Leaving the hospital on time: hospital bed utilization and reasons for discharge delay in the Netherlands.

Inappropriate bed occupancy due to delayed hospital discharge affects both physical and psychological well-being in patients and can disrupt patient flow. The Dutch healthcare system is facing ongoing pressure, especially during the current coronavirus disease pandemic, intensifying the need for optimal use of hospital beds. The aim of this study was to quantify inappropriate patient stays and describe the underlying reasons for the delays in discharge. The Day of Care Survey (DoCS) is a validated tool used to gain information about appropriate and inappropriate bed occupancy in hospitals. Between February 2019 and January 2021, the DoCS was performed five times in three different hospitals within the region of Amsterdam, the Netherlands. All inpatients were screened, using standardized criteria, for their need for in-hospital care at the time of survey and reasons for discharge delay. A total of 782 inpatients were surveyed. Of these patients, 94 (12%) were planned for definite discharge that day. Of all other patients, 145 (21%, ranging from 14% to 35%) were without the need for acute in-hospital care. In 74% (107/145) of patients, the reason for discharge delay was due to issues outside the hospital; most frequently due to a shortage of available places in care homes (26%, 37/145). The most frequent reason for discharge delay inside the hospital was patients awaiting a decision or review by the treating physician (14%, 20/145). Patients who did not meet the criteria for hospital stay were, in general, older [median 75, interquartile range (IQR) 65-84 years, and 67, IQR 55-75 years, respectively, P < .001] and had spent more days in hospital (7, IQR 5-14 days, and 3, IQR 1-8 days respectively, P < .001). Approximately one in five admitted patients occupying hospital beds did not meet the criteria for acute in-hospital stay or care at the time of the survey. Most delays were related to issues outside the immediate control of the hospital. Improvement programmes working with stakeholders focusing on the transfer from hospital to outside areas of care need to be further developed and may offer potential for the greatest gain. The DoCS can be a tool to periodically monitor changes and improvements in patient flow.

Managing Interhospital Referrals During a COVID-19 Patient Surge in Japan: Creating Available Beds by Exchanging Patients.

A COVID-19 patient surge in Japan from July to September 2021 caused a mismatch between patient severity and bed types because hospital beds were fully occupied and patient referrals between hospitals stagnated. Japan's predominantly private healthcare system lacks effective mechanisms to coordinate healthcare providers to address the mismatch. To address the surge, in August 2021, Tokyo Saiseikai Central Hospital started a scheme to exchange patients with other hospitals to mitigate the mismatch. In this article, we outline a retrospective observational study using medical records from a tertiary care medical center that treated severe COVID-19 cases. We describe daily patient admissions to our hospital's COVID-19 beds from July to September 2021, and compared the moving average of daily admissions before and after the exchange scheme was introduced. Bed occupancy reached nearly 100% in late July when the patient surge began and continued to exceed 100% in August when the surge peaked. However, the average daily admission did not decrease in August compared with July: the median daily admission (25th to 75th percentile) during each period was 2 (1 to 2.5) in late July and 3 (2 to 4) in August. The number of patients referred in from secondary care hospitals and the number of patients referred out was balanced in August. During the patient surge, the exchange scheme enabled the hospital to maintain and even increase the number of new admissions despite the bed shortage. Coordinating patient referrals in both directions simultaneously, rather than the usual 1-way transfer, can mitigate such mismatches.

Emergency department crowding and hospital transformation during COVID-19, a retrospective, descriptive study of a university hospital in Stockholm, Sweden.

OBJECTIVES: COVID-19 presents challenges to the emergency care system that could lead to emergency department (ED) crowding. The Huddinge site at the Karolinska university hospital (KH) responded through a rapid transformation of inpatient care capacity together with changing working methods in the ED. The aim is to describe the KH response to the COVID-19 crisis, and how ED crowding, and important input, throughput and output factors for ED crowding developed at KH during a 30-day baseline period followed by the first 60 days of the COVID-19 outbreak in Stockholm Region. METHODS: Different phases in the development of the crisis were described and identified retrospectively based on major events that changed the conditions for the ED. Results were presented for each phase separately. The outcome ED length of stay (ED LOS) was calculated with mean and 95% confidence intervals. Input, throughput, output and demographic factors were described using distributions, proportions and means. Pearson correlation between ED LOS and emergency ward occupancy by phase was estimated with 95% confidence interval. RESULTS: As new working methods were introduced between phase 2 and 3, ED LOS declined from mean (95% CI) 386 (373-399) minutes to 307 (297-317). Imaging proportion was reduced from 29 to 18% and admission rate increased from 34 to 43%. Correlation (95% CI) between emergency ward occupancy and ED LOS by phase was 0.94 (0.55-0.99). CONCLUSIONS: It is possible to avoid ED crowding, even during extreme and quickly changing conditions by leveraging previously known input, throughput and output factors. One key factor was the change in working methods in the ED with higher competence, less diagnostics and increased focus on rapid clinical admission decisions. Another important factor was the reduction in bed occupancy in emergency wards that enabled a timely admission to inpatient care. A key limitation was the retrospective study design.

