BACKGROUND: In response to the COVID-19 pandemic and as part of the statewide health care coalition response, the Minnesota Critical Care Working Group (CCWG), composed of inter-professional leaders from the state's 9 largest health systems, was established and entrusted to plan and coordinate critical care support for Minnesota from March 2020 through July 1, 2021. RESEARCH QUESTION: Can a statewide CCWG develop contingency and crisis-level surge strategies and indicators in response to the COVID-19 pandemic while evolving into a highly collaborative team? STUDY DESIGN AND METHODS: CCWG members (intensivists, ethicists, nurses, Minnesota Department of Health and Minnesota Hospital Association leaders) met by audio video conferencing as often as daily assessing COVID-19 and non-COVID-19 hospitalization data, developed surge evidence reflecting contingency vs crisis conditions, and planned responses collaboratively. A foundation of collaboration and teamwork developed which facilitated an effective statewide response. RESULTS: Pandemic surge health care system strategies included use of surge ICU beds, adapted staffing models, restriction of nonemergency procedures, augmentation of tele-ICU care, ability to recognize increasing staff shortages, use of PICU beds for younger adults, and use of noninvasive ventilation in non-ICU settings. CCWG supported development of the Minnesota Medical Operations Coordination Center, which was instrumental in load balancing and mitigating crisis conditions. Minnesota surge strategies are compared with published prepandemic and pandemic experiences regarding staff, space, supplies and medications/equipment, and system strategies. Adopted severe surge best practices included use of adapted staffing models and noninvasive ventilation in non-ICU settings. CCWG effectively developed shared strategies and facilitated ICU load balancing, which supported a regionally consistent standard of care. INTERPRETATION: The CCWG developed statewide critical care surge strategies assisting health care organization response to COVID-19 surges, providing a platform for clinical and operational activities. Collaboration, trust, and teamwork between CCWG leaders and health care organizations was essential to success and serves as a model for future events.

Importance:Some US states established state medical operations coordination centers (SMOCCs) during the pandemic to coordinate transfers and maximize care delivery when available beds were limited. Understanding their associations might inform their continued value in helping reduce delays and minimize strain during public health emergency conditions. Objective:To examine the association of SMOCC establishment with adult interhospital transfers (IHTs) during pandemic surges. Design, Setting, and Participants:This interrupted time series cohort study evaluated hospitalized adults (aged ≥18 years) from 8 US states. Data analysis was completed in March 2025. Exposure:SMOCC establishment. SMOCC initiation was adjudicated through a published survey and inquiries with state health departments. Main Outcomes and Measures:Outcome measures were immediate and long-term change in IHT by emergency medical services (EMS) agencies that continuously reported in the National EMS Information Systems database between June 1, 2020, and December 30, 2022. The inflection point (SMOCC establishment) was centered using relative dates while controlling for seasonality. Hospital occupancy stress was measured using daily hospital census and staffed bed counts and weighted by fixed bed capacity. Effect modification by increasing occupancy stress across study states was tested using an interaction term. Findings were validated in several subset analyses. Results:Across the study's 8 states (Alaska, Colorado, Idaho, Maryland, North Carolina, Oregon, Utah, and Virginia), 441 709 transfers (median [IQR] age, 61.0 [44.0-73.0] years; 227 982 [51.6%] male) were analyzed, with 321 078 (72.8%) occurring after SMOCC establishment. SMOCC establishment was associated with an immediate increase (rate ratio [RR], 1.35; 95% CI, 1.05-1.74; P = .02) followed by a long-term decrease (RR, 0.94; 95% CI, 0.90-0.97; P < .001) in transfer rates. A significant increase in transfers per decile increase in occupancy stress was observed 40 weeks into SMOCC establishment (RR, 1.23; 95% CI, 1.06-1.42; P = .007). Findings were similar across transfers grouped by urbanicity, mode of transport, patient age, and acuity. Conclusions and Relevance:In this cohort study of 8 US states, pandemic initiation of a SMOCC was associated with an immediate increase in transfer rates between hospitals of approximately 35% after establishment and, after a potential lag, appeared to meet an increasing demand for transferring patients during surges. These results suggest that activating SMOCCs during large-scale public health emergencies might improve access to care and mitigate transfer gridlocks, but their utility during routine times warrants study.

