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INTRODUCTION: Traditionally, wards in acute care hospitals consist predominately of multioccupancy bays with some single rooms. There is an increasing global trend towards a higher proportion of single rooms in hospitals, with the UK National Health Service (NHS) advocating for single-room provision in all new hospital builds. There is limited evidence on the impact of a ward environment incorporating mostly single and some multioccupancy bays on patient care and organisational outcomes. METHODS AND ANALYSES: This study will assess the impact of a newly designed 28-bedded ward environment, with 20 single rooms and two four-bedded bays, on patient and staff experiences and outcomes in an acute NHS Trust in East England. The study is divided into two work packages (WP)-WP1 is a quantitative data extraction of routinely collected patient and staff data while WP2 is a mixed-methods process evaluation consisting of one-to-one, in-depth, semistructured interviews with staff, qualitative observations of work processes on the ward and a quantitative data evaluation of routinely collected process evaluation data from patients and staff. ETHICS AND DISSEMINATION: Ethical approval was obtained from the UK Health Research Authority (IRAS ID: 334395). Study findings will be shared with key stakeholders, published in peer-reviewed high-impact journals and presented at relevant conferences.

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Three models/methods are given to understand the extreme international variation in available and occupied hospital bed numbers. These models/methods all rely on readily available data. In the first, occupied beds (rather than available beds) are used to measure the expressed demand for hospital beds. The expressed occupied bed demand for three countries was in the order Australia > England > USA. Next, the age-standardized mortality rate (ASMR) has dual functions. Less developed countries/regions have low access to healthcare, which results in high ASMR, or a negative slope between ASMR versus available/occupied beds. In the more developed countries, high ASMR can also be used to measure the 'need' for healthcare (including occupied beds), a positive slope among various social (wealth/lifestyle) groups, which will include Indigenous peoples. In England, a 100-unit increase in ASMR (European Standard population) leads to a 15.3-30.7 (feasible range) unit increase in occupied beds per 1000 deaths. Higher ASMR shows why the Australian states of the Northern Territory and Tasmania have an intrinsic higher bed demand. The USA has a high relative ASMR (for a developed/wealthy country) because healthcare is not universal in the widest sense. Lastly, a method for benchmarking the whole hospital's average bed occupancy which enables them to run at optimum efficiency and safety. English hospitals operate at highly disruptive and unsafe levels of bed occupancy, manifesting as high 'turn-away'. Turn-away implies bed unavailability for the next arriving patient. In the case of occupied beds, the slope of the relationship between occupied beds per 1000 deaths and deaths per 1000 population shows a power law function. Scatter around the trend line arising from year-to-year fluctuations in occupied beds per 1000 deaths, ASMR, deaths per 1000 population, changes in the number of persons hidden in the elective, outpatient and diagnostic waiting lists, and local area variation in births affecting maternity, neonatal, and pediatric bed demand. Additional variation will arise from differences in the level of local funding for social care, especially elderly care. The problems associated with crafting effective bed planning are illustrated using the English NHS as an example.

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This paper presents the design and evaluation of an engagement-free and contactless vital signs and occupancy monitoring system called BedDot. While many existing works demonstrated contactless vital signs estimation, they do not address the practical challenge of environment noises, online bed occupancy detection and data quality assessment in the realworld environment. This work presents a robust signal quality assessment algorithm consisting of three parts: bed occupancy detection, movement detection, and heartbeat detection, to identify high-quality data. It also presents a series of innovative vital signs estimation algorithms that leverage the advanced signal processing and Bayesian theorem for contactless heart rate (HR), respiration rate (RR), and inter-beat interval (IBI) estimation. The experimental results demonstrate that BedDot achieves over 99% accuracy for bed occupancy detection, and MAE of 1.38 BPM, 1.54 BPM, and 24.84 ms for HR, RR, and IBI estimation, respectively, compared with an FDA-approved device. The BedDot system has been extensively tested with data collected from 75 subjects for more than 80 hours under different conditions, demonstrating its generalizability across different people and environments.

