-first Century

The effective and efficient coordination of emergent patient care at the point of injury followed by the systematic resource-based triage of casualties are the most critical factors that influence patient outcomes after mass casualty incidents (MCIs). The effectiveness and appropriateness of implemented actions are largely determined by the extent and efficacy of the planning and preparation that occur before the MCI. The goal of this work was to define the essential efforts related to planning, preparation, and execution of acute and subacute medical care for disaster burn casualties. This type of MCI is frequently referred to as a burn MCI."

Development of the science of mass casualty incident management: reflection on the medical response to the Wenchuan earthquake and Hangzhou bus fire

OBJECTIVE

In this paper, we review the previous classic research paradigms of a mass casualty incident (MCI) systematically and reflect the medical response to the Wenchuan earthquake and Hangzhou bus fire, in order to outline and develop an improved research paradigm for MCI management.

METHODS

We searched PubMed, EMBASE, China Wanfang, and China Biology Medicine (CBM) databases for relevant studies. The following key words and medical subject headings were used: 'mass casualty incident', 'MCI', 'research method', 'Wenchuan', 'earthquake', 'research paradigm', 'science of surge', 'surge', 'surge capacity', and 'vulnerability'. Searches were performed without year or language restriction. After searching the four literature databases using the above listed key words and medical subject headings, related articles containing research paradigms of MCI, 2008 Wenchuan earthquake, July 5 bus fire, and science of surge and vulnerability were independently included by two authors.

RESULTS

The current progresses on MCI management include new golden hour, damage control philosophy, chain of survival, and three links theory. In addition, there are three evaluation methods (medical severity index (MSI), potential injury creating event (PICE) classification, and disaster severity scale (DSS)), which can dynamically assess the MCI situations and decisions for MCI responses and can be made based on the results of such evaluations. However, the three methods only offer a retrospective evaluation of MCI and thus fail to develop a real-time assessment of MCI responses. Therefore, they cannot be used as practical guidance for decision-making during MCI. Although the theory of surge science has made great improvements, we found that a very important factor has been ignored-vulnerability, based on reflecting on the MCI response to the 2008 Wenchuan earthquake and July 5 bus fire in Hangzhou.

CONCLUSIONS

This new paradigm breaks through the limitation of traditional research paradigms and will contribute to the development of a methodology for disaster research.

Pediatric Disposition Classification (Reverse Triage) System to Create Surge Capacity

BACKGROUND

Critically insufficient pediatric hospital capacity may develop during a disaster or surge event. Research is lacking on the creation of pediatric surge capacity. A system of "reverse triage," with early discharge of hospitalized patients, has been developed for adults and shows great potential but is unexplored in pediatrics.

METHODS

We conducted an evidence-based modified-Delphi consensus process with 25 expert panelists to derive a disposition classification system for pediatric inpatients on the basis of risk tolerance for a consequential medical event (CME). For potential validation, critical interventions (CIs) were derived and ranked by using a Likert scale to indicate CME risk should the CI be withdrawn or withheld for early disposition.

RESULTS

Panelists unanimously agreed on a 5-category risk-based disposition classification system. The panelists established upper limit (mean) CME risk for each category as <2% (interquartile range [IQR]: 1-2%); 7% (5-10%), 18% (10-20%), 46% (20-65%), and 72% (50-90%), respectively. Panelists identified 25 CIs with varying degrees of CME likelihood if withdrawn or withheld. Of these, 40% were ranked high risk (Likert scale mean ≥7) and 32% were ranked modest risk (≤3).

CONCLUSIONS

The classification system has potential for an ethically acceptable risk-based taxonomy for pediatric inpatient reverse triage, including identification of those deemed safe for early discharge during surge events.