Health SecurityVol. 22, No. 1 CommentariesData and Disasters: Essential Information Needed for All Healthcare ThreatsJohn L. Hick, Eric S. Toner, Dan Hanfling, Paul D. Biddinger, and James V. LawlerJohn L. HickAddress correspondence to: John L. Hick, MD, Hennepin County Medical Center, Emergency Medicine MC825, 701 Park Ave., Minneapolis, MN 55415 E-mail Address: [email protected]John L. Hick, MD, is a Faculty Emergency Physician, Hennepin Healthcare, and a Professor of Emergency Medicine, University of Minnesota; both in Minneapolis, MN.Search for more papers by this author, Eric S. TonerEric S. Toner, MD, is a Senior Scholar, Johns Hopkins Center for Health Security, Baltimore, MD.Search for more papers by this author, Dan HanflingDan Hanfling, MD, is a Clinical Professor of Emergency Medicine at George Washington University, Washington, DC, and Vice President on the Technical Staff at In-Q-Tel, Arlington, VA.Search for more papers by this author, Paul D. BiddingerPaul D. Biddinger, MD, FACEP, is Chief Preparedness and Continuity Officer, Mass General Brigham; Director, Center for Disaster Medicine, Department of Emergency Medicine, Massachusetts General Hospital; Associate Professor of Emergency Medicine, Harvard Medical School; and Senior Fellow in Emergency Preparedness, Harvard TH Chan School of Public Health; all in Boston, MA.Search for more papers by this author, and James V. LawlerJames V. Lawler, MD, MPH, FIDSA, is Associate Director for International Programs and Innovation, Global Center for Health Security, and a Professor, Division of Infectious Diseases; both at the University of Nebraska Medical Center, Omaha, NE.Search for more papers by this authorPublished Online:19 Feb 2024https://doi.org/10.1089/hs.2023.0067AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookXLinked InRedditEmail View articleFiguresReferencesRelatedDetailsCited byIntroduction to the Special Feature: Threat Agnostic Approaches to Biodefense and Public Health Are Now a Reality Amesh A. Adalja, Kelsey Lane Warmbrod, and Mary J. Lancaster19 February 2024 | Health Security, Vol. 22, No. 1 Volume 22Issue 1Feb 2024 InformationCopyright 2024, Mary Ann Liebert, Inc., publishersTo cite this article:John L. Hick, Eric S. Toner, Dan Hanfling, Paul D. Biddinger, and James V. Lawler.Data and Disasters: Essential Information Needed for All Healthcare Threats.Health Security.Feb 2024.3-10.http://doi.org/10.1089/hs.2023.0067Published in Volume: 22 Issue 1: February 19, 2024Online Ahead of Print:September 20, 2023 TopicsBig data analyticsPublic health care PDF download

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.

BACKGROUND: COVID-19 led to unprecedented inpatient capacity challenges, particularly in ICUs, which spurred development of statewide or regional placement centers for coordinating transfer (load -balancing) of adult patients needing intensive care to hospitals with remaining capacity. RESEARCH QUESTION: Do Medical Operations Coordination Centers (MOCC) augment patient placement during times of severe capacity challenges? STUDY DESIGN AND METHODS: The Minnesota MOCC was established with a focus on transfer of adult ICU and medical -surgical patients; trauma, cardiac, stroke, burn, and extracorporeal membrane oxygenation cases were excluded. The center operated within one health care system's bed management center, using a dedicated 24/7 telephone number. Major health care systems statewide and two tertiary centers in a neighboring state participated, sharing information on system status, challenges, and strategies. Patient volumes and transfer data were tracked; client satisfaction was evaluated through an anonymous survey. RESULTS: From August 1, 2020, through March 31, 2022, a total of 5,307 requests were made, 2,008 beds identified, 1,316 requests canceled, and 1,981 requests were unable to be fulfilled. A total of 1,715 patients had COVID-19 (32.3%), and 2,473 were negative or low risk for COVID19 (46.6%). COVID-19 status was unknown in 1,119 (21.1%). Overall, 760 were patients on ventilators (49.1% COVID-19 positive). The Minnesota Critical Care Coordination Center placed most patients during the fall 2020 surge with the Minnesota Governor's stay-at-home order during the peak. However, during the fall 2021 surge, only 30% of ICU patients and 39% of medical -surgical patients were placed. Indicators characterizing severe surge include the number of Critical Care Coordination Center requests, decreasing placements, longer placement times, and time series analysis showing significant request -acceptance differences. INTERPRETATION: Implementation of a large-scale Minnesota MOCC program was effective at placing patients during the first COVID-19 pandemic fall 2020 surge and was well regarded by hospitals and health systems. However, under worsening duress of limited resources during the fall 2021 surge, placement of ICU and medical -surgical patients was greatly decreased.