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BACKGROUND: Predicting the bed occupancy rate (BOR) is essential for efficient hospital resource management, long-term budget planning, and patient care planning. Although macro-level BOR prediction for the entire hospital is crucial, predicting occupancy at a detailed level, such as specific wards and rooms, is more practical and useful for hospital scheduling. OBJECTIVE: The aim of this study was to develop a web-based support tool that allows hospital administrators to grasp the BOR for each ward and room according to different time periods. METHODS: We trained time-series models based on long short-term memory (LSTM) using individual bed data aggregated hourly each day to predict the BOR for each ward and room in the hospital. Ward training involved 2 models with 7- and 30-day time windows, and room training involved models with 3- and 7-day time windows for shorter-term planning. To further improve prediction performance, we added 2 models trained by concatenating dynamic data with static data representing room-specific details. RESULTS: We confirmed the results of a total of 12 models using bidirectional long short-term memory (Bi-LSTM) and LSTM, and the model based on Bi-LSTM showed better performance. The ward-level prediction model had a mean absolute error (MAE) of 0.067, mean square error (MSE) of 0.009, root mean square error (RMSE) of 0.094, and R2 score of 0.544. Among the room-level prediction models, the model that combined static data exhibited superior performance, with a MAE of 0.129, MSE of 0.050, RMSE of 0.227, and R2 score of 0.600. Model results can be displayed on an electronic dashboard for easy access via the web. CONCLUSIONS: We have proposed predictive BOR models for individual wards and rooms that demonstrate high performance. The results can be visualized through a web-based dashboard, aiding hospital administrators in bed operation planning. This contributes to resource optimization and the reduction of hospital resource use.

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Objective To analyze and evaluate the hospital bed efficiency and development trend of 54 tertiary public hospitals in Shandong Province from 2013 to 2020 and to provide a reference for improving efficiency in the use of hospital beds and rationally allocating medical and health resources.Methods Based on the panel data of 54 tertiary public hospitals in Shandong Province from 2013 to 2020,three indicators of bed utilization rate,bed turnover times and the average length of stay of patients were selected to describe the differences of bed use efficiency in each category by using variance analysis and to evaluate the bed use efficiency in tertiary public hospitals in Shandong Province.Results The results of the study show that the bed utilization rate of tertiary public hospitals in Shandong Province decreased from 2013 to 2020,but it is still higher than the national average level.The results of variance analysis showed that in 2019,the overall bed utilization rate of 54 third-level public hospitals reached 95.9%,which was overloaded.The number of bed turnover and the average day of hospitalization were improved,but there were significant differences in bed use efficiency among hospitals with different bed size and institutional attributes(P＜0.05).Conclusion Shandong Province should strengthen the rational allocation of medical resources,improve the level of hospital management,formulate relevant policies to promote the rational use of hospital bed resources,and further improve hospital bed efficiency.

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Background: In March 2020, the WHO declared COVID-19 as a pandemic, and Tunisia implemented a containment and targeted screening strategy. The country's public health policy has since focused on managing hospital beds. Methods: The study analyzed the bed occupancy rates in public hospitals in Tunisia during the pandemic. The evolution of daily cases and nonpharmaceutical interventions (NPI) actions undertaken by the Tunisian Government were also analyzed. The study used 3 indices to assess bed flexibility: Ramp duration until the peak, ramp growth until the peak, and ramp rate until the peak. The study also calculated the time shift at the start and peak of each wave to evaluate the government's response efficacy. Results: The study found that the evolution of the epidemic in Tunisia had 2 phases. The first phase saw the pandemic being controlled due to strong NPI actions, while the second phase saw a relaxation of measures and an increase in wave intensity. ICU bed availability followed the demand for beds, but ICU bed occupancy remained high, with a maximum of 97%. The government's response in terms of bed distribution and reallocation was slow. The study found that the most deadly wave by ICU occupied bed was the third wave due to a historical variant, while the fifth wave due to the delta variant was the most deadly in terms of cumulative death. Conclusions: The study concluded that decision-makers could use its findings to assess their response capabilities in the current pandemic and future ones. The study highlighted the importance of flexible and responsive healthcare systems in managing pandemics.