Critical resources for hospital surge capacity: an expert consensus panel

Background: Hospital surge capacity (HSC) is dependent on the ability to increase or conserve resources. The hospital surge model put forth by the Agency for Healthcare Research and Quality (AHRQ) estimates the resources needed by hospitals to treat casualties resulting from 13 national planning scenarios. However, emergency planners need to know which hospital resource are most critical in order to develop a more accurate plan for HSC in the event of a disaster. Objective: To identify critical hospital resources required in four specific catastrophic scenarios; namely, pandemic influenza, radiation, explosive, and nerve gas. Methods: We convened an expert consensus panel comprised of 23 participants representing health providers (i.e., nurses and physicians), administrators, emergency planners, and specialists. Four disaster scenarios were examined by the panel. Participants were divided into 4 groups of five or six members, each of which were assigned two of four scenarios. They were asked to consider 132 hospital patient care resources- extracted from the AHRQ's hospital surge model- in order to identify the ones that would be critical in their opinion to patient care. The definition for a critical hospital resource was the following: absence of the resource is likely to have a major impact on patient outcomes, i.e., high likelihood of untoward event, possibly death. For items with any disagreement in ranking, we conducted a facilitated discussion (modified Delphi technique) until consensus was reached, which was defined as more than 50% agreement. Intraclass Correlation Coefficients (ICC) were calculated for each scenario, and across all scenarios as a measure of participant agreement on critical resources. For the critical resources common to all scenarios, Kruskal-Wallis test was performed to measure the distribution of scores across all scenarios. Results: Of the 132 hospital resources, 25 were considered critical for all four scenarios by more than 50% of the participants. The number of hospital resources considered to be critical by consensus varied from one scenario to another; 58 for the pandemic influenza scenario, 51 for radiation exposure, 41 for explosives, and 35 for nerve gas scenario. Intravenous crystalloid solution was the only resource ranked by all participants as critical across all scenarios. The agreement in ranking was strong in nerve agent and pandemic influenza (ICC= 0.7 in both), and moderate in explosives (ICC= 0.6) and radiation (ICC= 0.5). Conclusion: In four disaster scenarios, namely, radiation, pandemic influenza, explosives, and nerve gas scenarios; supply of as few as 25 common resources may be considered critical to hospital surge capacity. The absence of any these resources may compromise patient care. More studies are needed to identify critical hospital resources in other disaster scenarios.

Refining Surge Capacity: Conventional, Contingency, and Crisis Capacity

Health care facility surge capacity has received significant planning attention recently, but there is no commonly accepted framework for detailed, phased surge capacity categorization and implementation. This article proposes a taxonomy within surge capacity of conventional capacity (implemented in major mass casualty incidents and representing care as usually provided at the institution), contingency capacity (using adaptations to medical care spaces, staffing constraints, and supply shortages without significant impact on delivered medical care), and crisis capacity (implemented in catastrophic situations with a significant impact on standard of care). Suggested measurements used to gauge a quantifiable component of surge capacity and adaptive strategies for staff and supply challenges are proposed. The use of refined definitions of surge capacity as it relates to space, staffing, and supply concerns during a mass casualty incident may aid phased implementation of surge capacity plans at health care facilities and enhance the consistency of terminology and data collection between facilities and regions. (Disaster Med Public Health Preparedness. 2009;3(Suppl 1):S59–S67)

Health systems' "surge capacity": state of the art and priorities for future research

CONTEXT

Over the past decade, a number of high-impact natural hazard events, together with the increased recognition of pandemic risks, have intensified interest in health systems' ability to prepare for, and cope with, "surges" (sudden large-scale escalations) in treatment needs. In this article, we identify key concepts and components associated with this emerging research theme. We consider the requirements for a standardized conceptual framework for future research capable of informing policy to reduce the morbidity and mortality impacts of such incidents. Here our objective is to appraise the consistency and utility of existing conceptualizations of health systems' surge capacity and their components, with a view to standardizing concepts and measurements to enable future research to generate a cumulative knowledge base for policy and practice.

METHODS

A systematic review of the literature on concepts of health systems' surge capacity, with a narrative summary of key concepts relevant to public health.