BackgroundWhereas organizational literature has provided much insight into the conceptual and theoretical underpinnings of organizational leadership and management during emergencies, measures to operationalize related effective practices during crises remain sparse.PurposeTo address this need, we developed the Healthcare Emergency Response Optimization survey, which set out to examine the leadership and management practices in health care organizations that support resilience and performance during crisis.MethodologyWe administered an online survey in April to May 2022 to health care administrators and frontline staff intimately involved in their hospital's emergency response during the COVID-19 pandemic, which included a sample of 379 respondents across nine rural and urban hospitals (response rate: 44.4%). We used confirmatory factor analysis and quantile regressions to examine the results.ResultsApplying confirmatory factor analysis, we retained 36 items in our survey that comprised eight measures for formal and informal practices to assess crisis leadership and management. To test effectiveness of the specified practices, we regressed self-reported resilience and performance measures on the formality and informality scores. Findings show that informal practices mattered most for resilience, whereas formal practices mattered most for performance. We also identified specific practices (anticipation, transactional and relational interactions, and ad hoc collaborations) for resilience and performance.Practice ImplicationsThese validated measures of organizational practices assess emergency response during crisis, with an emphasis on the actions and decisions of leadership as well as the management of organizational structures and processes. Organizations using these measures may subsequently modify preparedness and planning approaches to better manage future crises.

BACKGROUND:Imbalances between hospital caseload and care resources that strained U.S. hospitals during the pandemic have persisted after the pandemic amid ongoing staff shortages. Understanding which hospital types were more resilient to pandemic overcrowding-related excess deaths may prioritize patient safety during future crises. OBJECTIVE:To determine whether hospital type classified by capabilities and resources (that is, extracorporeal membrane oxygenation [ECMO] capability, multiplicity of intensive care unit [ICU] types, and large or small hospital) influenced COVID-19 volume-outcome relationships during Delta wave surges. DESIGN:Retrospective cohort study. SETTING:620 U.S. hospitals in the PINC AI Healthcare Database. PARTICIPANTS:Adult inpatients with COVID-19 admitted July to November 2021. MEASUREMENTS:Hospital-months were ranked by previously validated surge index (severity-weighted COVID-19 inpatient caseload relative to hospital bed capacity) percentiles. Hierarchical models were used to evaluate the effect of log-transformed surge index on the marginally adjusted probability of in-hospital mortality or discharge to hospice. Effect modification was assessed for by 4 mutually exclusive hospital types. RESULTS:Among 620 hospitals recording 223 380 inpatients with COVID-19 during the Delta wave, there were 208 ECMO-capable, 216 multi-ICU, 36 large (≥200 beds) single-ICU, and 160 small (<200 beds) single-ICU hospitals. Overall, 50 752 (23%) patients required admission to the ICU, and 34 274 (15.3%) died. The marginally adjusted probability for mortality was 5.51% (95% CI, 4.53% to 6.50%) per unit increase in the log surge index (strain attributable mortality = 7375 [CI, 5936 to 8813] or 1 in 5 COVID-19 deaths). The test for interaction showed no difference (P = 0.32) in log surge index-mortality relationship across 4 hospital types. Results were consistent after excluding transferred patients, restricting to patients with acute respiratory failure and mechanical ventilation, and using alternative strain metrics. LIMITATION:Residual confounding. CONCLUSION:Comparably detrimental relationships between COVID-19 caseload and survival were seen across all hospital types, including highly advanced centers, and well beyond the pandemic's learning curve. These lessons from the pandemic heighten the need to minimize caseload surges and their effects across all hospital types during public health and staffing crises. PRIMARY FUNDING SOURCE:Intramural Research Program of the National Institutes of Health Clinical Center.