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Purpose: To study the relationship between three variables, namely, the number of supermarkets around, the average distance to medical institutions designated to be covered by basic medical insurance, and the presence of parks around, and the sustainable development of elderly care institutions. Patients and Methods: This paper is based on the census database of elderly care institutions in Beijing. We ran a self-developed Python program to mine the database after adding three variables, namely, the number of supermarkets around, the average distance to medical institutions designated to be covered by basic medical insurance (hereinafter referred to as "designated medical institutions"), and the presence of parks around, and then used the least squares method in regression analysis. Results: The number of years of depreciation for fixed assets had some degree of influence on the occupancy rate of an elderly care institution under sustained operation. The occupancy rate of above 80% was efficient for an elderly care institution. There was a progressive relationship between the external spatial factors and the internal resources of an elderly care institution in terms of influencing the occupancy rate of the institution. Optimizing the spatial distribution of elderly care institutions should first optimize the choice of location. Conclusion: The occupancy rate of an elderly care institution was closely related to the convenience of living conditions and that site selection was crucial for such a facility. We arrived at two recommendations for the future development of elderly care institutions, 1. Reducing the number of the institutions in areas with unfavorable geographic positions and serving senior care needs within an appropriate close distance, so as to better optimize the spatial distribution of senior care facilities, improve the efficiency of resource allocation. 2. Strike a balance between the economic benefits of operating entities and the social benefits associated with older people.

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Aim: Identify factors associated with COVID-19 intensive care unit (ICU) admission and death among hospitalized cases in Portugal, and variations from the first to the second wave in Portugal, March-December 2020. Introduction: Determinants of ICU admission and death for COVID-19 need further understanding and may change over time. We used hospital discharge data (ICD-10 diagnosis-related groups) to identify factors associated with COVID-19 outcomes in two epidemic periods with different hospital burdens to inform policy and practice. Methods: We conducted a retrospective cohort study including all hospitalized cases of laboratory-confirmed COVID-19 in the Portuguese NHS hospitals, discharged from March to December 2020. We calculated sex, age, comorbidities, attack rates by period, and calculated adjusted relative risks (aRR) for the outcomes of admission to ICU and death, using Poisson regressions. We tested effect modification between two distinct pandemic periods (March-September/October-December) with lower and higher hospital burden, in other determinants. Results: Of 18,105 COVID-19 hospitalized cases, 10.22% were admitted to the ICU and 20.28% died in hospital before discharge. Being aged 60-69 years (when compared with those aged 0-49) was the strongest independent risk factor for ICU admission (aRR 1.91, 95%CI 1.62-2.26). Unlike ICU admission, risk of death increased continuously with age and in the presence of specific comorbidities. Overall, the probability of ICU admission was reduced in the second period but the risk of death did not change. Risk factors for ICU admission and death differed by epidemic period. Testing interactions, in the period with high hospital burden, those aged 80-89, women, and those with specific comorbidities had a significantly lower aRR for ICU admission. Risk of death increased in the second period for those with dementia and diabetes. Discussion and conclusions: The probability of ICU admission was reduced in the second period. Different patient profiles were identified for ICU and deaths among COVID-19-hospitalized patients in different pandemic periods with lower and higher hospital burden, possibly implying changes in clinical practice, priority setting, or clinical presentation that should be further investigated and discussed considering impacts of higher burden on services in health outcomes, to inform preparedness, healthcare workforce planning, and pandemic prevention measures.

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Objetivo: A falta de leitos hospitalares no Brasil é queixa comum entre usuários do Sistema Único de Saúde. Objetivo: Relatar a experiência da construção de um Serviço de Gerenciamento de leitos e apresentar a atuação do enfermeiro como gestor, em prol da visibilidade e fortalecimento da classe de enfermagem. Método: Relato de experiência da implementação da gestão de leitos de um hospital público estadual de médio porte, em um município do interior do estado de São Paulo. Resultado: A partir da implantação houve mudanças no perfil dos indicadores dos setores assistencias, com a utilização dos leitos aproveitados em sua capacidade máxima. Observou-se a diminuição da fila de espera para internação em consequência do acesso oportuno e ordenado à vaga. Conclusão: Pode-se inferir que o gerenciamento de leitos é efetivo e eficiente na gestão hospitalar com resultados operacionais e financeiros satisfatórios e um fator preponderante para a segurança e satisfação dos clientes.(AU)

Objective: The lack of hospital beds in Brazil is a common complaint among users of the Unified Health System. Objective: To report the experience of the construction of a Bed Management Service and to present the nurse's role as manager, for the visibility and strengthening of the nursing class. Method: Experience report of the implementation of bed management in a public hospital of medium size, in a city in the interior of the state of São Paulo. Result: From the implementation there were changes in the profile of the indicators of the care sectors, with the use of beds used to their maximum capacity. A reduction in the waiting list for hospitalization was observed as a result of the timely and orderly access to vacancies. Conclusion: It can be inferred that the management of beds is effective and efficient in hospital management with satisfactory operational and financial results and a preponderant factor for the customers' safety and satisfaction.(AU)

