Effect of a mass casualty incident: clinical outcomes and hospital charges for casualty patients versus concurrent inpatients

Effect of Coronavirus Disease 2019 (Covid-19), a Nationwide Mass Casualty Disaster on Intensive Care Units: Clinical Outcomes and Associated Cost-of-Care

OBJECTIVE

The COVID-19 pandemic resulted in millions of deaths worldwide and is considered a significant mass-casualty disaster (MCD). The surge of patients and scarcity of resources negatively impacted hospitals, patients and medical practice. We hypothesized ICUs during this MCD had a higher acuity of illness, and subsequently had increased lengths of stay (LOS), complication rates, death rates and costs of care. The purpose of this study was to investigate those outcomes.

METHODS

This was a multicenter, retrospective study that compared intensive care admissions in 2020 to those in 2019 to evaluate patient outcomes and cost of care. Data were obtained from the Vizient Clinical Data Base/Resource Manager (Vizient Inc., Irvine, Texas, USA).

RESULTS

Data included the number of ICU admissions, patient outcomes, case mix index and summary of cost reports. Quality outcomes were also collected, and a total of 1304981 patients from 333 hospitals were included. For all medical centers, there was a significant increase in LOS index, ICU LOS, complication rate, case mix index, total cost, and direct cost index.

CONCLUSION

The MCD caused by COVID-19 was associated with increased adverse outcomes and cost-of-care for ICU patients.

Registered nurses: can our supply meet the demand during a disaster?

BACKGROUND

Death and destructions are often reported during natural disasters; yet little is known about how hospitals operate during disasters and if there are sufficient resources available for hospitals to provide ongoing care during these catastrophic events. The purpose of this study was to determine if the State of New Jersey had a supply of registered nurses (RNs) that was sufficient to meet the needs of hospitalized patients during a natural disaster - Hurricane Sandy.

METHODS

Secondary data were used to forecast the demand and supply of New Jersey RNs during Hurricane Sandy. Data sources from November 2011 and 2012 included the State Inpatient Databases (SID), American Hospital Association (AHA) Annual Survey on hospital characteristics and staffing data from New Jersey Department of Health. Three models were used to estimate the RN shortage for each hospital, which was the difference between the demand and supply of RN full-time equivalents.

RESULTS

Data were available on 66 New Jersey hospitals, more than half of which experienced a shortage of RNs during Hurricane Sandy. For hospitals with a RN shortage in ICUs, a 20% increase in observed RN supply was needed to meet the demand; and a 10% increase in observed RN supply was necessary to meet the demand for hospitals with a RN shortage in non-ICUs.

CONCLUSION

Findings from this study suggest that many hospitals in New Jersey had a shortage of RNs during Hurricane Sandy. Efforts are needed to improve the availability of nurse resources during a natural disaster.

From intensive care to step-down units: Managing patients throughput in response to COVID-19

QUALITY PROBLEM OR ISSUE

The on-going COVID-19 pandemic may cause the collapse of healthcare systems because of unprecedented hospitalization rates.

INITIAL ASSESSMENT

A total of 8.2 individuals per 1000 inhabitants have been diagnosed with COVID-19 in our province. The hospital predisposed 110 beds for COVID-19 patients: on the day of the local peak, 90% of them were occupied and intensive care unit (ICU) faced unprecedented admission rates, fearing system collapse.

CHOICE OF SOLUTION

Instead of increasing the number of ICU beds, the creation of a step-down unit (SDU) close to the ICU was preferred: the aim was to safely improve the transfer of patients and to relieve ICU from the risk of overload.

IMPLEMENTATION

A nine-bed SDU was created next to the ICU, led by intensivists and ICU nurses, with adequate personal protective equipment, monitoring systems and ventilators for respiratory support when needed. A second six-bed SDU was also created.

EVALUATION

Patients were clinically comparable to those of most reports from Western Countries now available in the literature. ICU never needed supernumerary beds, no patient died in the SDU, and there was no waiting time for ICU admission of critical patients. SDU has been affordable from human resources, safety and economic points of view.

LESSONS LEARNED

COVID-19 is like an enduring mass casualty incident. Solutions tailored on local epidemiology and available resources should be implemented to preserve the efficiency and adaptability of our institutions and provide the adequate sanitary 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.

