Explore the vast range of titles available.

Although the number of intensive care unit (ICU) beds in the United States is increasing, it is unknown whether this trend is consistent across all regions. We sought to better characterize regional variation in ICU bed changes over time and identify regional characteristics associated with these changes. We used data from the Centers for Medicare and Medicaid Services and the U.S. Census to summarize the numbers of hospitals, hospital beds, ICU beds, and ICU occupancy at the level of Dartmouth Atlas hospital referral region from 2000 to 2009. We categorized regions into quartiles of bed change over the study interval and examined the relationship between change categories, regional characteristics, and population characteristics over time. From 2000 to 2009 the national number of ICU beds increased 15%, from 67,579 to 77,809, mirroring population. However, there was substantial regional variation in absolute changes (median, +16 ICU beds; interquartile range, -3 to +51) and population-adjusted changes (median, +0.9 ICU beds per 100,000; interquartile range, -3.8 to +5.9), with 25.0% of regions accounting for 74.8% of overall growth. At baseline, regions with increasing numbers of ICU beds had larger populations, lower ICU beds per 100,000 capita, higher average ICU occupancy, and greater market competition as measured by the Herfindahl-Hirschman Index (P < 0.001 for all comparisons). National trends in ICU bed growth are not uniformly reflected at the regional level, with most growth occurring in a small number of highly populated regions.

Machine learning models are vital for forecasting and optimizing healthcare parameters, especially in the context of rising mental health issues in India and globally. With increasing demand for mental health services, effective resource management, like bed occupancy forecasting, is crucial to ensure proper patient care and reduce the burden on healthcare facilities. This study applies six machine learning models, namely Support Vector Regression, eXtreme Gradient Boosting, Random Forest, K-Nearest Neighbors, Gradient Boosting, and Decision Tree, to forecast weekly bed occupancy of the second largest mental hospital in India, using data from 2008 to 2024. Accuracy of models were evaluated using Mean Absolute Percentage Error, and Diebold–Mariano test for assessing differences in predictive performance. Further, we forecast the bed occupancy, providing crucial insights for healthcare administrators in capacity planning and resource allocation, supporting data-driven decisions and enhancing the quality of mental health services in India.

To examine the reduction in NHS inpatient hospital beds in England from 1960 until 2020, including five categories: Acute, Geriatric, Maternity, Mental Illness and Learning Disability beds; and to measure regional differences at the end of the study period. Retrospective observational study. NHS in England. Inpatient hospital beds. NHS inpatient bed provision per 100,000 population. Rate of reduction calculated as percentage change each year. NHS bed provision in 7 regions of England compared for the year 2019/20. NHS inpatient bed provision declined for sixty consecutive years. The overall reduction was 78.0% between 1960 and 2020. Greatest reduction was in Learning Disability beds (98.7%), followed by Mental Illness (90.6%), Geriatric (75.0%), Maternity (67.4%) and the least reduction in Acute beds (63.0%). There were two periods of accelerating rates of bed reduction, the first in the 1980s, and the second in the 2000s. At the end of the study period there was significant regional variation in bed numbers. Bed reductions were a constant feature, with important differences between bed categories and across time. This needs to be addressed when planning for future pandemics and winter bed pressures. By the end of the study the NHS was no longer providing the same level of inpatient care in different regions of England, particularly for Learning Disability.

Background Bed capacity management is a critical issue facing hospital administrators, and inefficient discharges impact patient flow throughout the hospital. National recommendations include a focus on providing care that is timely and efficient, but a lack of standardised discharge criteria at our institution contributed to unpredictable discharge timing and lengthy delays. Our objective was to increase the percentage of Hospital Medicine patients discharged within 2 h of meeting criteria from 42% to 80%. Methods A multidisciplinary team collaborated to develop medically appropriate discharge criteria for 11 common inpatient diagnoses. Discharge criteria were embedded into electronic medical record (EMR) order sets at admission and could be modified throughout a patient's stay. Nurses placed an EMR time-stamp to signal when patients met all discharge goals. Strategies to improve discharge timeliness emphasised completion of discharge tasks prior to meeting criteria. Interventions focused on buy-in from key team members, pharmacy process redesign, subspecialty consult timeliness and feedback to frontline staff. A P statistical process control chart assessed the impact of interventions over time. Length of stay (LOS) and readmission rates before and after implementation of process measures were compared using the Wilcoxon rank-sum test. Results The percentage of patients discharged within 2 h significantly improved from 42% to 80% within 18 months. Patients studied had a decrease in median overall LOS (from 1.56 to 1.44 days; p=0.01), without an increase in readmission rates (4.60% to 4.21%; p=0.24). The 12-month rolling average census for the study units increased from 36.4 to 42.9, representing an 18% increase in occupancy. Conclusions Through standardising discharge goals and implementation of high-reliability interventions, we reduced LOS without increasing readmission rates.

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.