Importance Transferring patients to other hospitals because of inpatient saturation or need for higher levels of care was often challenging during the early waves of the COVID-19 pandemic. Understanding how transfer patterns evolved over time and amid hospital overcrowding could inform future care delivery and load balancing efforts. Objective To evaluate trends in outgoing transfers at overall and caseload-strained hospitals during the COVID-19 pandemic vs prepandemic times. Design, Setting, and Participants This retrospective cohort study used data for adult patients at continuously reporting US hospitals in the PINC-AI Healthcare Database. Data analysis was performed from February to July 2023. Exposures Pandemic wave, defined as wave 1 (March 1, 2020, to May 31, 2020), wave 2 (June 1, 2020, to September 30, 2020), wave 3 (October 1, 2020, to June 19, 2021), Delta (June 20, 2021, to December 18, 2021), and Omicron (December 19, 2021, to February 28, 2022). Main Outcomes and Measures Weekly trends in cumulative mean daily acute care transfers from all hospitals were assessed by COVID-19 status, hospital urbanicity, and census index (calculated as daily inpatient census divided by nominal bed capacity). At each hospital, the mean difference in transfer counts was calculated using pairwise comparisons of pandemic (vs prepandemic) weeks in the same census index decile and averaged across decile hospitals in each wave. For top decile (ie, high-surge) hospitals, fold changes (and 95% CI) in transfers were adjusted for hospital-level factors and seasonality. Results At 681 hospitals (205 rural [30.1%] and 476 urban [69.9%]; 360 [52.9%] small with <200 beds and 321 [47.1%] large with >=;200 beds), the mean (SD) weekly outgoing transfers per hospital remained lower than the prepandemic mean of 12.1 (10.4) transfers per week for most of the pandemic, ranging from 8.5 (8.3) transfers per week during wave 1 to 11.9 (10.7) transfers per week during the Delta wave. Despite more COVID-19 transfers, overall transfers at study hospitals cumulatively decreased during each high national surge period. At 99 high-surge hospitals, compared with a prepandemic baseline, outgoing acute care transfers decreased in wave 1 (fold change -15.0%; 95% CI, -22.3% to -7.0%; P < .001), returned to baseline during wave 2 (2.2%; 95% CI, -4.3% to 9.2%; P = .52), and displayed a sustained increase in subsequent waves: 19.8% (95% CI, 14.3% to 25.4%; P < .001) in wave 3, 19.2% (95% CI, 13.4% to 25.4%; P < .001) in the Delta wave, and 15.4% (95% CI, 7.8% to 23.5%; P < .001) in the Omicron wave. Observed increases were predominantly limited to small urban hospitals, where transfers peaked (48.0%; 95% CI, 36.3% to 60.8%; P < .001) in wave 3, whereas large urban and small rural hospitals displayed little to no increases in transfers from baseline throughout the pandemic. Conclusions and Relevance Throughout the COVID-19 pandemic, study hospitals reported paradoxical decreases in overall patient transfers during each high-surge period. Caseload-strained rural (vs urban) hospitals with fewer than 200 beds were unable to proportionally increase transfers. Prevailing vulnerabilities in flexing transfer capabilities for care or capacity reasons warrant urgent attention.

