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.

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.

A Model to Compare International Hospital Bed Numbers, including a Case Study on the Role of Indigenous People on Acute 'Occupied' Bed Demand in Australian States.

Comparing international or regional hospital bed numbers is not an easy matter, and a pragmatic method has been proposed that plots the number of beds per 1000 deaths versus the log of deaths per 1000 population. This method relies on the fact that 55% of a person's lifetime hospital bed utilization occurs in the last year of life-irrespective of the age at death. This is called the nearness to death effect. The slope and intercept of the logarithmic relationship between the two are highly correlated. This study demonstrates how lines of equivalent bed provision can be constructed based on the value of the intercept. Sweden looks to be the most bed-efficient country due to long-term investment in integrated care. The potential limitations of the method are illustrated using data from English Clinical Commissioning Groups. The main limitation is that maternity, paediatric, and mental health care do not conform to the nearness to death effect, and hence, the method mainly applies to adult acute care, especially medical and critical care bed numbers. It is also suggested that sensible comparison can only be made by comparing levels of occupied beds rather than available beds. Occupied beds measure the expressed bed demand (although often constrained by access to care issues), while available beds measure supply. The issue of bed supply is made complex by the role of hospital size on the average occupancy margin. Smaller hospitals are forced to operate at a lower average occupancy; hence, countries with many smaller hospitals such as Germany and the USA appear to have very high numbers of available beds. The so-called 85% occupancy rule is an "urban myth" and has no fundamental basis whatsoever. The very high number of "hospital" beds in Japan is simply an artefact arising from "nursing home" beds being counted as a "hospital" bed in this country. Finally, the new method is applied to the expressed demand for occupied acute beds in Australian states. Using data specific to acute care, i.e., excluding mental health and maternity, a long-standing deficit of beds was identified in Tasmania, while an unusually high level of occupied beds in the Northern Territory (NT) was revealed. The high level of demand for beds in the NT appears due to an exceptionally large population of indigenous people in this state, who are recognized to have elevated health care needs relative to non-indigenous Australians. In this respect, indigenous Australians use 3.5 times more occupied bed days per 1000 deaths (1509 versus 429 beds per 1000 deaths) and 6 times more occupied bed days per 1000 population (90 versus 15 beds per 1000 population) than their non-indigenous counterparts. The figure of 1509 beds per 1000 deaths (or 4.13 occupied beds per 1000 deaths) for indigenous Australians is indicative of a high level of "acute" nursing care in the last months of life, probably because nursing home care is not readily available due to remoteness. A lack of acute beds in the NT then results in an extremely high average bed occupancy rate with contingent efficiency and delayed access implications.

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.

["Follow up" strategy in the management of hospital stay]. / Estrategia de Follow up en la gestión de la estancia hospitalaria.

Background: In countries with emerging economies, the adequate and efficient management of resources is a priority, through strategies to reduce prolonged stay, increase the availability of beds, maximize profitability and reduce iatrogenic complications. Objective: The purpose of the study was to evaluate the effect of the "Follow up" strategy (FU) on the main indicators of the hospitalization process. Material and methods: A cross-sectional, comparative study was developed to evaluate the impact of the FU strategy on the indicators: hospital admissions and discharges, average days of hospital stay (DEH), percentage of hospital occupancy (OH), bed substitution interval (ISC), bed turnover rate (CRI) and prolonged hospital stay (EHP). Results: The FU was associated with a reduction in DEH [5.7 (5.5-6.1) vs. 6.5 days (6.1-6.9), p = 0.01]; ISC [0.6 (0.4-0.8) vs. 1.2 (0.8-1.3), p = 0.01] and EHP [23.6 (21.6-24.7) vs. 26.3% (24.4-28.7), p = 0.02] compared to the control group, with an increase in existence [1436 (1381-1472) vs. 1347 patient days (1280-1402), p = 0.02], respectively. There was no significant difference in the number of admissions, discharges or in the IRC. Conclusions: The FU reduces the average number of days of hospital stay, the rate of bed substitution and prolonged stay.