Barriers and facilitators to provide continuity of care to dischargeable patients in disasters: A qualitative study

OBJECTIVE

Early discharge of some in-patients is the effective measure to create hospital surge capacity in disasters. However, some of these patients may need to post-discharge continuity of care. The aim of the current study then is to explore the barriers of continuity of care, and to provide suitable solutions for potentially dischargeable patients during disasters.

METHODS

This qualitative study was conducted in Iran in 2017. The data was collected via unstructured interviews with 24 disaster professionals; and analyzed by content analysis method.

RESULTS

Identified barriers to the continuity of care were classified into seven categories, 'lack of disaster paradigm'; 'challenges of pre-hospital system'; 'insufficient coordination and cooperation'; 'inadequate hospital preparedness'; 'lack of using available resources and capacities'; 'poor patients' knowledge' and 'poor planning'. The suggested solutions for post-discharge continuity of care were: creation of registry and follow-up system; removing pre-hospital challenges; including disaster management courses in medical school curriculum; promoting hospital preparedness by All-Hazard Approach; and effective use of available resources.

CONCLUSION

Understanding the barriers to continuity of care for discharged patients for adopting policies based on experiences of health care providers can help planners to design and implement effective programs, which will enhance patients' access to necessary care.

All-Cause Hospital Admissions Among Older Adults After a Natural Disaster

STUDY OBJECTIVE

We characterize hospital admissions among older adults for any cause in the 30 days after a significant natural disaster in the United States. The main outcome was all-cause hospital admissions in the 30 days after natural disaster. Separate analyses were conducted to examine all-cause hospital admissions excluding the 72 hours after the disaster, ICU admissions, all-cause inhospital mortality, and admissions by state.

METHODS

A self-controlled case series analysis using the 2011 Medicare Provider and Analysis Review was conducted to examine exposure to natural disaster by elderly adults located in zip codes affected by tornadoes during the 2011 southeastern superstorm. Spatial data of tornado events were obtained from the National Oceanic and Atmospheric Administration's Severe Report database, and zip code data were obtained from the US Census Bureau.

RESULTS

All-cause hospital admissions increased by 4% for older adults in the 30 days after the April 27, 2011, tornadoes (incidence rate ratio 1.04; 95% confidence interval 1.01 to 1.07). When the first 3 days after the disaster that may have been attributed to immediate injuries were excluded, hospitalizations for any cause also remained higher than when compared with the other 11 months of the year (incidence rate ratio 1.04; 95% confidence interval 1.01 to 1.07). There was no increase in ICU admissions or inhospital mortality associated with the natural disaster. When data were examined by individual states, Alabama, which had the highest number of persons affected, had a 9% increase in both hospitalizations and ICU admissions.

CONCLUSION

When all time-invariant characteristics were controlled for, this natural disaster was associated with a significant increase in all-cause hospitalizations. This analysis quantifies acute care use after disasters through examining all-cause hospitalizations and represents an important contribution to building models of resilience-the ability to recover from a disaster-and hospital surge capacity.

Disaster Planning: Financing a Burn Disaster, Where Do You Turn and What Are Your Options When Your Hospital Has Been Impacted by a Burn Disaster in the United States?

The cost associated with a single burn injured patient can be significant. The American healthcare system functions in part based on traditional market forces which include supply and demand. In addition, there are a variety of payer sources with disparate payment for the same services. Thus, when a group of patients with serious injuries needing complicated care are underinsured or uninsured, or lacks the ability to pay, the financial health of the organization providing the care can be undermined. When a medical disaster with significant numbers of burn injured patients occurs, the financial concerns can be compounded with this singular event. It is critical to be cognizant of the disaster-related financial resources available. Knowing where to turn and what may be available can help assure that the institution caring for this group of high cost patients does not simultaneously take on significant financial risk in the aftermath of the disaster. This article includes national (United States) financial data with respect to burn injury, and focuses on (United States) governmental financial resources during and after a disaster. This review includes identifying and discussing traditional financial support, as well as atypical but established programs where, during a disaster, health care institutions may be eligible for assistance to cover part or all of the associated costs.

Prehospital Response Time Delays for Emergency Patients in Events of Concurrent Mass Casualty Incidents

Objective

We investigated the extent of delays in the response time of emergency medical services (EMS) as an impact of mass casualty incidences (MCIs) in the same area.