Background COVID-19 led to unprecedented inpatient capacity challenges, particularly in ICUs, which spurred development of statewide or regional placement centers for coordinating transfer (load-balancing) of adult patients needing intensive care to hospitals with remaining capacity. Research Question Do Medical Operations Coordination Centers (MOCC) augment patient placement during times of severe capacity challenges? Study Design and Methods The Minnesota MOCC was established with a focus on transfer of adult ICU and medical-surgical patients; trauma, cardiac, stroke, burn, and extracorporeal membrane oxygenation cases were excluded. The center operated within one health care system's bed management center, using a dedicated 24/7 telephone number. Major health care systems statewide and two tertiary centers in a neighboring state participated, sharing information on system status, challenges, and strategies. Patient volumes and transfer data were tracked; client satisfaction was evaluated through an anonymous survey. Results From August 1, 2020, through March 31, 2022, a total of 5,307 requests were made, 2,008 beds identified, 1,316 requests canceled, and 1,981 requests were unable to be fulfilled. A total of 1,715 patients had COVID-19 (32.3%), and 2,473 were negative or low risk for COVID-19 (46.6%). COVID-19 status was unknown in 1,119 (21.1%). Overall, 760 were patients on ventilators (49.1% COVID-19 positive). The Minnesota Critical Care Coordination Center placed most patients during the fall 2020 surge with the Minnesota Governor's stay-at-home order during the peak. However, during the fall 2021 surge, only 30% of ICU patients and 39% of medical-surgical patients were placed. Indicators characterizing severe surge include the number of Critical Care Coordination Center requests, decreasing placements, longer placement times, and time series analysis showing significant request-acceptance differences. Interpretation Implementation of a large-scale Minnesota MOCC program was effective at placing patients during the first COVID-19 pandemic fall 2020 surge and was well regarded by hospitals and health systems. However, under worsening duress of limited resources during the fall 2021 surge, placement of ICU and medical-surgical patients was greatly decreased. COVID-19 led to unprecedented inpatient capacity challenges, particularly in ICUs, which spurred development of statewide or regional placement centers for coordinating transfer (load-balancing) of adult patients needing intensive care to hospitals with remaining capacity. Do Medical Operations Coordination Centers (MOCC) augment patient placement during times of severe capacity challenges? The Minnesota MOCC was established with a focus on transfer of adult ICU and medical-surgical patients; trauma, cardiac, stroke, burn, and extracorporeal membrane oxygenation cases were excluded. The center operated within one health care system's bed management center, using a dedicated 24/7 telephone number. Major health care systems statewide and two tertiary centers in a neighboring state participated, sharing information on system status, challenges, and strategies. Patient volumes and transfer data were tracked; client satisfaction was evaluated through an anonymous survey. From August 1, 2020, through March 31, 2022, a total of 5,307 requests were made, 2,008 beds identified, 1,316 requests canceled, and 1,981 requests were unable to be fulfilled. A total of 1,715 patients had COVID-19 (32.3%), and 2,473 were negative or low risk for COVID-19 (46.6%). COVID-19 status was unknown in 1,119 (21.1%). Overall, 760 were patients on ventilators (49.1% COVID-19 positive). The Minnesota Critical Care Coordination Center placed most patients during the fall 2020 surge with the Minnesota Governor's stay-at-home order during the peak. However, during the fall 2021 surge, only 30% of ICU patients and 39% of medical-surgical patients were placed. Indicators characterizing severe surge include the number of Critical Care Coordination Center requests, decreasing placements, longer placement times, and time series analysis showing significant request-acceptance differences. Implementation of a large-scale Minnesota MOCC program was effective at placing patients during the first COVID-19 pandemic fall 2020 surge and was well regarded by hospitals and health systems. However, under worsening duress of limited resources during the fall 2021 surge, placement of ICU and medical-surgical patients was greatly decreased. Successful Critical Care Operations: The Minnesota COVID-19 ExperienceCHESTVol. 165Issue 1PreviewAlthough infectious disease outbreaks and mass casualty events have occurred at an alarmingly increasing frequency since the turn of this century, global preparedness responses have largely been reactionary. Indeed, after several severe influenza epidemic seasons, the National Academy of Sciences released guidance on crisis standards of care in 2012 to assist hospitals in prioritizing patient care in the setting of future capacity strain.1 One year later, the US Department of Health and Human Services through the Assistant Secretary for Preparedness and Response (now known as the Administration for Strategic Preparedness and Response, or ASPR) released the "Interim Healthcare Coalition Checklist for Pandemic Planning," which outlined essential domains hospitals and hospital systems should address to be prepared for future disasters. Full-Text PDF

1 Department of Emergency Medicine, Hennepin Healthcare, Minneapolis, MN. 2 Department of Emergency Medicine, University of Minnesota, Minneapolis, MN. 3 Critical Care Medicine Department, National Institutes of Health Clinical Center, Bethesda, MD. *See also p. 574. Dr. Kadri received support for article research from the National Institutes of Health. Dr. Hick has not disclosed that he does not have any potential conflicts of interest

While medical countermeasures in COVID-19 have largely focused on vaccinations, monoclonal antibodies (mAbs) were early outpatient treatment options for COVID-positive patients. In Minnesota, a centralized access platform was developed to offer access to mAbs that linked over 31,000 patients to care during its operation. The website allowed patients, their representative, or providers to screen the patient for mAbs against Emergency Use Authorization (EUA) criteria and connect them with a treatment site if provisionally eligible. A validated clinical risk scoring system was used to prioritize patients during times of scarcity. Both an ethics and a clinical subject matter expert group advised the Minnesota Department of Health on equitable approaches to distribution across a range of situations as the pandemic evolved. This case study outlines the implementation of this online platform and clinical outcomes of its users. We assess the impact of referral for mAbs on hospitalizations and death during a period of scarcity, finding in particular that vaccination conferred a substantially larger protection against hospitalization than a referral for mAbs, but among unvaccinated users that did not get a referral, chances of hospitalization increased by 4.1 percentage points.

