Frequency and Risk of Emergency Medical Service Interhospital Transportation of Patients With Acute Lower Respiratory Tract Illness During the COVID-19 Pandemic in the US

Association Between Hospital Type and Resilience During COVID-19 Caseload Stress : A Retrospective Cohort Study

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.

Trends in Patient Transfers From Overall and Caseload-Strained US Hospitals During the COVID-19 Pandemic

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.

The Minnesota Medical Operations Coordination Center: A COVID-19 Statewide Response to Ensure Access to Critical Care and Medical-Surgical Beds

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.

Hospitals' electronic access to information needed to treat COVID-19

Objective

To understand whether hospitals had electronic access to information needed to treat COVID-19 patients and identify factors contributing to differences in information availability.

Materials and methods

Using 2021 data from the American Hospital Association IT Supplement, we produced national estimates on the electronic availability of information needed to treat COVID-19 at US non-federal acute care hospitals (N = 1976) and assessed differences in information availability by hospital characteristics and engagement in interoperable exchange.

Results

In 2021, 38% of hospitals electronically received information needed to effectively treat COVID-19 patients. Information availability was significantly higher among higher-resourced hospitals and those engaged in interoperable exchange (44%) compared to their counterparts. In adjusted analyses, hospitals engaged in interoperable exchange were 140% more likely to receive needed information electronically compared to those not engaged in exchange (relative risk [RR]=2.40, 95% CI, 1.82-3.17, P<.001). System member hospitals (RR = 1.62, 95% CI, 1.36-1.92, P<.001) and major teaching hospitals (RR = 1.35, 95% CI, 1.10-1.64, P=.004) were more likely to have information available; for-profit hospitals (RR = 0.14, 95% CI, 0.08-0.24, P<.001) and hospitals in high social deprivation areas (RR = 0.83, 95% CI, 0.71-0.98, P = .02) were less likely to have information available.

Discussion

Despite high rates of hospitals' engagement in interoperable exchange, hospitals' electronic access to information needed to support the care of COVID-19 patients was limited.

Conclusion

Limited electronic access to patient information from outside sources may impede hospitals' ability to effectively treat COVID-19 and support patient care during public health emergencies.

Interfacility Transport of Critically Ill Patients

OBJECTIVES

To assess recent advances in interfacility critical care transport.

DATA SOURCES

PubMed English language publications plus chapters and professional organization publications.

STUDY SELECTION

Manuscripts including practice manuals and standard (1990-2021) focused on interfacility transport of critically ill patients.

DATA EXTRACTION

Review of society guidelines, legislative requirements, objective measures of outcomes, and transport practice standards occurred in work groups assessing definitions and foundations of interfacility transport, transport team composition, and transport specific considerations. Qualitative analysis was performed to characterize current science regarding interfacility transport.

DATA SYNTHESIS

The Task Force conducted an integrative review of 496 manuscripts combined with 120 from the authors' collections including nonpeer reviewed publications. After title and abstract screening, 40 underwent full-text review, of which 21 remained for qualitative synthesis.

CONCLUSIONS

Since 2004, there have been numerous advances in critical care interfacility transport. Clinical deterioration may be mitigated by appropriate patient selection, pretransport optimization, and transport by a well-resourced team and vehicle. There remains a dearth of high-quality controlled studies, but notable advances in monitoring, en route management, transport modality (air vs ground), as well as team composition and training serve as foundations for future inquiry. Guidance from professional organizations remains uncoupled from enforceable regulations, impeding standardization of transport program quality assessment and verification.