Using Geocoding to Identify COVID-19 Outbreaks in Congregate Residential Settings: San Francisco's Outbreak Response in Single-Room Occupancy Hotels.

More than 500 single-room occupancy hotels (SROs), a type of low-cost congregate housing with shared bathrooms and kitchens, are available in San Francisco. SRO residents include essential workers, people with disabilities, and multigenerational immigrant families. In March 2020, with increasing concerns about the potential for rapid transmission of COVID-19 among a population with disproportionate rates of comorbidity, poor access to care, and inability to self-isolate, the San Francisco Department of Public Health formed an SRO outbreak response team to identify and contain COVID-19 clusters in this congregate residential setting. Using address-matching geocoding, the team conducted active surveillance to identify new cases and outbreaks of COVID-19 at SROs. An outbreak was defined as 3 separate households in the SRO with a positive test result for COVID-19. From March 2020 through February 2021, the SRO outbreak response team conducted on-site mass testing of all residents at 52 SROs with outbreaks identified through geocoding. The rate of positive COVID-19 tests was significantly higher at SROs with outbreaks than at SROs without outbreaks (12.7% vs 6.4%; P < .001). From March through May 2020, the rate of COVID-19 cases among SRO residents was higher than among residents of other settings (ie, non-SRO residents), before decreasing and remaining at an equal level to non-SRO residents during later periods of 2020. The annual case fatality rate for SRO residents and non-SRO residents was similar (1.8% vs 1.5%). This approach identified outbreaks in a setting at high risk of COVID-19 and facilitated rapid deployment of resources. The geocoding surveillance approach could be used for other diseases and in any setting for which a list of addresses is available.

An average of nearly 200,000 new infections per day over a six-week period: What is the impact of such a severe COVID-19 pandemic on the healthcare system in Japan?

During a six-week period from July 20 to August 31, 2022, Japan experienced its highest level of COVID-19 infection ever, with an average of nearly 200,000 new infections per day nationwide. Cases requiring inpatient care peaked at 1,993,062. Twenty-seven prefectures (out of 47 prefectures) had an average hospital bed occupancy of 50% or higher, and bed occupancy in Kanagawa in particular reached 98% in mid-August. In Tokyo, bed occupancy by patients with severe COVID-19 reached 57% and peaked at 64% in mid-August. Although the number of new infections per day has decreased since September, hospital bed occupancy, the number of severe cases, and deaths remain high nationwide. Efforts including vaccination campaigns, domestic surveillance, and routine infection control measures based on the varied knowledge that the Japanese public already has should be thoroughly implemented to reduce the number of the infected in order to avoid an increase the number of serious cases and deaths.

The cross-scale correlations between individuals and nations in COVID-19 mortality.

It is challenging to quantitatively clarify the determining medical and social factors of COVID-19 mortality, which varied by 2 to 3 orders of magnitude across countries. Here, we present evidence that the temporal evolution of mortality follows a logistic law for 54 countries in four waves. A universal linear law is found between the early mortality growth time and the epidemic duration, one of the most important quantities, with a factor of 7.3 confirmed by data. Saturation mortality is found to have a power law relationship with median age and bed occupancy, which quantitatively explains the great variation in mortality based on the two key thresholds of median age (= 38) and bed occupancy (= 22%). We predict that deaths will be reduced by 38.5% when the number of beds is doubled for countries with older populations. Facing the next wave of the epidemic, this model can make early predictions on the epidemic duration and hospital bed demand.

Acute respiratory infection rates in primary care anticipate ICU bed occupancy during COVID-19 waves.