ImportanceThe second year of the COVID-19 pandemic saw periods of dire health care resource limitations in the US, sometimes prompting official declarations of crisis, but little is known about how these conditions were experienced by frontline clinicians. ObjectiveTo describe the experiences of US clinicians practicing under conditions of extreme resource limitation during the second year of the pandemic. Design, Setting, and ParticipantsThis qualitative inductive thematic analysis was based on interviews with physicians and nurses providing direct patient care at US health care institutions during the COVID-19 pandemic. Interviews were conducted between December 28, 2020, and December 9, 2021. ExposureCrisis conditions as reflected by official state declarations and/or media reports. Main Outcomes and MeasuresClinicians' experiences as obtained through interviews. ResultsInterviews with 23 clinicians (21 physicians and 2 nurses) who were practicing in California, Idaho, Minnesota, or Texas were included. Of the 23 total participants, 21 responded to a background survey to assess participant demographics; among these individuals, the mean (SD) age was 49 (7.3) years, 12 (57.1%) were men, and 18 (85.7%) self-identified as White. Three themes emerged in qualitative analysis. The first theme describes isolation. Clinicians had a limited view on what was happening outside their immediate practice setting and perceived a disconnect between official messaging about crisis conditions and their own experience. In the absence of overarching system-level support, responsibility for making challenging decisions about how to adapt practices and allocate resources often fell to frontline clinicians. The second theme describes in-the-moment decision-making. Formal crisis declarations did little to guide how resources were allocated in clinical practice. Clinicians adapted practice by drawing on their clinical judgment but described feeling ill equipped to handle some of the operationally and ethically complex situations that fell to them. The third theme describes waning motivation. As the pandemic persisted, the strong sense of mission, duty, and purpose that had fueled extraordinary efforts earlier in the pandemic was eroded by unsatisfying clinical roles, misalignment between clinicians' own values and institutional goals, more distant relationships with patients, and moral distress. Conclusions and RelevanceThe findings of this qualitative study suggest that institutional plans to protect frontline clinicians from the responsibility for allocating scarce resources may be unworkable, especially in a state of chronic crisis. Efforts are needed to directly integrate frontline clinicians into institutional emergency responses and support them in ways that reflect the complex and dynamic realities of health care resource limitation.

Massive pulmonary embolism (hemodynamically unstable, defined as systolic BP <90 mmHg) has significant morbidity and mortality. Point of care ultrasound (POCUS) has allowed clinicians to detect evidence of massive pulmonary embolism much earlier in the patient's clinical course, especially when patient instability precludes computerized tomography confirmation. POCUS detection of massive pulmonary embolism has traditionally been performed by physicians. This case series demonstrates four cases of massive pulmonary embolism diagnosed with POCUS performed by non-physician prehospital personnel.

During the Covid-19 pandemic, many U.S. states deployed state capacity coordination centers (SCCCs) to organize load balancing and facilitate equitable access to acute care during surges. Some SCCCs continue to operate today, whereas others intend to reactivate in the event of a future surge. SCCCs, sometimes called medical operations coordination centers (MOCCs), aggregate capacity data and coordinate patient placement among multiple independent regional hospitals. Despite their wide utilization during and after the pandemic, data regarding SCCCs are primarily anecdotal, with no systematic study to inform best practices. To identify the prevalence (current and prior), design, and performance of SCCCs (including the number and percentage of patient placement requests fulfilled along with time to placement), the authors conducted a national survey in partnership with the American Hospital Association. Between June 2022 and April 2023, the authors administered a screening survey to the 50 state hospital associations to ascertain the presence or absence of SCCCs within each state and followed up with a comprehensive survey to SCCC leaders, addressing SCCC design and performance. The screening survey response rate was 88.0% (44 of 50 states); 19 of 44 states (43.2%) reported using SCCCs (as early as December 2019). The authors identified contacts for 16 of those 19 SCCCs; 12 of 16 (75.0%) responded to the comprehensive survey. SCCCs varied widely in design, authority, and utility. Five of 12 SCCCs were operational at the time of the comprehensive survey, having operated continuously; the remaining seven operated only during surges. In terms of equity-enhancing features, 9 of 12 SCCCs explicitly disregarded patient insurance status in determining placement; 7 of 12 SCCCs reported participation by all hospitals in the covered region, whereas the remainder coordinated with only a subset of hospitals in the covered region. Only 2 of 12 SCCCs could mandate patient placement acceptance, with authority by the state governor's executive order or binding hospital agreement. The median number of patient placement requests received by SCCCs was 3,614 (minimum, 109; maximum, 15,000; interquartile range, 4,560). The median percentage of placement requests fulfilled was 63.3% (minimum, 30.0%; maximum, 99.8%; interquartile range, 43.2%). The authors describe SCCC design and performance in aggregate, provide individual center data and reflections, and discuss future research priorities to elucidate factors associated with high-performing SCCCs. As of the date of response, all respondents with currently operating SCCCs reported intent to continue operations following the Covid-19 pandemic, suggesting that SCCCs may be used to facilitate load balancing during both public health emergencies and routine capacity challenges. Future research priorities include identifying barriers to SCCC adoption of equity-enhancing features (especially related to hospital participation and patient acceptance mandates), elucidating design and implementation factors associated with high SCCC patient placement rates, and comparing SCCCs with alternative regional load-balancing mechanisms.