Objetivo: La falta de camas hospitalarias en Brasil es una queja común entre los usuarios del Sistema Único de Salud. Objetivo: Relatar la experiencia de la construcción de un Servicio de Gestión de camas y presentar la actuación de la enfermera como gestora, para la visibilidad y fortalecimiento de la clase de enfermería. Método: Relato de experiência da implementação da gestão de lechos de um hospital público estadual de médio porte, em um município do interior do estado de São Paulo. Resultado: A partir da implementação houve mudanças no perfil dos indicadores dos setores assistência, com o uso de camas utilizadas ao seu máximo de capacidade. Observou-se a diminuição da fila de espera para internação em consequência do acesso oportuno e ordenado à vaga. Conclusão: É possível inferir que a gestão de camas é eficaz e eficiente na gestão hospitalar com resultados operacionais e financeiros satisfatórios e um factor preponderante para a segurança e satisfação dos clientes.(AU)

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OBJECTIVE: The aim: Retrospectively evaluate the effectiveness of the use of beds and human resources for the treatment of children with respiratory diseases in hospitals in the period 2008-2021. PATIENTS AND METHODS: Materials and methods: We calculated indicators that characterize the efficiency of the use of bed and personnel resources: the density of beds per 10,000, the rate of hospitalized children per 10,000 (RH per 10,000), the bed occupancy rate per year (BOR), average length of stay (ALOS), full-time positions (FTP) per 100,000, number of beds per 1 FTP of doctors. RESULTS: Results: During 2008-2021, there was a significant decrease in the density of all types of beds. The percentage of hospitalized children for inpatient treatment decreased, BOR decreased, and ALOS decreased. The density of full-time positions of allergists increased by +23.78%, pediatricians by +4.86%, pulmonologists decreased by -13.15%. In 2021, there were 10.31 beds for 1 FTP of an allergist, 12.8 beds for 1 FTP of a pulmonologist, and 5.83 beds for 1 FTP of a pediatrician. According to the correlation matrix, it was established that the more beds there are for 1 full-time position of a pediatrician and 1 full-time position of an allergist, the longer the ALOS and the bed occupancy rate are. CONCLUSION: Conclusions: When planning staffing of health care institutions, it is necessary to mind the level of urbanization of the region, and ensure status of the general practitioner as a leading medical specialist responsible for medical care during the first meeting with the patient and his subsequent follow-up.

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Objectives: We observed a decrease in the number of patients who were offered reperfusion therapy. We aimed to investigate if whether hospital system pressure measured as the percentage of stroke bed occupancy influenced decisions on treatment and disposition. Design: Data from a regional quality of stroke care database were obtained and linked to the organizational data monitoring of the hourly inpatient stroke bed occupancy rate. Logistic regression was used to analyze the relationship. Results: A total of 15,025 admissions were included from 1 January 2019 to 24 August 2022. Of these, 5,659 (38%) had an acute ischemic stroke. The rates of reperfusion therapy treatment were the highest in 2019 (36.2%) and 2020 (34.1%) and declined afterward (30.0% in 2021). In the logistic regression analysis, an occupancy rate of ≥85% in the hour of the first admission was associated with reduced odds of admission at the stroke unit within 3 h from the symptom onset [adjusted odds ratio: 0.80, 95% confidence interval: (0.71-0.90), p < 0.001] and a reduced odds of receiving reperfusion therapy (adjusted odds ratio: 0.83 (0.73-0.95), p = 0.007). Conclusion: An increased bed occupancy level in the hour of the first hospital admission for stroke patients was strongly associated with decreased odds of receiving reperfusion therapy.