Methods

We defined an MCI case as an event that resulted in 6 or more patients being transported by EMS, and prehospital response time as the time from the call to arrival at the scene. We matched patients before and after MCIs by dividing them into categories of 3 hours before, 0-1 hour after, 1-2 hours after, and 2-3 hours after the MCIs. We compared prehospital response times using multiple linear regression.

Results

A total of 33,276 EMS-treated patients were matched. The prehospital response time for the category of 3 hours before the MCIs was 8.8 minutes (SD: 8.2), treated as the reference, whereas that for the category of 0-1 hour after the MCI was 11.3 minutes (P<0.01). The multiple linear regression analysis revealed that prehospital response time increased by 2.5 minutes (95% CI: 2.3-2.8) during the first hour and by 0.3 minutes (95% CI: 0.1-0.6) during the second hour after MCIs.

Conclusion

There were significant delays in the prehospital response time for emergency patients after MCIs, and it lasted for 2 hours as the spillover effect. (Disaster Med Public Health Preparedness. 2018;12:94–100)

The Association Between Hospital Capacity Strain and Inpatient Outcomes in Highly Developed Countries: A Systematic Review

BACKGROUND

Increases in patient needs can strain hospital resources, which may worsen care quality and outcomes. This systematic literature review sought to understand whether hospital capacity strain is associated with worse health outcomes for hospitalized patients and to evaluate benefits and harms of health system interventions to improve care quality during times of hospital capacity strain.

METHODS

Parallel searches were conducted in MEDLINE, CINAHL, the Cochrane Library, and reference lists from 1999-2015. Two reviewers assessed study eligibility. We included English-language studies describing the association between capacity strain (high census, acuity, turnover, or an indirect measure of strain such as delayed admission) and health outcomes or intermediate outcomes for children and adults hospitalized in highly developed countries. We also included studies of health system interventions to improve care during times of capacity strain. Two reviewers extracted data and assessed risk of bias using the Newcastle-Ottawa Score for observational studies and the Cochrane Collaboration Risk of Bias Assessment Tool for experimental studies.

RESULTS

Of 5,702 potentially relevant studies, we included 44 observational and 8 experimental studies. There was marked heterogeneity in the metrics used to define capacity strain, hospital settings, and overall study quality. Mortality increased during times of capacity strain in 18 of 30 studies and in 9 of 12 studies in intensive care unit settings. No experimental studies were randomized, and none demonstrated an improvement in health outcomes after implementing the intervention. The pediatric literature is very limited; only six observational studies included children. There was insufficient study homogeneity to perform meta-analyses.

DISCUSSION

In highly developed countries, hospital capacity strain is associated with increased mortality and worsened health outcomes. Evidence-based solutions to improve outcomes during times of capacity strain are needed.

[Pretreatment mass casualty incident workflow analysis : Comparison of two level 1 trauma centers]

BACKGROUND

Mass casualty incidents (MCI) have particularly high demands on patient care processes but occur rather rarely in daily hospital routine. Therefore, it is common to use simulations to train staff and to optimize institutional processes.

OBJECTIVES

Aim of study was to compare the pre-therapeutic in-house workflow of two differently structured level 1 trauma sites in the case of a simulated mass casualty incident (MCI).

MATERIALS AND METHODS

A MCI of 70 patients was simulated by actors in a manner that was as realistic as possible. The on-site triage assigned 7 cases to trauma site A with relatively long in-house distances and 4 patients to an independent trauma site B in which these distances were relatively short. During in-house treatment, time intervals for reaching milestones were measured and compared using the Mann-Whitney U test.

RESULTS

As no simultaneous patient arrival occurred, the Patient Distribution Matrix proved to be effective. Site A needed more time (minutes) from admission to endpoints (A: 31.85 ± 7.99; B: 21.62 ± 4.76; p = 0.059). In detail, the time intervals were particularly longer for both patient stay in trauma room (A: 8.46 ± 3.02; B: 2.73 ± 0.78, p < 0.01) and transfer time to the CT room (A: 1.81 ± 0.62; B: 0.06 ± 0.03, p < 0.01). A shorter stay in the CT room did not compensate these effects (A: 8.86 ± 1.84; B: 10.40 ± 2.89, p = 0.571). For both sites, image calculation and distribution were relatively time consuming (17.36 ± 3.05).

CONCLUSIONS

Although short in-house distances accelerated pretherapeutic treatment processes significantly, both sites remained clearly within the "golden hour". The strongest potential bottleneck was the time interval until images were available at the endpoints.