FINDINGS

The academic literature on surge capacity demonstrates considerable variation in its conceptualization, terms, definitions, and applications. This, together with an absence of detailed and comparable data, has hampered efforts to develop standardized conceptual models, measurements, and metrics. Some degree of consensus is evident for the components of surge capacity, but more work is needed to integrate them. The overwhelming concentration in the United States complicates the generalizability of existing approaches and findings.

CONCLUSIONS

The concept of surge capacity is a useful addition to the study of health systems' disaster and/or pandemic planning, mitigation, and response, and it has far-reaching policy implications. Even though research in this area has grown quickly, it has yet to fulfill its potential to generate knowledge to inform policy. Work is needed to generate robust conceptual and analytical frameworks, along with innovations in data collection and methodological approaches that enhance health systems' readiness for, and response to, unpredictable high-consequence surges in demand.

Design of a model to predict surge capacity bottlenecks for burn mass casualties at a large academic medical center

OBJECTIVES

To design and test a model to predict surge capacity bottlenecks at a large academic medical center in response to a mass-casualty incident (MCI) involving multiple burn victims.

METHODS

Using the simulation software ProModel, a model of patient flow and anticipated resource use, according to principles of disaster management, was developed based upon historical data from the University Hospital of the University of Michigan Health System. Model inputs included: (a) age and weight distribution for casualties, and distribution of size and depth of burns; (b) rate of arrival of casualties to the hospital, and triage to ward or critical care settings; (c) eligibility for early discharge of non-MCI inpatients at time of MCI; (d) baseline occupancy of intensive care unit (ICU), surgical step-down, and ward; (e) staff availability-number of physicians, nurses, and respiratory therapists, and the expected ratio of each group to patients; (f) floor and operating room resources-anticipating the need for mechanical ventilators, burn care and surgical resources, blood products, and intravenous fluids; (g) average hospital length of stay and mortality rate for patients with inhalation injury and different size burns; and (h) average number of times that different size burns undergo surgery. Key model outputs include time to bottleneck for each limiting resource and average waiting time to hospital bed availability.

RESULTS

Given base-case model assumptions (including 100 mass casualties with an inter-arrival rate to the hospital of one patient every three minutes), hospital utilization is constrained within the first 120 minutes to 21 casualties, due to the limited number of beds. The first bottleneck is attributable to exhausting critical care beds, followed by floor beds. Given this limitation in number of patients, the temporal order of the ensuing bottlenecks is as follows: Lactated Ringer's solution (4 h), silver sulfadiazine/Silvadene (6 h), albumin (48 h), thrombin topical (72 h), type AB packed red blood cells (76 h), silver dressing/Acticoat (100 h), bismuth tribromophenate/Xeroform (102 h), and gauze bandage rolls/Kerlix (168 h). The following items do not precipitate a bottleneck: ventilators, topical epinephrine, staplers, foams, antimicrobial non-adherent dressing/Telfa types A, B, or O blood. Nurse, respiratory therapist, and physician staffing does not induce bottlenecks.

CONCLUSIONS

This model, and similar models for non-burn-related MCIs, can serve as a real-time estimation and management tool for hospital capacity in the setting of MCIs, and can inform supply decision support for disaster management.

Availability of a pediatric trauma center in a disaster surge decreases triage time of the pediatric surge population: a population kinetics model

Background

The concept of disaster surge has arisen in recent years to describe the phenomenon of severely increased demands on healthcare systems resulting from catastrophic mass casualty events (MCEs) such as natural disasters and terrorist attacks. The major challenge in dealing with a disaster surge is the efficient triage and utilization of the healthcare resources appropriate to the magnitude and character of the affected population in terms of its demographics and the types of injuries that have been sustained.