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Co-circulation of influenza and SARS-CoV-2 has the potential to place considerable strain on health-care services. We estimate the cost-effectiveness and health-care resource utilization impacts of influenza vaccination of low-risk 50-64-y-olds in the United Kingdom (UK) against a background SARS-CoV-2 circulation. A dynamic susceptible-exposed-infected-recovered model was used to simulate influenza transmission, with varying rates of vaccine coverage in the low-risk 50-64 y age-group. Four scenarios were evaluated: no vaccination (baseline), 40%, 50%, and 60% coverage. For the 50% and 60% coverage, this rate was also applied to high-risk 50-64-y-olds, whereas 48.6% was used for the baseline and 40% coverage scenarios. Cost-effectiveness was estimated in terms of humanistic outcomes and incremental cost-effectiveness ratio (ICER), with discounting applied at 3%. Overall, influenza vaccination of 50-64-y-olds resulted in reductions in GP visits, hospitalizations, and deaths, with a reduction in influenza-related mortality of 34%, 41%, and 52% for 40%, 50%, and 60% coverage, respectively. All four scenarios resulted in acute and intensive care unit (ICU) bed occupancy levels above available capacity, although vaccination of low-risk 50-64-y-olds resulted in a 35-54% and 16-25% decrease in excess acute and ICU bed requirements, respectively. Vaccination of this group against influenza was highly cost-effective from the payer perspective, with ICERs of £2,200-£2,343/quality-adjusted life year across the coverage rates evaluated. In conclusion, in the UK, vaccination of low-risk 50-64-y-olds against influenza is cost-effective and can aid in alleviating bed shortages in a situation where influenza and SARS-CoV-2 are co-circulating.

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Introducción: La hospitalización a domicilio para pacientes quirúrgicos (HaDQ) es una al-ternativa a la hospitalización convencional para pacientes quirúrgicos estables clínicamente, que precisen procedimientos de enfermería complejos por intensidad, frecuencia o carac-terísticas, y control por especialista quirúrgico en el domicilio.Método: Estudio transversal, descriptivo y retrospectivo de la actividad de la HADQ de nuestro hospital durante los primeros seis me-ses del 2020, para analizar la repercusión de la pandemia por SARS-CoV-2 en la unidad. Se distinguen tres periodos: prepandemia (enero-febreo), confinamiento (marzo-abril), poscon-finamiento (mayo-junio). Se diferencian dos grupos: A (HaD convencional) y B (despistaje preoperatorio COVID19). Se recogieron diver-sas variables: mes, tipo, estancia (HaD y hospi-tal), procedimientos, reingresos, domicilio, tipo visitas, COVID+. Se realizó un análisis estadís-tico descriptivo cuantitativo y cualitativo de los resultados obtenidosResultados: Ingresaron 345 pacientes, 225 en el grupo A (fase Pre (34%), fase C (40%), y fase Pos (25%)), y 120 en el B (fase C (75%), fase Pos (25%)). El confinamiento (fase C) fue el pe-ríodo más activo de la HADQ, tanto por número de ingresos (53%), como por la complejidad del grupo A que requería más procedimientos (71%) y más visitas domiciliarias (52%). Tam-bién aumentaron los pacientes de zona de no cobertura (42%), que implicaron visitas médicas y de enfermería en Hospital de Día (HD) (21%), y aumento de consultas telefónicas médicas (36%). En la fase Pos disminuyeron un 37% los ingresos del grupo A.Conclusiones: La HaDQ se reorganizó por la pandemia para atender a más pacientes quirúr-gicos, siendo un recurso asistencial esencial, especialmente durante el confinamiento. (AU)

Introduction: The HaDQ is an alternative to conventional hospitalization for clinically stable surgical patients who require complex nursing procedures due to intensity, frequency or char-acteristics, and control by a surgical specialist at home.Method: Cross-sectional, descriptive and ret-rospective study of the HADQ activity of our hospital during the first six months of 2020, to analyze the impact of the SARSCov2 pandemic in the unit. Three periods are distinguished: pre-pandemic (Jan-Feb), lockdown (Mar-Apr), post-lockdown (May-Jun). Two groups are differen-tiated: A (conventional HaD) and B (COVID19 preoperative screening). Various variables were collected: month, type, stay (HaD and hospi-tal), procedures, readmissions, address, type of visits, covid+. A quantitative and qualitative descriptive statistical analysis of the results ob-tained was carried out.Results: 345 patients were admitted, 225 in group A (phase Pre (34%), Phase C (40%), and phase Post (25%)), and 120 in group B (Phase C (75%), phase Post (25%)). %)). The confinement (phase C) was the most active period of the HADQ, both due to the number of admissions (53%), and the complexity of group A, which re-quired more procedures (71%) and more home visits (52%). There was also an increase in pa-tients from the non-coverage area (42%), which involved medical and nursing visits at the Day Hospital (HD) (21%), and an increase in medi-cal telephone consultations (36%). In the phase Post, the income of group A decreased by 37%.Conclusions: The HaDQ was reorganized due to the pandemic to care for more surgical pa-tients, being an essential care resource, espe-cially during confinement. (AU)