BACKGROUND: Bed occupancy in the ICU is a major constraint to in-patient care during COVID-19 pandemic. Diagnoses of acute respiratory infection (ARI) by general practitioners have not previously been investigated as an early warning indicator of ICU occupancy. METHODS: A population-based central health care system registry in the autonomous community of Catalonia, Spain, was used to analyze all diagnoses of ARI related to COVID-19 established by general practitioners and the number of occupied ICU beds in all hospitals from Catalonia between March 26, 2020 and January 20, 2021. The primary outcome was the cross-correlation between the series of COVID-19-related ARI cases and ICU bed occupancy taking into account the effect of bank holidays and weekends. Recalculations were later implemented until March 27, 2022. FINDINGS: Weekly average incidence of ARI diagnoses increased from 252.7 per 100,000 in August, 2020 to 496.5 in October, 2020 (294.2 in November, 2020), while the average number of ICU beds occupied by COVID-19-infected patients rose from 1.7 per 100,000 to 3.5 in the same period (6.9 in November, 2020). The incidence of ARI detected in the primary care setting anticipated hospital occupancy of ICUs, with a maximum correlation of 17.3 days in advance (95% confidence interval 15.9 to 18.9). INTERPRETATION: COVID-19-related ARI cases may be a novel warning sign of ICU occupancy with a delay of over two weeks, a latency window period for establishing restrictions on social contacts and mobility to mitigate the propagation of COVID-19. Monitoring ARI cases would enable immediate adoption of measures to prevent ICU saturation in future waves.

Hospital bed occupancy rate is an independent risk factor for COVID-19 inpatient mortality: a pandemic epicentre cohort study.

INTRODUCTION: COVID-19 first struck New York City in the spring of 2020, resulting in an unprecedented strain on our healthcare system and triggering multiple changes in public health policy governing hospital operations as well as therapeutic approaches to COVID-19. We examined inpatient mortality at our centre throughout the course of the pandemic. METHODS: This is a retrospective chart review of clinical characteristics, treatments and outcome data of all patients admitted with COVID-19 from 1 March 2020 to 28 February 2021. Patients were grouped into 3-month quartiles. Hospital strain was assessed as per cent of occupied beds based on a normal bed capacity of 1491. RESULTS: Inpatient mortality decreased from 25.0% in spring to 10.8% over the course of the year. During this time, use of remdesivir, steroids and anticoagulants increased; use of hydroxychloroquine and other antibiotics decreased. Daily bed occupancy ranged from 62% to 118%. In a multivariate model with all year's data controlling for demographics, comorbidities and acuity of illness, percentage of bed occupancy was associated with increased 30-day in-hospital mortality of patients with COVID-19 (0.7% mortality increase for each 1% increase in bed occupancy; HR 1.007, CI 1.001 to 1.013, p=0.004) CONCLUSION: Inpatient mortality from COVID-19 was associated with bed occupancy. Early reduction in epicentre hospital bed occupancy to accommodate acutely ill and resource-intensive patients should be a critical component in the strategic planning for future pandemics.

Association Between Pandemic Coronavirus Disease 2019 Public Health Measures and Reduction in Critical Care Utilization Across ICUs in Alberta, Canada.

OBJECTIVES: The coronavirus disease 2019 pandemic has disrupted critical care services across the world. In anticipation of surges in the need for critical care services, governments implemented "lockdown" measures to preserve and create added critical care capacity. Herein, we describe the impact of lockdown measures on the utilization of critical care services and patient outcomes compared with nonlockdown epochs in a large integrated health region. DESIGN: This was a population-based retrospective cohort study. SETTING: Seventeen adult ICUs across 14 acute care hospitals in Alberta, Canada. PATIENTS: All adult (age ≥ 15 yr) patients admitted to any study ICU. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The main exposure was ICU admission during "lockdown" occurring between March 16, 2020, and June 30, 2020. This period was compared with two nonpandemic control periods: "year prior" (March 16, 2019, to June 30, 2019) and "pre lockdown" immediately prior (November 30, 2019, to March 15, 2020). The primary outcome was the number of ICU admissions. Secondary outcomes included the following: daily measures of ICU utilization, ICU duration of stay, avoidable delay in ICU discharge, and occupancy; and patient outcomes. Mixed multilevel negative binomial regression and interrupted time series regression were used to compare rates of ICU admissions between periods. Multivariable regressions were used to compare patient outcomes between periods. During the lockdown, there were 3,649 ICU admissions (34.1 [8.0] ICU admissions/d), compared with 4,125 (38.6 [9.3]) during the prelockdown period and 3,919 (36.6 [8.7]) during the year prior. Mean bed occupancy declined significantly during the lockdown compared with the nonpandemic periods (78.7%, 95.9%, and 96.4%; p < 0.001). Avoidable ICU discharge delay also decreased significantly (42.0%, 53.2%, and 58.3%; p < 0.001). During the lockdown, patients were younger, had fewer comorbid diseases, had higher acuity, and were more likely to be medical admissions compared with the nonpandemic periods. Adjusted ICU and hospital mortality and ICU and hospital lengths of stay were significantly lower during the lockdown compared with nonpandemic periods. CONCLUSIONS: The coronavirus disease 2019 lockdown resulted in substantial changes to ICU utilization, including a reduction in admissions, occupancy, patient lengths of stay, and mortality.