Results

In this paper a deterministic population kinetics model is used to predict the effect of the availability of a pediatric trauma center (PTC) upon the response to an arbitrary disaster surge as a function of the rates of pediatric patients' admission to adult and pediatric centers and the corresponding discharge rates of these centers. We find that adding a hypothetical pediatric trauma center to the response documented in an historical example (the Israeli Defense Forces field hospital that responded to the Haiti earthquake of 2010) would have allowed for a significant increase in the overall rate of admission of the pediatric surge cohort. This would have reduced the time to treatment in this example by approximately half. The time needed to completely treat all children affected by the disaster would have decreased by slightly more than a third, with the caveat that the PTC would have to have been approximately as fast as the adult center in discharging its patients. Lastly, if disaster death rates from other events reported in the literature are included in the model, availability of a PTC would result in a relative mortality risk reduction of 37%.

Conclusions

Our model provides a mathematical justification for aggressive inclusion of PTCs in planning for disasters by public health agencies.

Creation of surge capacity by early discharge of hospitalized patients at low risk for untoward events

OBJECTIVES

US hospitals are expected to function without external aid for up to 96 hours during a disaster; however, concern exists that there is insufficient capacity in hospitals to absorb large numbers of acute casualties. The aim of the study was to determine the potential for creation of inpatient bed surge capacity from the early discharge (reverse triage) of hospital inpatients at low risk of untoward events for up to 96 hours.

METHODS

In a health system with 3 capacity-constrained hospitals that are representative of US facilities (academic, teaching affiliate, community), a variety (N = 50) of inpatient units were prospectively canvassed in rotation using a blocked randomized design for 19 weeks ending in February 2006. Intensive care units (ICUs), nurseries, and pediatric units were excluded. Assuming a disaster occurred on the day of enrollment, patients who did not require any (previously defined) critical intervention for 4 days were deemed suitable for early discharge.

RESULTS

Of 3491 patients, 44% did not require any critical intervention and were suitable for early discharge. Accounting for additional routine patient discharges, full use of staffed and unstaffed licensed beds, gross surge capacity was estimated at 77%, 95%, and 103% for the 3 hospitals. Factoring likely continuance of nonvictim emergency admissions, net surge capacity available for disaster victims was estimated at 66%, 71%, and 81%, respectively. Reverse triage made up the majority (50%, 55%, 59%) of surge beds. Most realized capacity was available within 24 to 48 hours.

CONCLUSIONS

Hospital surge capacity for standard inpatient beds may be greater than previously believed. Reverse triage, if appropriately harnessed, can be a major contributor to surge capacity.

A novel approach to multihazard modeling and simulation

OBJECTIVE

To develop and apply a novel modeling approach to support medical and public health disaster planning and response using a sarin release scenario in a metropolitan environment.

METHODS

An agent-based disaster simulation model was developed incorporating the principles of dose response, surge response, and psychosocial characteristics superimposed on topographically accurate geographic information system architecture. The modeling scenarios involved passive and active releases of sarin in multiple transportation hubs in a metropolitan city. Parameters evaluated included emergency medical services, hospital surge capacity (including implementation of disaster plan), and behavioral and psychosocial characteristics of the victims.

RESULTS

In passive sarin release scenarios of 5 to 15 L, mortality increased nonlinearly from 0.13% to 8.69%, reaching 55.4% with active dispersion, reflecting higher initial doses. Cumulative mortality rates from releases in 1 to 3 major transportation hubs similarly increased nonlinearly as a function of dose and systemic stress. The increase in mortality rate was most pronounced in the 80% to 100% emergency department occupancy range, analogous to the previously observed queuing phenomenon. Effective implementation of hospital disaster plans decreased mortality and injury severity. Decreasing ambulance response time and increasing available responding units reduced mortality among potentially salvageable patients. Adverse psychosocial characteristics (excess worry and low compliance) increased demands on health care resources. Transfer to alternative urban sites was possible.

CONCLUSIONS

An agent-based modeling approach provides a mechanism to assess complex individual and systemwide effects in rare events.