This study investigates the process of planning for future inpatient resources (beds, staff and costs) for maternity (pregnancy and childbirth) services. The process of planning is approached from a patient-centered philosophy; hence, how do we discharge a suitably rested healthy mother who is fully capable of caring for the newborn baby back into the community? This demonstrates some of the difficulties in predicting future births and investigates trends in the average length of stay. While it is relatively easy to document longer-term (past) trends in births and the conditions relating to pregnancy and birth, it is exceedingly difficult to predict the future nature of such trends. The issue of optimum average bed occupancy is addressed via the Erlang B equation which links number of beds, average bed occupancy and turn-away. Turn-away is the proportion of times that there is not an immediately available bed for the next arriving inpatient. Data for maternity units show extreme and unexplained variation in turn-away. Economy of scale implied by queuing theory (and the implied role of population density) explains why many well intended community-based schemes fail to gain traction. The paper also addresses some of the erroneous ideas around the dogma that reducing length of stay ‘saves’ money. Maternity departments are encouraged to understand how their costs are calculated to avoid the trap where it is suggested by others that in reducing the length of stay, they will reduce costs and increase ‘efficiency’. Indeed, up to 60% of calculated maternity ‘costs’ are apportioned from (shared) hospital overheads from supporting departments such as finance, personnel, buildings and grounds, IT, information, etc., along with depreciation charges on the hospital-wide buildings and equipment. These costs, known as ‘the fixed costs dilemma’, are totally beyond the control of the maternity department and will vary by hospital depending on how these costs are apportioned to the maternity unit. Premature discharge, one of the unfortunate outcomes of turn-away, is demonstrated to shift maternity costs into the pediatric and neonatal departments as ‘boomerang babies’, and then require the cost of avoidable inpatient care. Examples are given from the English NHS of how misdirected government policy can create unforeseen problems.

Mental health disorders affect over 15% of the global working-age population, contributing to an annual economic loss of approximately USD 1 trillion due to diminished productivity and increased healthcare expenditures. In India, the post-pandemic surge in hospitalizations has placed additional strain on mental health infrastructure, exacerbating an already significant treatment gap. Overcrowding and inadequate forecasting mechanisms have resulted in occupancy rates that exceed hospital capacity, underscoring the urgent need for predictive tools to support admission planning and resource allocation. This study introduces a novel forecasting framework that applies Bayesian Model Averaging (BMA) with Zellner’s g-prior used here for the first time alongside deep learning models for predicting weekly bed occupancy at India’s second-largest mental health hospital. Time series data from 2008 to 2024 were used to train six models: Time Delay Neural Networks (TDNN), Recurrent Neural Networks (RNN), Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Bidirectional GRU (BiGRU). Model performance was optimized using random search (RS) and grid search (GS) hyperparameter tuning, allowing the framework to account for model uncertainty while improving predictive accuracy and consistency. Among all models, BiLSTM with GS tuning and BMA-GS model showed the best forecasting performance for bed-occupancy, achieving 98.06% accuracy (MAPE: 1.939%) and effectively capturing weekly fluctuations within ±13 beds. In contrast, RS-tuned models yielded higher errors (MAPE: 2.331%). Moreover, the average credible interval width decreased from 16.34 under BMA-RS to 13.28 with BMA-GS, indicating improved forecast precision and reliability. This study demonstrates that embedding Bayesian statistics specifically BMA with Zellner’s g-prior into deep learning architectures offers a robust and scalable solution for forecasting hospital bed occupancy. The proposed framework enhances predictive accuracy and reliability, supporting data-driven planning for hospital administrators and policymakers. It aligns with the objectives of India’s National Mental Health Programme (NMHP) and Sustainable Development Goal 3, advancing equitable and efficient access to mental healthcare.

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.

Summary A growing elderly population is expected worldwide, and the burden of hip fractures on health care system will continue to increase. By 2035, there will be a 2.7-fold increase in the number of hip fractures in Taiwan. The study provides quantitative basis for the future distribution of medical resources. Introduction Hip fractures have long been recognized as a major public health concern. The study aimed to determine time trends in the incidence of hip fractures and to forecast the number of hip fractures expected in Taiwan up to 2035. Methods A nationwide survey was conducted using data from the Taiwan National Health Insurance Research Database from 2004 to 2011. A total of 141,397 hip fractures were identified, with a mean of 17,675 fractures/year. Annual incidences of hip fractures were calculated and tested for trends. Projections of the incidence rates of hip fractures and bed days associated with hip fractures were calculated using Poisson regression on the historical incidence rates in combination with population projections from 2012 to 2035. Results The incidence rates of hip fracture during 2004–2011 were 317 and 211 per 100,000 person-years among women and men, respectively. Over this 8-year period, the age-standardized incidence of hip fracture decreased by 13.4 % among women and 12.2 % among men. Despite the decline in the age-standardized incidence, the absolute number of hip fractures increased owing to the aging population. The number of hip fractures is expected to increase from 18,338 in 2010 to 50,421 in 2035—a 2.7-fold increase. The number of bed days for 2010 and 2035 was estimated at 161,248 and 501,995, respectively, representing a 3.1-fold increase. Conclusions The socioeconomic impact of hip fractures will be high in the near future. This study provides a quantitative basis for future policy decisions to serve this need.

Estimating the lengths-of-stay (LoS) of hospitalised COVID-19 patients is key for predicting the hospital beds’ demand and planning mitigation strategies, as overwhelming the healthcare systems has critical consequences for disease mortality. However, accurately mapping the time-to-event of hospital outcomes, such as the LoS in the intensive care unit (ICU), requires understanding patient trajectories while adjusting for covariates and observation bias, such as incomplete data. Standard methods, such as the Kaplan-Meier estimator, require prior assumptions that are untenable given current knowledge. Using real-time surveillance data from the first weeks of the COVID-19 epidemic in Galicia (Spain), we aimed to model the time-to-event and event probabilities of patients’ hospitalised, without parametric priors and adjusting for individual covariates. We applied a non-parametric mixture cure model and compared its performance in estimating hospital ward (HW)/ICU LoS to the performances of commonly used methods to estimate survival. We showed that the proposed model outperformed standard approaches, providing more accurate ICU and HW LoS estimates. Finally, we applied our model estimates to simulate COVID-19 hospital demand using a Monte Carlo algorithm. We provided evidence that adjusting for sex, generally overlooked in prediction models, together with age is key for accurately forecasting HW and ICU occupancy, as well as discharge or death outcomes.