Remdesivir and dexamethasone as tools to relieve hospital care systems stressed by COVID-19: A modelling study on bed resources and budget impact.

Remdesivir and dexamethasone are the only drugs providing reductions in the lengths of hospital stays for COVID-19 patients. We assessed the impacts of remdesivir on hospital-bed resources and budgets affected by the COVID-19 outbreak. A stochastic agent-based model was combined with epidemiological data available on the COVID-19 outbreak in France and data from two randomized control trials. Strategies involving treating with remdesivir only patients with low-flow oxygen and patients with low-flow and high-flow oxygen were examined. Treating all eligible low-flow oxygen patients during the entirety of the second wave would have decreased hospital-bed occupancy in conventional wards by 4% [2%; 7%] and intensive care unit (ICU)-bed occupancy by 9% [6%; 13%]. Extending remdesivir use to high-flow-oxygen patients would have amplified reductions in ICU-bed occupancy by up to 14% [18%; 11%]. A minimum remdesivir uptake of 20% was required to observe decreases in bed occupancy. Dexamethasone had effects of similar amplitude. Depending on the treatment strategy, using remdesivir would, in most cases, generate savings (up to 722€) or at least be cost neutral (an extra cost of 34€). Treating eligible patients could significantly limit the saturation of hospital capacities, particularly in ICUs. The generated savings would exceed the costs of medications.

Rhythmicity of patient flow in an acute medical unit: relationship to hospital occupancy, 7-day working and the effect of COVID-19.

BACKGROUND: The Acute Medical Unit (AMU) provides care for unscheduled hospital admissions. Seven-day consultant presence and morning AMU discharges have been advocated to improve hospital bed management. AIMS: To determine whether a later time of daily peak AMU occupancy correlates with measures of hospital stress; whether 7-day consultant presence, for COVID-19, abolished weekly periodicity of discharges. DESIGN: Retrospective cohort analysis. METHODS: : Anonymised AMU admission and discharge times were retrieved from the Profile Information Management System (PIMS), at a large, urban hospital from 14 April 2014 to 31 December 2018 and 20 March to 2 May 2020 (COVID-19 peak). Minute-by-minute admission and discharge times were combined to construct a running total of AMU bed occupancy. Fourier transforms were used to determine periodicity. We tested association between (i) average AMU occupancy and (ii) time of peak AMU occupancy, with measures of hospital stress (total medical bed occupancy and 'medical outliers' on non-medical wards). RESULTS: : Daily, weekly and seasonal patterns of AMU bed occupancy were evident. Timing of AMU peak occupancy was unrelated to each measure of hospital stress: total medical inpatients (Spearman's rho, rs = 0.04, P = 0.24); number of medical outliers (rs = -0.06, P = 0.05). During COVID-19, daily bed occupancy was similar, with continuation of greater Friday and Monday discharges than the weekend. CONCLUSIONS: : Timing of peak AMU occupancy did not alter with hospital stress. Efforts to increase morning AMU discharges are likely to have little effect on hospital performance. Seven-day consultant presence did not abolish weekly periodicity of discharges-other factors influence weekend discharges.

Impact of Hospital Strain on Excess Deaths During the COVID-19 Pandemic - United States, July 2020-July 2021.

Surges in COVID-19 cases have stressed hospital systems, negatively affected health care and public health infrastructures, and degraded national critical functions (1,2). Resource limitations, such as available hospital space, staffing, and supplies led some facilities to adopt crisis standards of care, the most extreme operating condition for hospitals, in which the focus of medical decision-making shifted from achieving the best outcomes for individual patients to addressing the immediate care needs of larger groups of patients (3). When hospitals deviated from conventional standards of care, many preventive and elective procedures were suspended, leading to the progression of serious conditions among some persons who would have benefitted from earlier diagnosis and intervention (4). During March-May 2020, U.S. emergency department visits declined by 23% for heart attacks, 20% for strokes, and 10% for diabetic emergencies (5). The Cybersecurity & Infrastructure Security Agency (CISA) COVID Task Force* examined the relationship between hospital strain and excess deaths during July 4, 2020-July 10, 2021, to assess the impact of COVID-19 surges on hospital system operations and potential effects on other critical infrastructure sectors and national critical functions. The study period included the months during which the highly transmissible SARS-CoV-2 B.1.617.2 (Delta) variant became predominant in the United States.† The negative binomial regression model used to calculate estimated deaths predicted that, if intensive care unit (ICU) bed use nationwide reached 75% capacity an estimated 12,000 additional excess deaths would occur nationally over the next 2 weeks. As hospitals exceed 100% ICU bed capacity, 80,000 excess deaths would be expected in the following 2 weeks. This analysis indicates the importance of controlling case growth and subsequent hospitalizations before severe strain. State, local, tribal, and territorial leaders could evaluate ways to reduce strain on public health and health care infrastructures, including implementing interventions to reduce overall disease prevalence such as vaccination and other prevention strategies, as well as ways to expand or enhance capacity during times of high disease prevalence.