Emergency department surge: models and practical implications

BACKGROUND

Emergency Department crowding has long been described. Despite the daily challenges of managing emergency department volume and acuity; a surge response during a disaster entails even greater challenges including collaboration, intervention, and resourcefulness to effectively carry out pediatric disaster management. Understanding surge and how to respond with appropriate planning will lead to success. To achieve this, we sought to analyze models of surge; review regional and national data outlining surge challenges and factors that impact surge; and to outline potential solutions.

METHODS

We conducted a systemic review and included articles and documents that best described the theoretical and practical basis of surge response. We organized the systematic review according to the following questions: What are the elements and models that are delineated by the concept of surge? What is the basis for surge response based on regional and national published sources? What are the broad global solutions? What are the major lessons observed that will impact effective surge capacity?

RESULTS

Multiple models of surge are described including public health, facility-based and community-based; a 6-tiered response system; and intrinsic or extrinsic surge capacity. In addition, essential components (4 S's of surge response) are described along with regional and national data outlining surge challenges, impacting factors, global solutions, and lesions observed.

CONCLUSIONS

There are numerous shortcomings regionally and nationally affecting our ability to provide an effective and coordinated surge response. Planning, education, and training will lead to an effective pediatric disaster management response.

Use of "shuttered" hospitals to expand surge capacity

INTRODUCTION

With limited available hospital beds in most urban areas, there are very few options when trying to relocate patients already within the hospital to make room for incoming patients from a mass-casualty incident (MCI) or epidemic (a patient surge). This study investigates the possibility and process for utilizing shuttered (closed or former) hospitals to accept medically stable, ambulatory patients transferred from a tertiary medical facility.

METHODS

Two recently closed, acute care hospitals were evaluated critically to determine if they could be made ready to accept inpatients within 3-7 days of a MCI. This surge facility ideally would be able to support 200-300 patients/beds. Two generic scenarios were used for planning: (1) a patient surge (including one caused by conventional war or terrorism, weapons of mass destruction, or a disaster caused by natural hazards) requiring transfer of ambulatory, medically-stable inpatients to another facility in an effort to increase capacity at existing hospitals; and (2) a bio-event or epidemic where a shuttered hospital could be used as an isolation facility.

RESULTS

Both recently closed hospitals had significant, but different challenges to reopening, although with careful planning and resource allocation it would be possible to reopen them within 3-7 days. Planning was the most conclusive recommendation. It does not appear possible to reopen shuttered hospitals with major structural deterioration or a complete lack of current mission (i.e., no current utilities). Staffing would represent the most challenging issue as a surge facility would represent an incremental additional need for existing and scarce human resources.

CONCLUSIONS

With careful planning, a shuttered hospital could be reopened and ready to accept patients within 3-7 days of a MCI or epidemic.

The Surge Capacity for People in Emergencies (SCOPE) study in Australasian hospitals

OBJECTIVES

To measure physical assets in Australasian hospitals required for the management of mass casualties as a result of terrorism or natural disasters.

DESIGN AND SETTING

A cross-sectional survey of Australian and New Zealand hospitals.

PARTICIPANTS

All emergency department directors of Australasian College for Emergency Medicine (ACEM)-accredited hospitals, as well as private and non-ACEM accredited emergency departments staffed by ACEM Fellows in metropolitan Sydney.

MAIN OUTCOME MEASURES

Numbers of operating theatres, intensive care unit (ICU) beds and x-ray machines; state of preparedness using benchmarks defined by the Centers for Disease Control and Prevention in the United States.

RESULTS

We found that 61%-82% of critically injured patients would not have immediate access to operative care, 34%-70% would have delayed access to an ICU bed, and 42% of the less critically injured would have delayed access to x-ray facilities.

CONCLUSIONS

Our study demonstrates that physical assets in Australasian public hospitals do not meet US hospital preparedness benchmarks for mass casualty incidents. We recommend national agreement on disaster preparedness benchmarks and periodic publication of hospital performance indicators to enhance disaster preparedness.