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Wastewater-based epidemiology is widely applied in Austria since April 2020 to monitor the SARS-CoV-2 pandemic. With a steadily increasing number of monitored wastewater facilities, 123 plants covering roughly 70 % of the 9 million population were monitored as of August 2022. In this study, the SARS-CoV-2 viral concentrations in raw sewage were analysed to infer short-term hospitalisation occupancy. The temporal lead of wastewater-based epidemiological time series over hospitalisation occupancy levels facilitates the construction of forecast models. Data pre-processing techniques are presented, including the approach of comparing multiple decentralised wastewater signals with aggregated and centralised clinical data. Time­lead quantification was performed using cross-correlation analysis and coefficient of determination optimisation approaches. Multivariate regression models were successfully applied to infer hospitalisation bed occupancy. The results show a predictive potential of viral loads in sewage towards Covid-19 hospitalisation occupancy, with an average lead time towards ICU and non-ICU bed occupancy between 14.8-17.7 days and 8.6-11.6 days, respectively. The presented procedure provides access to the trend and tipping point behaviour of pandemic dynamics and allows the prediction of short-term demand for public health services. The results showed an increase in forecast accuracy with an increase in the number of monitored wastewater treatment plants. Trained models are sensitive to changing variant types and require recalibration of model parameters, likely caused by immunity by vaccination and/or infection. The utilised approach displays a practical and rapidly implementable application of wastewater-based epidemiology to infer hospitalisation occupancy.

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We study whether hospitals that exhibit systematically higher bed occupancy rates are associated with lower quality in England over 2010/11-2017/18. We develop an economic conceptual framework to guide our empirical analysis and run regressions to inform possible policy interventions. First, we run a pooled OLS regression to test if high bed occupancy is associated with, and therefore acts as a signal of, lower quality, which could trigger additional regulation. Second, we test whether this association is explained by exogenous demand-supply factors such as potential demand, and unavoidable costs. Third, we include determinants of bed occupancy (beds, length of stay, and volume) that might be associated with quality directly, rather than indirectly through bed occupancy. Last, we use a within-between random-effects specification to decompose these associations into those due to variations in characteristics between hospitals and variations within hospitals. We find that bed occupancy rates are positively associated with overall and surgical mortality, negatively associated with patient-reported health gains, but not associated with other indicators. These results are robust to controlling for demand-supply shifters, beds, and volume. The associations reduce by 12%-25% after controlling for length of stay in most cases and are explained by variations in bed occupancy between hospitals.

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We describe the models we built for predicting hospital admissions and bed occupancy of COVID-19 patients in the Netherlands. These models were used to make short-term decisions about transfers of patients between regions and for long-term policy making. For forecasting admissions we developed a new technique using linear programming. To predict occupancy we fitted residual lengths of stay and used results from queueing theory. Our models increased the accuracy of and trust in the predictions and helped manage the pandemic, minimizing the impact in terms of beds and maximizing remaining capacity for other types of care.

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BACKGROUND: Time-series forecasting models play a central role in guiding intensive care coronavirus disease 2019 (COVID-19) bed capacity in a pandemic. A key predictor of future intensive care unit (ICU) COVID-19 bed occupancy is the number of new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in the general population, which in turn is highly associated with week-to-week variability, reporting delays, regional differences, number of unknown cases, time-dependent infection rates, vaccinations, SARS-CoV­2 virus variants, and nonpharmaceutical containment measures. Furthermore, current and also future COVID ICU occupancy is significantly influenced by ICU discharge and mortality rates. METHODS: Both the number of new SARS-CoV­2 infections in the general population and intensive care COVID-19 bed occupancy rates are recorded in Germany. These data are statistically analyzed on a daily basis using epidemic SEIR (susceptible, exposed, infection, recovered) models using ordinary differential equations and multiple regression models. RESULTS: Forecast results of the immediate trend (20-day forecast) of ICU occupancy by COVID-19 patients are made available to decision makers at various levels throughout the country. CONCLUSION: The forecasts are compared with the development of available ICU bed capacities in order to identify capacity limitations at an early stage and to enable short-term solutions to be made, such as supraregional transfers.