After the publication of a 2014 consensus statement regarding mass critical care during public health emergencies, much has been learned about surge responses and the care of overwhelming numbers of patients during the COVID-19 pandemic. Gaps in prior pandemic planning were identified and require modification in the midst of severe ongoing surges throughout the world.

BACKGROUND:After the publication of a 2014 consensus statement regarding mass critical care during public health emergencies, much has been learned about surge responses and the care of overwhelming numbers of patients during the COVID-19 pandemic. Gaps in prior pandemic planning were identified and require modification in the midst of severe ongoing surges throughout the world. RESEARCH QUESTION:A subcommittee from The Task Force for Mass Critical Care (TFMCC) investigated the most recent COVID-19 publications coupled with TFMCC members anecdotal experience in order to formulate operational strategies to optimize contingency level care, and prevent crisis care circumstances associated with increased mortality. STUDY DESIGN AND METHODS:TFMCC adopted a modified version of established rapid guideline methodologies from the World Health Organization and the Guidelines International Network-McMaster Guideline Development Checklist. With a consensus development process incorporating expert opinion to define important questions and extract evidence, the TFMCC developed relevant pandemic surge suggestions in a structured manner, incorporating peer-reviewed literature, "gray" evidence from lay media sources, and anecdotal experiential evidence. RESULTS:Ten suggestions were identified regarding staffing, load-balancing, communication, and technology. Staffing models are suggested with resilience strategies to support critical care staff. ICU surge strategies and strain indicators are suggested to enhance ICU prioritization tactics to maintain contingency level care and to avoid crisis triage, with early transfer strategies to further load-balance care. We suggest that intensivists and hospitalists be engaged with the incident command structure to ensure two-way communication, situational awareness, and the use of technology to support critical care delivery and families of patients in ICUs. INTERPRETATION:A subcommittee from the TFMCC offers interim evidence-informed operational strategies to assist hospitals and communities to plan for and respond to surge capacity demands resulting from COVID-19.

Maintaining a public health emergency response for a sustained period of time requires availability of resources, physical and information technology infrastructure, and human capital. What perhaps is unprecedented is a medical center experiencing multiple disasters simultaneously. In this case study, the authors describe 2 separate disaster events experienced during the ongoing COVID-19 pandemic: (1) a cyberattack at Nebraska Medicine in Omaha, Nebraska, and (2) civil unrest following the murder of George Floyd in Minneapolis, Minnesota. Although these settings were very different, the following common themes can inform future disaster planning: the benefit of an already active incident command system, the prescient need for continuity of operations, and the anticipation of workforce fatigue. These dual-disaster experiences provide an opportunity to identify lessons learned that will drive improvements in emergency management through preparedness and mitigation measures and response innovations for future simultaneous disasters.

