Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 20 de 177
Filter
Add filters

Document Type
Year range
1.
Am J Disaster Med ; 16(3): 179-192, 2021.
Article in English | MEDLINE | ID: covidwho-1572826

ABSTRACT

OBJECTIVE: Many hospitals were unprepared for the surge of patients associated with the spread of coronavirus disease 2019 (COVID-19) pandemic. We describe the processes to develop and implement a surge plan framework for resource allocation, staffing, and standardized management in response to the COVID-19 pandemic across a large integrated regional healthcare system. SETTING: A large academic medical center in the Cleveland metropolitan area, with a network of 10 regional hospitals throughout Northeastern Ohio with a daily capacity of more than 500 intensive care unit (ICU) beds. RESULTS: At the beginning of the pandemic, an equitable delivery of healthcare services across the healthcare system was developed. This distribution of resources was implemented with the potential needs and resources of the individual ICUs in mind, and epidemiologic predictions of virus transmissibility. We describe the processes to develop and implement a surge plan framework for resource allocation, staffing, and standardized management in response to the COVID-19 pandemic across a large integrated regional healthcare system. We also describe an additional level of surge capacity, which is available to well-integrated institutions called "extension of capacity." This refers to the ability to immediately have access to the beds and resources within a hospital system with minimal administrative burden. CONCLUSIONS: Large integrated hospital systems may have an advantage over individual hospitals because they can shift supplies among regional partners, which may lead to faster mobilization of resources, rather than depending on local and national governments. The pandemic response of our healthcare system highlights these benefits.


Subject(s)
COVID-19 , Surge Capacity , Critical Care , Delivery of Health Care , Hospital Bed Capacity , Humans , Intensive Care Units , Pandemics , SARS-CoV-2
2.
Physiotherapy ; 113: 153-159, 2021 12.
Article in English | MEDLINE | ID: covidwho-1525919

ABSTRACT

In this short report the authors characterise inpatient bed occupancy and predicted rehabilitation need of patients cared for in two acute hospitals of a large London NHS Trust during the first wave of the COVID-19 pandemic, including 394 people with confirmed COVID-19. Data were captured on a single day (17th April 2020) from the two Trust hospitals to inform discharge planning in line with national COVID-19 Hospital Discharge Service policy guidance. Our data suggests that the proportion of COVID-19 patients predicted to require rehabilitation upon hospital discharge may be greater than the estimates described in the national COVID-19 Hospital Discharge Service policy guidance; posing the question is there a demand-capacity mismatch between rehabilitation need and service provision as a result of the COVID-19 pandemic?


Subject(s)
COVID-19 , Pandemics , Hospital Bed Capacity , Hospitals, Teaching , Humans , London/epidemiology , SARS-CoV-2
3.
PLoS One ; 16(11): e0260310, 2021.
Article in English | MEDLINE | ID: covidwho-1523457

ABSTRACT

The first case of COVID-19 was detected in North Carolina (NC) on March 3, 2020. By the end of April, the number of confirmed cases had soared to over 10,000. NC health systems faced intense strain to support surging intensive care unit admissions and avert hospital capacity and resource saturation. Forecasting techniques can be used to provide public health decision makers with reliable data needed to better prepare for and respond to public health crises. Hospitalization forecasts in particular play an important role in informing pandemic planning and resource allocation. These forecasts are only relevant, however, when they are accurate, made available quickly, and updated frequently. To support the pressing need for reliable COVID-19 data, RTI adapted a previously developed geospatially explicit healthcare facility network model to predict COVID-19's impact on healthcare resources and capacity in NC. The model adaptation was an iterative process requiring constant evolution to meet stakeholder needs and inform epidemic progression in NC. Here we describe key steps taken, challenges faced, and lessons learned from adapting and implementing our COVID-19 model and coordinating with university, state, and federal partners to combat the COVID-19 epidemic in NC.