Patients as Patches: Ecology and Epidemiology in Healthcare Environments

The modern healthcare system involves complex interactions among microbes, patients, providers, and the built environment. It represents a unique and challenging setting for control of the emergence and spread of infectious diseases. We examine an extension of the perspectives and methods from ecology (and especially urban ecology) to address these unique issues, and we outline 3 examples: (1) viewing patients as individual microbial ecosystems; (2) the altered ecology of infectious diseases specifically within hospitals; and (3) ecosystem management perspectives for infection surveillance and control. In each of these cases, we explore the accuracy and relevance of analogies to existing urban ecological perspectives, and we demonstrate a few of the potential direct uses of this perspective for altering research into the control of healthcare-associated infections. Infect Control Hosp Epidemiol. 2016;1507-1512.

Radiological mass casualty incident (MCI) workflow analysis: single-centre data of a mid-scale exercise

OBJECTIVE

The aim of the study was to analyse and interpret radiological mass casualty incident workflow data.

METHODS

In a mid-scale mass casualty incident exercise, the on-site triage assigned 12 cases to the investigated institution (11 included in the study). Two out of five institutional multislice-CT-scanners were used and the whole CT workflow and radiological service process chain were simulated as close to realistic as possible. The respective time intervals for reaching defined milestones were measured.

RESULTS

The average CT in-room time, i.e. from entering to leaving the CT room was 9.43 min [(standard deviation) SD: 2.27 min; 95% (confidence interval) CI: 7.90-10.95 min]. Time spent on CT table was 6.75 min (SD: 1.67; CI: 5.63-7.87), and the pure scan time was 4.22 min (SD: 0.64; CI: 3.79-4.65). The first images after entering the CT room were available at a dedicated CT workstation after 5.85 min (SD: 2.20; CI: 4.37-7.32) and institution wide via picture archiving system (PACS) after 7.85 min (SD: 1.27; CI: 7.00-8.71). However, the PACS archiving process, that is, transfer of standard reconstruction set of CT images into the PACS was finished after 20.85 min (SD: 2.97; CI: 18.85-22.84).

CONCLUSION

Up to six patients may be served per hour and per CT scanner by using a standard whole body CT polytrauma protocol. Dedicated CT triage protocols may even increase this number. The time portion until images were available at end points was relatively long. A solution has to be developed in order to avoid scenarios of patients being faster at end points than their images.

Developments in Surge Research Priorities: A Systematic Review of the Literature Following the Academic Emergency Medicine Consensus Conference, 2007-2015

OBJECTIVES

In 2006, Academic Emergency Medicine (AEM) published a special issue summarizing the proceedings of the AEM consensus conference on the "Science of Surge." One major goal of the conference was to establish research priorities in the field of "disasters" surge. For this review, we wished to determine the progress toward the conference's identified research priorities: 1) defining criteria and methods for allocation of scarce resources, 2) identifying effective triage protocols, 3) determining decision-makers and means to evaluate response efficacy, 4) developing communication and information sharing strategies, and 5) identifying methods for evaluating workforce needs.

METHODS

Specific criteria were developed in conjunction with library search experts. PubMed, Embase, Web of Science, Scopus, and the Cochrane Library databases were queried for peer-reviewed articles from 2007 to 2015 addressing scientific advances related to the above five research priorities identified by AEM consensus conference. Abstracts and foreign language articles were excluded. Only articles with quantitative data on predefined outcomes were included; consensus panel recommendations on the above priorities were also included for the purposes of this review. Included study designs were randomized controlled trials, prospective, retrospective, qualitative (consensus panel), observational, cohort, case-control, or controlled before-and-after studies. Quality assessment was performed using a standardized tool for quantitative studies.

RESULTS

Of the 2,484 unique articles identified by the search strategy, 313 articles appeared to be related to disaster surge. Following detailed text review, 50 articles with quantitative data and 11 concept papers (consensus conference recommendations) addressed at least one AEM consensus conference surge research priority. Outcomes included validation of the benchmark of 500 beds/million of population for disaster surge capacity, effectiveness of simulation- and Internet-based tools for forecasting of hospital and regional demand during disasters, effectiveness of reverse triage approaches, development of new disaster surge metrics, validation of mass critical care approaches (altered standards of care), use of telemedicine, and predictions of optimal hospital staffing levels for disaster surge events. Simulation tools appeared to provide some of the highest quality research.