Introducción: en los países con economías emergentes es prioritaria la gestión adecuada y eficiente de los recursos hospitalarios. Las estrategias de gestión pueden reducir la estancia prolongada, aumentar la disponibilidad de camas, maximizar la rentabilidad y reducir las complicaciones iatrogénicas. Objetivo: el propósito del estudio fue evaluar el efecto de la estrategia de Follow up (FU) en los principales indicadores del proceso de hospitalización. Material y métodos: se desarrolló un estudio transversal, comparativo, para evaluar el impacto de la estrategia de FU en los indicadores: ingresos y egresos hospitalarios, promedio de días de estancia hospitalaria (DEH), porcentaje de ocupación hospitalaria (OH), intervalo de sustitución de camas (ISC), índice de rotación de camas (IRC) y estancia hospitalaria prolongada (EHP). Resultados: la estrategia de FU se asoció con una reducción de los DEH [5.7 (5.5-6.1) frente a 6.5 días (6.1-6.9), p = 0.01]; ISC [0.6 (0.4-0.8) frente a 1.2 (0.8-1.3), p = 0.01] y EHP [23.6 (21.6-24.7) frente a 26.3% (24.4-28.7), p = 0.02] respecto al grupo control, con incremento de la existencia [1436 (1381-1472) frente a 1347 días paciente (1280-1402), p = 0.02], respectivamente. No hubo diferencia significativa en el número de ingresos, egresos ni en el IRC. Conclusiones: la estrategia de FU disminuyó el promedio de días de estancia hospitalaria, el índice de sustitución de camas y la estancia prolongada.

Do subsidized nursing homes and home care teams reduce hospital bed-blocking? Evidence from Portugal.

Excessive length of hospital stay is among the leading sources of inefficiency in healthcare. When a patient is clinically fit to be discharged but requires support outside the hospital, which is not readily available, they remain hospitalized until a safe discharge is possible -a phenomenon called bed-blocking. I study whether the availability of subsidized nursing homes and home care teams reduces hospital bed-blocking. Using individual data on the universe of inpatient admissions at Portuguese hospitals during 2000-2015, I find that the entry of home care teams in a region reduces bed-blocking by 4 days per episode, on average. Nursing home entry only reduces bed-blocking among patients with high care needs or when the intensity of entry is high. Reductions in bed-blocking do not harm patients' health. The beds freed up by reducing bed-blocking are used to admit additional elective patients.

Impact of the Automation of Inpatient Bed Management to Reduce the Emergency Service Waiting Time.

The patient waiting time to be transferred for hospitalization is the time that the patient waits between the decision to hospitalize and the actual admission to an inpatient hospital bed. One of the difficulties encountered in qualifying waiting time for inpatient bed is the inability of hospital information systems to measure it. Hospitals in France have a specialized bed allocation team. This team must manage the bed allocation problem between different hospital departments using phone communication to assign patients to the adapted service. This kind of communication represents a lengthy additional workload in which effectiveness is uncertain. This paper presents a new approach to automate bed management in downstream service. For that, we have implemented algorithms based on artificial intelligent integrated in an inpatient web platform using IoT-Beacons, which is implemented to improve and facilitate the exchange of availability information of downstream beds within the Lille university hospital center (LUHC).

Optimization of the Use of Hospital Beds as an Example of Improving the Functioning of Hospitals in Poland on the Basis of the Provincial Clinical Hospital No. 1 in Rzeszow.

An efficient health care system combines maximum accessibility with high-quality treatments, as well as cost optimization of individual health care facilities throughout the entire system. In hospitals, the critical element is the number of beds within individual wards, which generates costs and, at the same time, affects the capacity to serve patients. The aim of this article is to discuss the restructuring and optimization of hospital bed occupancy in a healthcare facility in the Podkarpackie voivodeship. The analysis covers the years 1999-2018. In the indicated period, the analyzed healthcare institution restructured the number of beds based on a forecast of the demand for services, which resulted in positive cost effects, without limiting patients' access to diagnostic and therapeutic care. The analyzed facility took part in a common trend of optimizing cost-effectiveness and efficiency of hospital operations in Poland.

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.

Can we accurately forecast non-elective bed occupancy and admissions in the NHS? A time-series MSARIMA analysis of longitudinal data from an NHS Trust.