Subject(s)
COVID-19/epidemiology , Hospital Bed Capacity/statistics & numerical data , Hospitalization/trends , Intensive Care Units/trends , Pandemics/statistics & numerical data , Delivery of Health Care , Forecasting , Humans , North Carolina/epidemiology
5.
Anaesthesist ; 69(10): 717-725, 2020 10.
Article in German | MEDLINE | ID: covidwho-1453673

ABSTRACT

BACKGROUND: Following the regional outbreak in China, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread all over the world, presenting the healthcare systems with huge challenges worldwide. In Germany the coronavirus diseases 2019 (COVID-19) pandemic has resulted in a slowly growing demand for health care with a sudden occurrence of regional hotspots. This leads to an unpredictable situation for many hospitals, leaving the question of how many bed resources are needed to cope with the surge of COVID-19 patients. OBJECTIVE: In this study we created a simulation-based prognostic tool that provides the management of the University Hospital of Augsburg and the civil protection services with the necessary information to plan and guide the disaster response to the ongoing pandemic. Especially the number of beds needed on isolation wards and intensive care units (ICU) are the biggest concerns. The focus should lie not only on the confirmed cases as the patients with suspected COVID-19 are in need of the same resources. MATERIAL AND METHODS: For the input we used the latest information provided by governmental institutions about the spreading of the disease, with a special focus on the growth rate of the cumulative number of cases. Due to the dynamics of the current situation, these data can be highly variable. To minimize the influence of this variance, we designed distribution functions for the parameters growth rate, length of stay in hospital and the proportion of infected people who need to be hospitalized in our area of responsibility. Using this input, we started a Monte Carlo simulation with 10,000 runs to predict the range of the number of hospital beds needed within the coming days and compared it with the available resources. RESULTS: Since 2 February 2020 a total of 306 patients were treated with suspected or confirmed COVID-19 at this university hospital. Of these 84 needed treatment on the ICU. With the help of several simulation-based forecasts, the required ICU and normal bed capacity at Augsburg University Hospital and the Augsburg ambulance service in the period from 28 March 2020 to 8 June 2020 could be predicted with a high degree of reliability. Simulations that were run before the impact of the restrictions in daily life showed that we would have run out of ICU bed capacity within approximately 1 month. CONCLUSION: Our simulation-based prognosis of the health care capacities needed helps the management of the hospital and the civil protection service to make reasonable decisions and adapt the disaster response to the realistic needs. At the same time the forecasts create the possibility to plan the strategic response days and weeks in advance. The tool presented in this study is, as far as we know, the only one accounting not only for confirmed COVID-19 cases but also for suspected COVID-19 patients. Additionally, the few input parameters used are easy to access and can be easily adapted to other healthcare systems.


Subject(s)
Coronavirus Infections/therapy , Critical Care/organization & administration , Hospital Bed Capacity , Hospitals, University/organization & administration , Intensive Care Units/organization & administration , Pneumonia, Viral/therapy , Betacoronavirus/isolation & purification , COVID-19 , Coronavirus Infections/epidemiology , Critical Care/statistics & numerical data , Germany , Hospitals, University/statistics & numerical data , Humans , Intensive Care Units/statistics & numerical data , Pandemics , Pneumonia, Viral/epidemiology , Prognosis , SARS-CoV-2
7.
Am J Med ; 134(11): 1380-1388.e3, 2021 11.
Article in English | MEDLINE | ID: covidwho-1397151

ABSTRACT

BACKGROUND: Whether the volume of coronavirus disease 2019 (COVID-19) hospitalizations is associated with outcomes has important implications for the organization of hospital care both during this pandemic and future novel and rapidly evolving high-volume conditions. METHODS: We identified COVID-19 hospitalizations at US hospitals in the American Heart Association COVID-19 Cardiovascular Disease Registry with ≥10 cases between January and August 2020. We evaluated the association of COVID-19 hospitalization volume and weekly case growth indexed to hospital bed capacity, with hospital risk-standardized in-hospital case-fatality rate (rsCFR). RESULTS: There were 85 hospitals with 15,329 COVID-19 hospitalizations, with a median hospital case volume was 118 (interquartile range, 57, 252) and median growth rate of 2 cases per 100 beds per week but varied widely (interquartile range: 0.9 to 4.5). There was no significant association between overall hospital COVID-19 case volume and rsCFR (rho, 0.18, P = .09). However, hospitals with more rapid COVID-19 case-growth had higher rsCFR (rho, 0.22, P = 0.047), increasing across case growth quartiles (P trend = .03). Although there were no differences in medical treatments or intensive care unit therapies (mechanical ventilation, vasopressors), the highest case growth quartile had 4-fold higher odds of above median rsCFR, compared with the lowest quartile (odds ratio, 4.00; 1.15 to 13.8, P = .03). CONCLUSIONS: An accelerated case growth trajectory is a marker of hospitals at risk of poor COVID-19 outcomes, identifying sites that may be targets for influx of additional resources or triage strategies. Early identification of such hospital signatures is essential as our health system prepares for future health challenges.