CONCLUSION

Disaster simulation studies have arguably revolutionized the study of disaster surge in the intervening years since the 2006 AEM Science of Surge conference, helping to validate some previously known disaster surge benchmarks and to generate new surge metrics. Use of reverse triage approaches and altered standards of care, as well as Internet-based tools such as Google Flu Trends, have also proven effective. However, there remains significant work to be done toward standardizing research methodologies and outcomes, as well as validating disaster surge metrics.

Trauma Surge Index: Advancing the Measurement of Trauma Surges and Their Influence on Mortality

BACKGROUND

Increases in trauma patient volume and acuity, such as during mass casualty events, can overwhelm hospitals, potentially worsening patient outcomes. Due to methodological limitations, the effect of trauma surges on clinical outcomes remains unclear, so hospitals have not prepared for such events in an evidence-based manner. The objective of this study was to develop a new measure of hospital capacity strain corresponding to trauma admissions and to examine the relationship between trauma surges and inpatient mortality.

STUDY DESIGN

Using trauma registry data from hospitals across the United States and Canada (2010 to 2011), we developed the Trauma Surge Index (TSI), a measure of capacity strain that controls for variation in hospital admission volume and patient acuity. Using the TSI and an established definition of mass casualty events, we quantified hospital surges and entered each measure as an exposure variable in separate risk-adjusted mortality models.

RESULTS

Using the TSI method, we observed that patients admitted during high-surge periods display significantly increased mortality compared with patients admitted during low-surge periods (odds ratio [OR] = 2.05; 95% CI, 1.36-3.10), and patients with firearms injuries were particularly at risk (OR = 7.29; 95% CI, 2.13-24.91). Using mass casualty event criteria, we found no difference between the mortality of patients admitted during trauma surges and nonsurge periods (OR = 0.94; 95% CI, 0.88-1.01).

CONCLUSIONS

We demonstrate the TSI, which is a novel method that identified periods of high-capacity strain in hospitals associated with increased trauma patient mortality. Our newly developed TSI method can be implemented by hospitals and trauma systems to examine periods of high-capacity strain retrospectively, identify specific resources that might have been needed, and better direct future investments in an evidence-based manner.

Surge capacity logistics: care of the critically ill and injured during pandemics and disasters: CHEST consensus statement

BACKGROUND

Successful management of a pandemic or disaster requires implementation of preexisting plans to minimize loss of life and maintain control. Managing the expected surges in intensive care capacity requires strategic planning from a systems perspective and includes focused intensive care abilities and requirements as well as all individuals and organizations involved in hospital and regional planning. The suggestions in this article are important for all involved in a large-scale disaster or pandemic, including front-line clinicians, hospital administrators, and public health or government officials. Specifically, this article focuses on surge logistics-those elements that provide the capability to deliver mass critical care.

METHODS

The Surge Capacity topic panel developed 23 key questions focused on the following domains: systems issues; equipment, supplies, and pharmaceuticals; staffing; and informatics. Literature searches were conducted to identify studies upon which evidence-based recommendations could be made. The results were reviewed for relevance to the topic, and the articles were screened by two topic editors for placement within one of the surge domains noted previously. Most reports were small scale, were observational, or used flawed modeling; hence, the level of evidence on which to base recommendations was poor and did not permit the development of evidence-based recommendations. The Surge Capacity topic panel subsequently followed the American College of Chest Physicians (CHEST) Guidelines Oversight Committee's methodology to develop suggestion based on expert opinion using a modified Delphi process.

RESULTS

This article presents 22 suggestions pertaining to surge capacity mass critical care, including requirements for equipment, supplies, and pharmaceuticals; staff preparation and organization; methods of mitigating overwhelming patient loads; the role of deployable critical care services; and the use of transportation assets to support the surge response.

CONCLUSIONS

Critical care response to a disaster relies on careful planning for staff and resource augmentation and involves many agencies. Maximizing the use of regional resources, including staff, equipment, and supplies, extends critical care capabilities. Regional coalitions should be established to facilitate agreements, outline operational plans, and coordinate hospital efforts to achieve predetermined goals. Specialized physician oversight is necessary and if not available on site, may be provided through remote consultation. Triage by experienced providers, reverse triage, and service deescalation may be used to minimize ICU resource consumption. During a temporary loss of infrastructure or overwhelmed hospital resources, deployable critical care services should be considered.