OBJECTIVES: The main objective of the study was to develop more accurate and precise short-term forecasting models for admissions and bed occupancy for an NHS Trust located in Bristol, England. Subforecasts for the medical and surgical specialties, and for different lengths of stay were realised DESIGN: Autoregressive integrated moving average models were specified on a training dataset of daily count data, then tested on a 6-week forecast horizon. Explanatory variables were included in the models: day of the week, holiday days, lagged temperature and precipitation. SETTING: A secondary care hospital in an NHS Trust in South West England. PARTICIPANTS: Hospital admissions between September 2016 and March 2020, comprising 1291 days. PRIMARY AND SECONDARY OUTCOME MEASURES: The accuracy of the forecasts was assessed through standard measures, as well as compared with the actual data using accuracy thresholds of 10% and 20% of the mean number of admissions or occupied beds. RESULTS: The overall Autoregressive Integrated Moving Average (ARIMA) admissions forecast was compared with the Trust's forecast, and found to be more accurate, namely, being closer to the actual value 95.6% of the time. Furthermore, it was more precise than the Trust's. The subforecasts, as well as those for bed occupancy, tended to be less accurate compared with the overall forecasts. All of the explanatory variables improved the forecasts. CONCLUSIONS: ARIMA models can forecast non-elective admissions in an NHS Trust accurately on a 6-week horizon, which is an improvement on the current predictive modelling in the Trust. These models can be readily applied to other contexts, improving patient flow.

Internal Regulation Center in hospitals: Repercussions of its implementation on the health services' indicators. / Núcleo Interno de Regulação hospitalar: repercussões da implantação nos indicadores dos serviços de saúde.

OBJECTIVE: To evaluate the hospital indicators and their repercussions on the number of monthly admissions to a public university hospital, before and after implementing the Internal Regulation Center. METHOD: An evaluative research study, of the Case Study type, developed in a public university hospital. A total of 28 indicators related to structure, production, productivity and quality were measured, which are part of internal Benchmarking. The data were analyzed by means of descriptive statistics and multiple regression to identify the independent factors and those associated with the number of monthly hospitalizations with 95% confidence intervals. RESULTS: Implementation of the Center significantly increased (p<0.001) the number of discharges, the bed utilization factor and the bed renewal rate, emergency hospitalization, bed occupancy percentage, surgical procedures performed and the patient-day mean value (p=0.027). There was a reduction (p<0.001) in the number of visits to the medical, obstetric and orthopedic emergency room, in the rates of in-hospital infection and infant mortality, as well as a mean reduction of 0.81/day, approximately one day less of hospitalization per patient, or a gain of 40 available beds per month. CONCLUSION: Although the number of available beds was lower in the post-implementation period, the bed replacement interval was reduced, representing an increase of 40 more beds per month due to the reduction in the patients' length of stay in the institution.

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.

Multimodal Quality Improvement Intervention With Dedicated Patient Flow Manager to Reduce Emergency Department Length of Stay and Occupancy: Interrupted Time Series Analysis.

INTRODUCTION: Crowding in the emergency department is a problem worldwide that can affect patient safety and clinical outcomes. The aim of this project was to evaluate a multimodal quality improvement intervention with a new patient flow manager to reduce ED length of stay and ED bed occupancy. METHODS: This single-site interrupted time-series analysis study was conducted in a tertiary hospital emergency department in South Korea. Interventions for a novel system load-balancing approach included a data-driven patient flow tracking informatics system, adding medical specialists, point-of-care creatinine testing (when required before diagnostic imaging) with dedicated imaging test slots for emergency patients, and introducing patient flow managers. Records of adult patients visiting the emergency department from January 2016 to March 2020 were included. Outcomes were ED length of stay and ED bed occupancy. Regression discontinuity analysis of an interrupted time series was used adjusting for seasonality and the number of patients per staff. RESULTS: A total of 46,494 patients in the preintervention period and 151,802 patients in the postintervention period were included. After the intervention, ED length of stay decreased by 4.07 hours, whereas the slope indicated a return to preintervention levels over time. Monthly average ED bed occupancy decreased by 34.6%, and the slope remained consistent over time. DISCUSSION: The multimodal quality improvement intervention that included a patient flow manager was an effective intervention to reduce the ED length of stay and the ED bed occupancy at the study site. The change for length of stay may not sustain over time without further intervention.