Subject(s)
Bed Occupancy/statistics & numerical data , COVID-19 , Hospital Bed Capacity/statistics & numerical data , Intensive Care Units/statistics & numerical data , Mortality , Quality Improvement/organization & administration , COVID-19/mortality , COVID-19/therapy , Civil Defense , Health Care Rationing/organization & administration , Health Care Rationing/standards , Hospital Mortality , Hospitalization/statistics & numerical data , Humans , Outcome Assessment, Health Care , Registries , Risk Assessment , SARS-CoV-2 , Triage/organization & administration , United States/epidemiology
11.
J Prev Med Hyg ; 62(2): E261-E269, 2021 Jun.
Article in English | MEDLINE | ID: covidwho-1355278

ABSTRACT

Background: The COVID-19-related deaths are growing rapidly around the world, especially in Europe and the United States. Purpose: In this study we attempt to measure the association of these variables with case fatality rate (CFR) and recovery rate (RR) using up-to-date data from around the world. Methods: Data were collected from eight global databases. According to the raw data of countries, the CFR and RR and their relationship with different predictors was compared for countries with 1,000 or more cases of COVID-19 confirmed cases. Results: There were no significant correlation between the CFR and number of hospital beds per 1,000 people, proportion of population aged 65 and older ages, and the number of computed tomography per one million inhabitants. Furthermore, based on the continents-based subgroup univariate regression analysis, the population (R2 = 0.37, P = 0.047), GPD (R2 = 0.80, P < 0.001), number of ICU Beds per 100,000 people (R2 = 0.93, P = 0.04), and number of CT per one million inhabitants (R2 = 0.78, P = 0.04) were significantly correlated with CFR in America. Moreover, the income-based subgroups analysis showed that the gross domestic product (R2 = 0.30, P = 0.001), number of ICU Beds per 100,000 people (R2 = 0.23, P = 0.008), and the number of ventilator (R2 = 0.46, P = 0.01) had significant correlation with CFR in high-income countries. Conclusions: The level of country's preparedness, testing capacity, and health care system capacities also are among the important predictors of both COVID-19 associated mortality and recovery. Thus, providing up-to-date information on the main predictors of COVID-19 associated mortality and recovery will hopefully improve various countries hospital resource allocation, testing capacities, and level of preparedness.


Subject(s)
COVID-19 Testing/statistics & numerical data , COVID-19/epidemiology , COVID-19/mortality , Delivery of Health Care/standards , Hospital Bed Capacity , Pandemics , Resource Allocation , Age Distribution , Aged , Aged, 80 and over , COVID-19/complications , Comorbidity , Europe/epidemiology , Humans , SARS-CoV-2
12.
BMC Infect Dis ; 21(1): 700, 2021 Jul 22.
Article in English | MEDLINE | ID: covidwho-1322927

ABSTRACT

BACKGROUND: Predicting hospital length of stay (LoS) for patients with COVID-19 infection is essential to ensure that adequate bed capacity can be provided without unnecessarily restricting care for patients with other conditions. Here, we demonstrate the utility of three complementary methods for predicting LoS using UK national- and hospital-level data. METHOD: On a national scale, relevant patients were identified from the COVID-19 Hospitalisation in England Surveillance System (CHESS) reports. An Accelerated Failure Time (AFT) survival model and a truncation corrected method (TC), both with underlying Weibull distributions, were fitted to the data to estimate LoS from hospital admission date to an outcome (death or discharge) and from hospital admission date to Intensive Care Unit (ICU) admission date. In a second approach we fit a multi-state (MS) survival model to data directly from the Manchester University NHS Foundation Trust (MFT). We develop a planning tool that uses LoS estimates from these models to predict bed occupancy. RESULTS: All methods produced similar overall estimates of LoS for overall hospital stay, given a patient is not admitted to ICU (8.4, 9.1 and 8.0 days for AFT, TC and MS, respectively). Estimates differ more significantly between the local and national level when considering ICU. National estimates for ICU LoS from AFT and TC were 12.4 and 13.4 days, whereas in local data the MS method produced estimates of 18.9 days. CONCLUSIONS: Given the complexity and partiality of different data sources and the rapidly evolving nature of the COVID-19 pandemic, it is most appropriate to use multiple analysis methods on multiple datasets. The AFT method accounts for censored cases, but does not allow for simultaneous consideration of different outcomes. The TC method does not include censored cases, instead correcting for truncation in the data, but does consider these different outcomes. The MS method can model complex pathways to different outcomes whilst accounting for censoring, but cannot handle non-random case missingness. Overall, we conclude that data-driven modelling approaches of LoS using these methods is useful in epidemic planning and management, and should be considered for widespread adoption throughout healthcare systems internationally where similar data resources exist.