We agree with White, Lo, and Peek on the need to address the deep inequities exposed during COVID-19. However, adjusting triage processes by using social indexes is not the way to do so. Translating ethical values into ethical operational frameworks is a difficult proposition. For example, despite general community consensus to prioritize younger individuals for access to scarce resources (“fair innings” principle),1Harris J. The Value of Life: An Introduction to Medical Ethics. Routledge & Keegan Paul, London1970Google Scholar,2Harris County Public Health and Environmental ServicesThe Harris County public engagement project on pandemic influenza. Harris County Public Health and Environmental Services.https://www.keystone.org/wp-content/uploads/2015/08/072911-Harris-County-TX-Pandemic-Influence-Engagement-Project-Report.pdfDate accessed: May 1, 2022Google Scholar a legally and operationally defensible mechanism to include age has been elusive, because of the preference of some populations—including Native Americans—to prioritize their elders. Furthermore, equal protection issues surrounding age-based allocation have been legally contested.3US Department of Health and Human ServicesOffice of Civil Rights. Civil Rights and COVID-19.https://www.hhs.gov/civil-rights/for-providers/civil-rights-covid19/index.htmlDate accessed: February 21, 2022Google Scholar Issues are multiplied and consequences magnified when we try to address broader and less binary social constructs such as race and economics in allocation. When we harm one individual by awarding treatment to another, we must match our values directly to our procedures and not “miss the target.” Even assuring essential worker reciprocity (rewarding service) and instrumentality (maintaining society) can be challenging. Who is an essential worker? Which jobs can avoid direct contact with the public? Many essential workers contracted COVID-19 outside the job setting, and sometimes in defiance of community precautions.4Carlson J. Minnesota Study: Home, community more risky for health care providers than patients with COVID-19.Star Tribune. October 30, 2020; https://www.startribune.com/home-community-more-risky-for-nurses-than-virus-patients/572912181/Date accessed: February 21, 2022Google Scholar,5Salcedo A. Nursing Home Staffers Attended a 300-Person Superspreader Wedding. Now Six Residents Have Died. December 7, 2020.https://www.washingtonpost.com/nation/2020/12/07/washington-superspreader-wedding-nursing-homes-covid/Date accessed: February 21, 2022Google Scholar The authors say that they do not intend to address historical inequity but they essentially propose to, because their correction is not aimed at the equivalent critical care outcomes but adjusts for undiagnosed and complex medical problems associated with social determinants of health such as socioeconomic deprivation and distrust of medical providers and treatments. Who exactly they intend to prioritize is unclear. Is it the poor in general? Is there differential priority between Black, Latinx, and Native American individuals? Do they intend to offer direct benefit to rural communities that score highly on Area Deprivation Index (ADI) but not Social Vulnerability Index indicators, knowing that these populations may be distrustful of medical care? Their table on outcomes relies on a series of nesting assumptions that we do not believe have validity. Assuming that all patients that do not receive an ICU bed will die is at odds with the successful higher-acuity care provided in non-critical care units in addition to telemedicine and “care-in-place” support for critical care extension. Even though race was specified as a factor to consider in the Emergency Use Authorization for monoclonal antibody treatments, several states are facing legal challenges for including race as a consideration.6US Food and Drug AdministrationFact Sheet for Health Care Providers, Emergency Use Authorization (EUA) of Bamlanivimab and Etesevimab.https://www.fda.gov/media/145802/downloadDate accessed: February 21, 2022Google Scholar,7Rizzo S. Former Trump Adviser Falsely Claims States Are Rationing Scarce Covid Treatments Based Largely on Race. February 10, 2022.https://www.washingtonpost.com/health/2022/02/10/conservatives-covid-treatments-race/Date accessed: February 21, 2022Google Scholar For example, Minnesota withdrew race as a factor in its allocation framework because of equal protection issues, despite clear evidence that race independently predicted increased hospitalization risk.8Olson J. Minnesota Removes Race as Factor in Rationing COVID-19 Antibody Treatment. January 13, 2022.https://www.startribune.com/minnesota-removes-race-as-factor-in-rationing-covid-19-antibodies/600135503/Date accessed: February 21, 2022Google Scholar Although we agree with the authors’ goals, their proposal insufficiently identifies the beneficiaries, corrections, and correlation to the ADI as a solution. The ADI and other nonspecific population measures should not be used in critical care resource allocation. We must improve clinical prognostic tools, refine processes for determining nonbeneficial care, eliminate inappropriate decision schemes such as those reliant on SOFA scores, ensure implementation of load-balancing mechanisms to promote consistency of care,9White D.B. Villarroel L. Hick J. Inequitable access to hospital care: protecting disadvantaged populations during public health emergencies.N Engl J Med. 2021; 385: 2211-2214Crossref Scopus (2) Google Scholar and work toward improving trust in, and access to, medical care. Financial/nonfinancial disclosures: None declared. COUNTERPOINT: Is Considering Social Determinants of Health Ethically Permissible for Fair Allocation of Critical Care Resources During the COVID-19 Pandemic? NoCHESTVol. 162Issue 1PreviewCOVID-19 has laid bare existing inequities in health care,1,2 and the disproportionate impact on the poor and communities of color have rightfully driven a search for solutions to improve access across the spectrum of medical care delivery. Identifying at-risk areas of our community for targeted interventions is thus a key mitigation strategy to reduce further impact. Full-Text PDF Rebuttal From Dr White et alCHESTVol. 162Issue 1PreviewWe appreciate the opportunity to respond to our colleagues’ arguments against incorporating equity considerations when allocating scarce critical care resources during a pandemic. We respond to four objections they raised. Full-Text PDF POINT: Is Considering Social Determinants of Health Ethically Permissible for Fair Allocation of Critical Care Resources During the COVID-19 Pandemic? YesCHESTVol. 162Issue 1PreviewThere is growing agreement that triage protocols for scarce medical resources such as ICU beds and ventilators should—at the very least—not exacerbate the profound disparities in health outcomes that are occurring during the COVID-19 pandemic among racial or ethnic minorities, persons with disabilities, and low-income people. Some have suggested that ICU triage guided solely by medical prognosis (ie, chances of survival to hospital discharge) will accomplish this.1 Although pure prognosis-based triage may seem equitable, it is not; it would exacerbate health disparities that have become a national priority to mitigate. Full-Text PDF