Surprisingly Few Avoidable Harms From Pandemic Strain in Multihospital U.S. Survey: Resilient Hospitals or Respondent Bias?* : Critical Care Medicine

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Surprisingly Few Avoidable Harms From Pandemic Strain in Multihospital U.S. Survey: Resilient Hospitals or Respondent Bias?*

Neupane, Maniraj MD, PhD; Kadri, Sameer S. MD, MS

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Critical Care Medicine 51(4):p 543-545, April 2023. | DOI: 10.1097/CCM.0000000000005810
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Historically, hospitals and ICUs have shown they can safely flex when needed to respond to day-to-day fluctuations in caseloads (1). However, the massive and sustained caseloads during the pandemic were a different beast––an unthinkable scenario that often necessitated uncomfortable deviations from typical care standards. Variability in the responses to and recovery from similar degrees of surge strain experienced at different hospitals might have evinced an obvious vulnerability: did some hospitals simply handle surge strain better than others? There is a critical need to identify the weak links and foster resilience (2) across healthcare systems nationally and globally. But where would we even begin?

Central to improving such resilience is a reliable capture of strain, responses, and outcomes. However, there was no playbook or optimal standard to measure or define surge strain pre-pandemic (3,4). As the pandemic took hold, investigators rushed heterogenous metrics on disparate care settings, at different time points and examined different outcomes. Interestingly, some studies have found surge strain to be detrimental (5–7) and others found no associated harms (8,9). Healthcare leaders and policy makers are left wondering whether these differences represent varying boluses of strain, varying performance, or simply varying lenses.

Improvisations in harnessing near real-time big data during the pandemic helped understand strain burden and effects to a degree, but the devil is in the details. These data sources often lack clinical and day-to-day granularity as well as the on-the-ground scoop as to why we see what we see in the data (10). We need to unpack further what happened at surging hospitals during the pandemic, so we do not repeat our mistakes and avoid patient harms in the future.

Qualitative surveys could help fill some of these evidence gaps. They can throw light on where, how, and why hospital strain manifested and pinpoint potential contributors to patient harm and guide future research and action. Surveys have helped capture care variations (11) and providers’ coping strategies from burnout (12) during the pandemic. However, surveys have their own limitations: their results are as best as the survey tool used and the respondents surveyed. They are prone to recall and social desirability bias and especially when surveying for high-level operational issues, results largely depend on respondent’s overall situational awareness.

Anesi et al (13) should be commended for further exploring clinicians’ perceptions on hospital strain. Utilizing the Society of Critical Care Medicine’s Discovery-Severe Acute Respiratory Infection-Preparedness network, they administered a longitudinal survey over 85 pandemic weeks (November 2020–June 2022). Authors adapted a previously validated survey developed during the influenza epidemic, that encapsulated mostly dichotomous stress response, and interrogated clinical investigators from 13 large U.S. academic hospitals from six states. The study has some noteworthy strengths. The survey had an excellent response rate of 91% and includes the more recent Omicron wave. Respondents reported on perceived stress as well as perceived patient harm and for the overall hospital, ICU, and their emergency department (ED). Additionally, authors related severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caseload peaks in counties surrounding these hospitals to the start and end of the perceived stress and perceived care deviations.

There are four key findings in the study by Anesi et al (13): First, in each pandemic week, four of 10 hospitals, three of 10 ICUs, and one of 10 EDs perceived operational stresses due to COVID-19 patients. Second, the hospital and ICU strain correlated well with each other but less so with ED strain. Third, that hospital and ICU stress was perceived persisting for over 10 weeks post-peak. However, fourth, and perhaps the most striking and actionable finding was that despite perceived strains from resource limitations at study hospitals, conspicuously few avoidable harms were perceived by respondents (specifically in only 5% of hospital weeks).

This begs the million-dollar question: why? It could be that study hospitals and their health systems were in fact more resilient than the average U.S. hospital. If so, then we have a lot to learn from these large academic centers of resilience! Was it that these hospitals more technologically equipped? Or had better, preemptive staff retention and load balancing strategies? Further investigation of the major contributors of resilience might identify some low-cost, high-reward interventions that could be implementable elsewhere. However, resilience-building is expensive. It can often involve big-ticket items that might be harder to implement under current fiscal circumstances and global repression of everything “pandemic.”

Hence, it is critical to understand whether we can take the findings of the study by Anesi et al (13) at face value and whether it will change thinking in a manner that could drive or suppress action regionally and nationally.