Subject(s)
COVID-19/therapy , Intensive Care Units/statistics & numerical data , Length of Stay/statistics & numerical data , Aged , COVID-19/epidemiology , Data Analysis , England/epidemiology , Female , Hospital Bed Capacity , Hospital Planning/methods , Humans , Male , Middle Aged
13.
J Ambul Care Manage ; 43(4): 306-307, 2020.
Article in English | MEDLINE | ID: covidwho-1319201
15.
Ann Surg ; 273(5): 844-849, 2021 05 01.
Article in English | MEDLINE | ID: covidwho-1304017

ABSTRACT

OBJECTIVE: We sought to quantify the financial impact of elective surgery cancellations in the US during COVID-19 and simulate hospitals' recovery times from a single period of surgery cessation. BACKGROUND: COVID-19 in the US resulted in cessation of elective surgery-a substantial driver of hospital revenue-and placed patients at risk and hospitals under financial stress. We sought to quantify the financial impact of elective surgery cancellations during the pandemic and simulate hospitals' recovery times. METHODS: Elective surgical cases were abstracted from the Nationwide Inpatient Sample (2016-2017). Time series were utilized to forecast March-May 2020 revenues and demand. Sensitivity analyses were conducted to calculate the time to clear backlog cases and match expected ongoing demand in the post-COVID period. Subset analyses were performed by hospital region and teaching status. RESULTS: National revenue loss due to major elective surgery cessation was estimated to be $22.3 billion (B). Recovery to market equilibrium was conserved across strata and influenced by pre- and post-COVID capacity utilization. Median recovery time was 12-22 months across all strata. Lower pre-COVID utilization was associated with fewer months to recovery. CONCLUSIONS: Strategies to mitigate the predicted revenue loss of $22.3B due to major elective surgery cessation will vary with hospital-specific supply-demand equilibrium. If patient demand is slow to return, hospitals should focus on marketing of services; if hospital capacity is constrained, efficient capacity expansion may be beneficial. Finally, rural and urban nonteaching hospitals may face increased financial risk which may exacerbate care disparities.


Subject(s)
COVID-19/prevention & control , Elective Surgical Procedures/economics , Financial Management, Hospital , Hospital Costs , Pandemics/prevention & control , Quarantine , Female , Healthcare Disparities/economics , Hospital Bed Capacity , Humans , Male , Middle Aged , SARS-CoV-2 , Time Factors , United States
16.
Ann Intern Med ; 174(9): 1240-1251, 2021 09.
Article in English | MEDLINE | ID: covidwho-1296184