Trends in 28-Day Mortality of Critical Care Patients With Coronavirus Disease 2019 in the United Kingdom: A National Cohort Study, March 2020 to January 2021.

OBJECTIVES: To determine whether the previously described trend of improving mortality in people with coronavirus disease 2019 in critical care during the first wave was maintained, plateaued, or reversed during the second wave in United Kingdom, when B117 became the dominant strain. DESIGN: National retrospective cohort study. SETTING: All English hospital trusts (i.e., groups of hospitals functioning as single operational units), reporting critical care admissions (high dependency unit and ICU) to the Coronavirus Disease 2019 Hospitalization in England Surveillance System. PATIENTS: A total of 49,862 (34,336 high dependency unit and 15,526 ICU) patients admitted between March 1, 2020, and January 31, 2021 (inclusive). INTERVENTIONS: Not applicable. MEASUREMENTS AND MAIN RESULTS: The primary outcome was inhospital 28-day mortality by calendar month of admission, from March 2020 to January 2021. Unadjusted mortality was estimated, and Cox proportional hazard models were used to estimate adjusted mortality, controlling for age, sex, ethnicity, major comorbidities, social deprivation, geographic location, and operational strain (using bed occupancy as a proxy). Mortality fell to trough levels in June 2020 (ICU: 22.5% [95% CI, 18.2-27.4], high dependency unit: 8.0% [95% CI, 6.4-9.6]) but then subsequently increased up to January 2021: (ICU: 30.6% [95% CI, 29.0-32.2] and high dependency unit, 16.2% [95% CI, 15.3-17.1]). Comparing patients admitted during June-September 2020 with those admitted during December 2020-January 2021, the adjusted mortality was 59% (CI range, 39-82) higher in high dependency unit and 88% (CI range, 62-118) higher in ICU for the later period. This increased mortality was seen in all subgroups including those under 65. CONCLUSIONS: There was a marked deterioration in outcomes for patients admitted to critical care at the peak of the second wave of coronavirus disease 2019 in United Kingdom (December 2020-January 2021), compared with the post-first-wave period (June 2020-September 2020). The deterioration was independent of recorded patient characteristics and occupancy levels. Further research is required to determine to what extent this deterioration reflects the impact of the B117 variant of concern.

Smart testing and critical care bed sharing for COVID-19 control.

During the early months of the current COVID-19 pandemic, social distancing measures effectively slowed disease transmission in many countries in Europe and Asia, but the same benefits have not been observed in some developing countries such as Brazil. In part, this is due to a failure to organise systematic testing campaigns at nationwide or even regional levels. To gain effective control of the pandemic, decision-makers in developing countries, particularly those with large populations, must overcome difficulties posed by an unequal distribution of wealth combined with low daily testing capacities. The economic infrastructure of these countries, often concentrated in a few cities, forces workers to travel from commuter cities and rural areas, which induces strong nonlinear effects on disease transmission. In the present study, we develop a smart testing strategy to identify geographic regions where COVID-19 testing could most effectively be deployed to limit further disease transmission. By smart testing we mean the testing protocol that is automatically designed by our optimization platform for a given time period, knowing the available number of tests, the current availability of ICU beds and the initial epidemiological situation. The strategy uses readily available anonymised mobility and demographic data integrated with intensive care unit (ICU) occupancy data and city-specific social distancing measures. Taking into account the heterogeneity of ICU bed occupancy in differing regions and the stages of disease evolution, we use a data-driven study of the Brazilian state of Sao Paulo as an example to show that smart testing strategies can rapidly limit transmission while reducing the need for social distancing measures, even when testing capacity is limited.

Association of COVID-19 Hospitalization Volume and Case Growth at US Hospitals with Patient Outcomes.

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.