A noteworthy limitation of the study was that it did not use specific quantitative metrics to assess strain and its impact. While SARS-CoV-2 case burden was provided at the county level, this information was not available for individual study hospitals. Consequently, it is difficult to compare performance and outcomes at similarly impacted hospitals. It is also difficult to know whether the intensity of the strain experienced at study hospitals mirrored many of the hard-hit, nonsurveyed centers around the country. For example, after staffing reassignments, less than 5% of hospital weeks were reported to have inadequate staffing within individual disciplines. However, this does not seem reflective of the staffing crisis encountered nationally especially in the latter half of the pandemic raising questions on generalizability. More recently, data on individual hospital staff occupancy and staffed bed have become publicly accessible through a database recently introduced by the U.S. Department of Health and Human Services (14) and might enable more robust inferences in similar studies in the future.

Finally, who responds to surveys matters a lot in what can be inferred from them. Authors report respondents were mainly critical care clinical investigators. Was strain experienced by allied healthcare workers at the bedside, in the supply closet or in the command center relayed in the survey? The optics of surge-related harms can be detrimental to an individual hospitals future traffic and bottom line. The C-suite versus the nursing leadership might have very different takes on the same events and different stakes in the game. A more comprehensive understanding of the full gamut of respondents, their background and degree of frontline involvement would have been helpful. Furthermore, the sensitivity of the survey instrument matters. Respondents were offered dichotomous options in the survey to report perceived harm. Would harm have been more readily recognized with a more granular gauge? Mass media increased public exposure to many extreme case scenarios at hospitals during the pandemic. Were respondent perceptions dampened by those extreme case scenarios? Finally, not all forms of harms are visible, and yet might impact patient morbidity and mortality without being evidently linked to strain.

Anesi et al (13) provide a thought-provoking glimpse into the qualitative side of hospital strain and impacts during the pandemic. Whether the study might have identified centers of resilience, a relatively rosier experience than the rest of the nation or respondent bias ought to be explored further. If in fact the hospitals maintained high quality care even while strained, they could have lessons for other centers and opportunities for organizational, procedural, and infrastructural reform. These reforms could help patients during surges and regular times and benefit generations to come.

REFERENCES

1. Iwashyna TJ, Kramer AA, Kahn JM: Intensive care unit occupancy and patient outcomes. Crit Care Med. 2009; 37:1545–1557
2. Barbash IJ, Kahn JM: Fostering hospital resilience—lessons from COVID-19. JAMA. 2021; 326:693–694
3. Eriksson CO, Stoner RC, Eden KB, et al.: The association between hospital capacity strain and inpatient outcomes in highly developed countries: A systematic review. J Gen Intern Med. 2017; 32:686–696
4. Rewa OG, Stelfox HT, Ingolfsson A, et al.: Indicators of intensive care unit capacity strain: A systematic review. Crit Care. 2018; 22:86
5. Kadri SS, Sun J, Lawandi A, et al.: Association between caseload surge and COVID-19 survival in 558 U.S. hospitals, March to August 2020. Ann Intern Med. 2021; 174:1240–1251
6. Wilcox ME, Rowan KM, Harrison DA, et al.: Does unprecedented ICU capacity strain, as experienced during the COVID-19 pandemic, impact patient outcome? Crit Care Med. 2022; 50:e548–e556
7. Payet C, Polazzi S, Rimmelé T, et al.: Mortality among noncoronavirus disease 2019 critically ill patients attributable to the pandemic in France. Crit Care Med. 2022; 50:138–143
8. Jeitziner MM, Moser A, Wendel-Garcia PD, et al.: Critical care staffing ratio and outcome of COVID-19 patients requiring intensive care unit admission during the first pandemic wave: A retrospective analysis across Switzerland from the RISC-19-ICU observational cohort. Swiss Med Wkly. 2022; 152:w30183
9. Doidge JC, Gould DW, Ferrando-Vivas P, et al.: Trends in intensive care for patients with COVID-19 in England, Wales, and Northern Ireland. Am J Respir Crit Care Med. 2021; 203:565–574
10. Iwashyna TJ, Liu V: What’s so different about big data? A primer for clinicians trained to think epidemiologically. Annal Am Thoracic Soc. 2014; 11:1130–1135
11. Mathews KS, Seitz KP, Vranas KC, et al.: Variation in initial U.S. hospital responses to the coronavirus disease 2019 pandemic. Crit Care Med. 2021; 49:1038–1048
12. Burns KEA, Moss M, Lorens E, et al.: Wellness and coping of physicians who worked in ICUs during the pandemic: A multicenter cross-sectional North American survey. Crit Care Med. 2022; 50:1689–1700
13. Anesi GL, Andrews A, Bai HJ, et al.; Severe Acute Respiratory Infection – Preparedness (SARI-PREP) Study Group: Perceived Hospital Stress, Severe Acute Respiratory Syndrome Coronavirus 2 Activity, and Care Process Temporal Variance During the COVID-19 Pandemic. Crit Care Med. 2023; 51:445–459
14. U.S. Department of Health & Human Services: COVID-19 Reported Patient Impact and Hospital Capacity by Facility. 2022. Available at: https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/anag-cw7u. Accessed December 24, 2022
Keywords:

COVID-19; pandemic; strain; surge; survey

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