ABSTRACT

BACKGROUND: Several U.S. hospitals had surges in COVID-19 caseload, but their effect on COVID-19 survival rates remains unclear, especially independent of temporal changes in survival. OBJECTIVE: To determine the association between hospitals' severity-weighted COVID-19 caseload and COVID-19 mortality risk and identify effect modifiers of this relationship. DESIGN: Retrospective cohort study. (ClinicalTrials.gov: NCT04688372). SETTING: 558 U.S. hospitals in the Premier Healthcare Database. PARTICIPANTS: Adult COVID-19-coded inpatients admitted from March to August 2020 with discharge dispositions by October 2020. MEASUREMENTS: Each hospital-month was stratified by percentile rank on a surge index (a severity-weighted measure of COVID-19 caseload relative to pre-COVID-19 bed capacity). The effect of surge index on risk-adjusted odds ratio (aOR) of in-hospital mortality or discharge to hospice was calculated using hierarchical modeling; interaction by surge attributes was assessed. RESULTS: Of 144 116 inpatients with COVID-19 at 558 U.S. hospitals, 78 144 (54.2%) were admitted to hospitals in the top surge index decile. Overall, 25 344 (17.6%) died; crude COVID-19 mortality decreased over time across all surge index strata. However, compared with nonsurging (<50th surge index percentile) hospital-months, aORs in the 50th to 75th, 75th to 90th, 90th to 95th, 95th to 99th, and greater than 99th percentiles were 1.11 (95% CI, 1.01 to 1.23), 1.24 (CI, 1.12 to 1.38), 1.42 (CI, 1.27 to 1.60), 1.59 (CI, 1.41 to 1.80), and 2.00 (CI, 1.69 to 2.38), respectively. The surge index was associated with mortality across ward, intensive care unit, and intubated patients. The surge-mortality relationship was stronger in June to August than in March to May (slope difference, 0.10 [CI, 0.033 to 0.16]) despite greater corticosteroid use and more judicious intubation during later and higher-surging months. Nearly 1 in 4 COVID-19 deaths (5868 [CI, 3584 to 8171]; 23.2%) was potentially attributable to hospitals strained by surging caseload. LIMITATION: Residual confounding. CONCLUSION: Despite improvements in COVID-19 survival between March and August 2020, surges in hospital COVID-19 caseload remained detrimental to survival and potentially eroded benefits gained from emerging treatments. Bolstering preventive measures and supporting surging hospitals will save many lives. PRIMARY FUNDING SOURCE: Intramural Research Program of the National Institutes of Health Clinical Center, the National Institute of Allergy and Infectious Diseases, and the National Cancer Institute.


Subject(s)
COVID-19/mortality , Hospitalization/statistics & numerical data , Adrenal Cortex Hormones/therapeutic use , Adult , COVID-19/therapy , Critical Care/statistics & numerical data , Female , Hospital Bed Capacity/statistics & numerical data , Hospital Mortality , Humans , Male , Odds Ratio , Respiration, Artificial , Retrospective Studies , Risk Assessment , Risk Factors , SARS-CoV-2 , Survival Rate , United States/epidemiology
18.
Med Care ; 59(5): 371-378, 2021 05 01.
Article in English | MEDLINE | ID: covidwho-1254915

ABSTRACT

BACKGROUND: Planning for extreme surges in demand for hospital care of patients requiring urgent life-saving treatment for coronavirus disease 2019 (COVID-19), while retaining capacity for other emergency conditions, is one of the most challenging tasks faced by health care providers and policymakers during the pandemic. Health systems must be well-prepared to cope with large and sudden changes in demand by implementing interventions to ensure adequate access to care. We developed the first planning tool for the COVID-19 pandemic to account for how hospital provision interventions (such as cancelling elective surgery, setting up field hospitals, or hiring retired staff) will affect the capacity of hospitals to provide life-saving care. METHODS: We conducted a review of interventions implemented or considered in 12 European countries in March to April 2020, an evaluation of their impact on capacity, and a review of key parameters in the care of COVID-19 patients. This information was used to develop a planner capable of estimating the impact of specific interventions on doctors, nurses, beds, and respiratory support equipment. We applied this to a scenario-based case study of 1 intervention, the set-up of field hospitals in England, under varying levels of COVID-19 patients. RESULTS: The Abdul Latif Jameel Institute for Disease and Emergency Analytics pandemic planner is a hospital planning tool that allows hospital administrators, policymakers, and other decision-makers to calculate the amount of capacity in terms of beds, staff, and crucial medical equipment obtained by implementing the interventions. Flexible assumptions on baseline capacity, the number of hospitalizations, staff-to-beds ratios, and staff absences due to COVID-19 make the planner adaptable to multiple settings. The results of the case study show that while field hospitals alleviate the burden on the number of beds available, this intervention is futile unless the deficit of critical care nurses is addressed first. DISCUSSION: The tool supports decision-makers in delivering a fast and effective response to the pandemic. The unique contribution of the planner is that it allows users to compare the impact of interventions that change some or all inputs.