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This paper introduces mathematical models that support dynamic fair balancing of COVID-19 patients over hospitals in a region and across regions. Patient flow is captured in an infinite server queueing network. The dynamic fair balancing model within a region is a load balancing model incorporating a forecast of the bed occupancy, while across regions, it is a stochastic program taking into account scenarios of the future bed surpluses or shortages. Our dynamic fair balancing models yield decision rules for patient allocation to hospitals within the region and reallocation across regions based on safety levels and forecast bed surplus or bed shortage for each hospital or region. Input for the model is an accurate real-time forecast of the number of COVID-19 patients hospitalised in the ward and the Intensive Care Unit (ICU) of the hospitals based on the predicted inflow of patients, their Length of Stay and patient transfer probabilities among ward and ICU. The required data is obtained from the hospitals' data warehouses and regional infection data as recorded in the Netherlands. The algorithm is evaluated in Dutch regions for allocation of COVID-19 patients to hospitals within the region and reallocation across regions using data from the second COVID-19 peak.

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To describe time trends and the burden of long-stay patients (LSP) and frequent-readmission patients (FRP) in the PICUs in The Netherlands. DESIGN: Retrospective analysis of data from the nationwide Pediatric Intensive Care Evaluation registry including all PICU admissions in The Netherlands. SETTING: All PICUs in The Netherlands. PATIENTS: All PICU patients less than 18 years old admitted between 2003 and 2017. Two groups of patients were identified: LSP (admitted ≥30 d) and FRP (≥3 readmissions within the first year after their first admission). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 47,424 children were admitted on 59,759 occasions. LSP (3.3% of total cohort) and FRP (2.1%) accounted for 37.5% and 14.5% of cumulative admission days, respectively. Patients fulfilling both LSP and FRP criteria (0.6%) accounted for 9.9% of cumulative admission days. No significant time trends were observed between 2003 and 2017 for the number of LSP and FRP, nor for accounted cumulative admission days. Age and disease severity-adjusted mortality was significantly higher for LSP (odds ratio [OR], 2.16; 95% CI, 1.66-2.82; p < 0.001) and FRP OR 1.40 (95% CI, 0.97-2.01; p = 0.069) compared with the general PICU population. Overall PICU mortality decreased significantly between 2003 (6.5%) and 2017 (3.7%; p = 0.004), but remained constant over time for both LSP (17.2%) and FRP (6.3%). CONCLUSIONS: The proportion of LSP and FRP and their burden on PICU capacity are considerable and remained constant between 2003 and 2017. Whereas age and disease severity-adjusted mortality decreased in the general PICU population, it did not change in LSP and FRP.

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BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has disrupted critical care services worldwide. Examining how critical care systems responded to the COVID-19 pandemic on a national level will be useful in setting future critical care plans. The present study aimed to describe the utilization of critical care services before and during the COVID-19 pandemic using a nationwide Japanese inpatient administrative database. METHODS: All patients admitted to an intensive care unit (ICU) or a high-dependency care unit (HDU) from February 9, 2019, to February 8, 2021, in the Japanese Diagnosis Procedure Combination inpatient database were included. February 9, 2020, was used as the breakpoint separating the periods before and during COVID-19 pandemic. Hospital and patient characteristics were compared before and during the COVID-19 pandemic. Change in ICU and HDU bed occupancy before and during the COVID-19 pandemic was evaluated using interrupted time-series analysis. RESULTS: The number of ICU patients before and during the COVID-19 pandemic was 297,679 and 277,799, respectively, and the number of HDU patients was 408,005 and 384,647, respectively. In the participating hospitals (383 ICU-equipped hospitals and 460 HDU-equipped hospitals), the number of hospitals which increased the ICU and HDU beds capacity were 14 (3.7%) and 33 (7.2%), respectively. Patient characteristics and outcomes in ICU and HDU were similar before and during the COVID-19 pandemic except main etiology for admission of COVID-19. The mean ICU bed occupancy before and during the COVID-19 pandemic was 51.5% and 47.5%, respectively. The interrupted time-series analysis showed a downward level change in ICU bed occupancy during the COVID-19 pandemic (- 4.29%, 95% confidence intervals - 5.69 to - 2.88%), and HDU bed occupancy showed similar trends. Of 383 hospitals with ICUs, 232 (60.6%) treated COVID-19 patients in their ICUs. Their annual hospital case volume of COVID-19 ICU patients varied greatly, with a median of 10 (interquartile range 3-25, min 1, max 444). CONCLUSIONS: The ICU and HDU bed capacity did not increase while their bed occupancy decreased during the COVID-19 pandemic in Japan. There was no change in clinicians' decision-making to forego ICU/HDU care for selected patients, and there was no progress in the centralization of critically ill COVID-19 patients.