Subject(s)
COVID-19 , Health Planning Guidelines , Health Services Needs and Demand , Hospitals , Surge Capacity , Workforce , Critical Care Nursing , England , Equipment and Supplies, Hospital , Health Personnel , Hospital Bed Capacity , Humans
19.
Dig Surg ; 38(4): 259-265, 2021.
Article in English | MEDLINE | ID: covidwho-1247450

ABSTRACT

BACKGROUND: The first COVID-19 pandemic wave hit most of the health-care systems worldwide. The present survey aimed to provide a European overview on the COVID-19 impact on surgical oncology. METHODS: This anonymous online survey was accessible from April 24 to May 11, 2020, for surgeons (n = 298) who were contacted by the surgical society European Digestive Surgery. The survey was completed by 88 surgeons (29.2%) from 69 different departments. The responses per department were evaluated. RESULTS: Of the departments, 88.4% (n = 61/69) reported a lower volume of patients in the outpatient clinic; 69.1% (n = 47/68) and 75.0% (n = 51/68) reported a reduction in hospital bed and the operating room capacity, respectively. As a result, the participants reported an average reduction of 29.3% for all types of oncological resections surveyed in this questionnaire. The strongest reduction was observed for oncological resections of hepato-pancreatico-biliary (HPB) cancers. Of the interviewed surgeons, 68.7% (n = 46/67) agreed that survival outcomes will be negatively impacted by the pandemic. CONCLUSION: The first COVID-19 pandemic wave had a significant impact on surgical oncology in Europe. The surveyed surgeons expect an increase in the number of unresectable cancers as well as poorer survival outcomes due to cancellations of follow-ups and postponements of surgeries.


Subject(s)
COVID-19/epidemiology , Hospital Bed Capacity/statistics & numerical data , Neoplasms/surgery , Oncology Service, Hospital/statistics & numerical data , Surgical Oncology/statistics & numerical data , Adult , Ambulatory Care/statistics & numerical data , COVID-19/diagnosis , Chemotherapy, Adjuvant/statistics & numerical data , Cross-Sectional Studies , Europe/epidemiology , Female , Humans , Male , Middle Aged , Neoplasms/diagnosis , Neoplasms/drug therapy , Operating Rooms/statistics & numerical data , Surveys and Questionnaires , Survival Rate , Time-to-Treatment/statistics & numerical data
20.
Crit Care Med ; 49(7): 1038-1048, 2021 07 01.
Article in English | MEDLINE | ID: covidwho-1246785

ABSTRACT

OBJECTIVES: The coronavirus disease 2019 pandemic has strained many healthcare systems. In response, U.S. hospitals altered their care delivery systems, but there are few data regarding specific structural changes. Understanding these changes is important to guide interpretation of outcomes and inform pandemic preparedness. We sought to characterize emergency responses across hospitals in the United States over time and in the context of local case rates early in the coronavirus disease 2019 pandemic. DESIGN: We surveyed hospitals from a national acute care trials group regarding operational and structural changes made in response to the coronavirus disease 2019 pandemic from January to August 2020. We collected prepandemic characteristics and changes to hospital system, space, staffing, and equipment during the pandemic. We compared the timing of these changes with county-level coronavirus disease 2019 case rates. SETTING AND PARTICIPANTS: U.S. hospitals participating in the Prevention and Early Treatment of Acute Lung Injury Network Coronavirus Disease 2019 Observational study. Site investigators at each hospital collected local data. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Forty-five sites participated (94% response rate). System-level changes (incident command activation and elective procedure cancellation) occurred at nearly all sites, preceding rises in local case rates. The peak inpatient census during the pandemic was greater than the prior hospital bed capacity in 57% of sites with notable regional variation. Nearly half (49%) expanded ward capacity, and 63% expanded ICU capacity, with nearly all bed expansion achieved through repurposing of clinical spaces. Two-thirds of sites adapted staffing to care for patients with coronavirus disease 2019, with 48% implementing tiered staffing models, 49% adding temporary physicians, nurses, or respiratory therapists, and 30% changing the ratios of physicians or nurses to patients. CONCLUSIONS: The coronavirus disease 2019 pandemic prompted widespread system-level changes, but front-line clinical care varied widely according to specific hospital needs and infrastructure. Linking operational changes to care delivery processes is a necessary step to understand the impact of the coronavirus disease 2019 pandemic on patient outcomes.


Subject(s)
COVID-19/epidemiology , Delivery of Health Care/organization & administration , Hospitals , Surge Capacity/organization & administration , Critical Care/organization & administration , Hospital Bed Capacity , Humans , Intensive Care Units/organization & administration , SARS-CoV-2 , Surveys and Questionnaires , United States/epidemiology , Workforce/organization & administration
SELECTION OF CITATIONS
SEARCH DETAIL
...