[The potential role of the Swedish Intensive Care Registry data for decision-making in intensive care]. / Intensivvårdsregistrets roll vid riskbedömning och prioritering.

The Swedish Intensive Care Registry collects and analyses data regarding intensive care in Sweden. Based on this data an overview can be created regarding available hospital beds and occupancy rates in Swedish intensive care, as well as demographic and clinical characteristics of the patients, and their survival. Through this, identification of patient groups with poorer prognosis is possible, facilitating reflection of the appropriateness of intensive care and invasive procedures that may cause discomfort to the patient.

A New Approach for Understanding International Hospital Bed Numbers and Application to Local Area Bed Demand and Capacity Planning.

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.

Trends in hospital capacity and utilization in Puerto Rico by health regions, 2010-2020.

Like many under resourced, island communities, most of the municipalities in Puerto Rico are medically underserved. However, there is limited information about changes in hospital capacity and any regional disparities in availability of hospital services in Puerto Rico, especially given the multiple public health emergencies the island has faced in recent years (e.g. hurricanes, earthquakes, and COVID-19). This study described the trends in hospital capacity and utilization for the Island of Puerto Rico and by health regions from 2010 to 2020. We analyzed the 2021-22 Area Health Resource File (AHRF) and aggregated the data by seven health regions, which are groupings of municipalities defined by the Puerto Rico Department of Health. Ten-year estimates for hospital utilization were adjusted for population size by health region. During the more recent five-year period, there were decreases in hospitals, hospital beds, and surgeries, which represent a shift from the earlier five-year period. Over the 10 years of the study period, there was an overall decrease in population-adjusted measures of hospital utilization on the island of Puerto Rico-despite multiple disasters that would, theoretically, increase need for health care services. We also found variation in hospital capacity and utilization by health regions indicating the rate of change was not uniform across Puerto Rico. The capacity of Puerto Rico's hospital system has shrunk over the past decade which may pose a challenge when responding to recurrent major public health emergencies, especially within specific health regions.

The allocation and utilization efficiency of hospital beds in Sichuan Province, China.

OBJECTIVE: To analyze the allocation and utilization efficiency of hospital beds in Sichuan Province, China, and to provide a scientific basis for improving the rational allocation and efficient utilization. METHODS: The supply and demand balance method, health resource agglomeration degree (HRAD), bed efficiency index and bed utilization model were used to evaluate the allocation and utilization efficiency of hospital beds in Sichuan Province from 2017 to 2021. RESULTS: The number of hospital beds per 1000 population in Sichuan Province increased from 4.97 in 2017 to 5.94 in 2021. The overall supply and demand ratio of hospital beds in Sichuan Province is between 0.85 and 1.01, and the supply and demand situation is a basically balanced situation. The HRAD of hospital beds in Ya'an, Aba, Ganzi and Liangshan is <1, indicating that the equity of hospital beds by geography in these regions is low. The difference between HRAD and population agglomeration degree (PAD) in 9 regions, including Deyang, Aba, Ganzi and Liangshan, is <0, indicating that there are insufficient hospital beds in these areas relative to the agglomerated population. The bed efficiency index of hospital beds in 17 regions, including Chengdu, Zigong, Aba and Ganzi, are all <1, which means that hospital beds are operating with low efficiency. The bed utilization model of Panzhihua is efficiency type, that of Zigong, Deyang and Ziyang is pressure bed type, and that of Nanchong and Ya'an is idle type. CONCLUSION: The hospital bed allocation in Sichuan Province is relatively good, and the supply and demand situation is in a basically balanced situation. The hospital bed allocation in Aba, Ganzi and Liangshan is insufficient by geography and population. The overall operational efficiency of hospital beds is low, and there are more idle and pressure bed utilization models.

[Future Developments in Trauma Care in Germany]. / Zukunft der Traumaversorgung in Deutschland.

Trauma surgical care in Germany faces major challenges. The increasing number of cases due to demographic change, combined with reduced bed capacity, requires a rethink in many areas. In order to continue to ensure basic and standard care at a high level and across the board in the future, economic incentives must be created to maintain sufficient locations for trauma care. At the same time, there is a shortage of skilled workers that will worsen in the coming years if appropriate measures are not taken to counteract it. Structural changes will also be needed to improve cross-sector networking between outpatient and inpatient care. With the increase in outpatient care, future shortages of both bed capacity and staff shortages may be buffered.

2022 insights on hospital bed distribution in Saudi Arabia: evaluating needs to achieve global standards.

BACKGROUND: Equitable geographical distribution of health resources, such as hospital beds, is fundamental in ensuring public accessibility to healthcare services. This study examines the distribution of hospital beds across Saudi Arabia's 20 health regions. METHODS: A secondary data analysis was conducted using the 2022 Saudi Ministry of Health Statistical Yearbook. The study focused on calculating the hospital beds-per-1,000-people ratio across Saudi Arabia's 20 health regions. The analysis involved comparing regional bed distributions using the Gini index and Lorenz curve to assess the distribution of hospital beds. RESULTS: The national average beds-per-1,000-people ratio was 2.43, serving a population of approximately 32.2 million. The calculated mean Gini index for bed distribution was 0.15, which indicates a relatively equitable distribution. Further analysis revealed some regional disparities, with health regions like Makkah and Jeddah displaying critically low bed-to-population ratios. In contrast, others like Al-Jouf and the Northern region reported higher ratios. The study also identified the need for an additional 17,062 beds to meet international standards of 2.9 beds per 1,000 people. CONCLUSIONS: The findings revealed a national average beds-per-1,000-people ratio of 2.43, with some regional disparities. The study highlights the critical need for targeted healthcare planning and policy interventions to address the uneven distribution of hospital beds across Saudi Arabia. TRIAL REGISTRATION: Not applicable.

Feasibility of forecasting future critical care bed availability using bed management data.

OBJECTIVES: This project aims to determine the feasibility of predicting future critical care bed availability using data-driven computational forecast modelling and routinely collected hospital bed management data. METHODS: In this proof-of-concept, single-centre data informatics feasibility study, regression-based and classification data science techniques were applied retrospectively to prospectively collect routine hospital-wide bed management data to forecast critical care bed capacity. The availability of at least one critical care bed was forecasted using a forecast horizon of 1, 7 and 14 days in advance. RESULTS: We demonstrated for the first time the feasibility of forecasting critical care bed capacity without requiring detailed patient-level data using only routinely collected hospital bed management data and interpretable models. Predictive performance for bed availability 1 day in the future was better than 14 days (mean absolute error 1.33 vs 1.61 and area under the curve 0.78 vs 0.73, respectively). By analysing feature importance, we demonstrated that the models relied mainly on critical care and temporal data rather than data from other wards in the hospital. DISCUSSION: Our data-driven forecasting tool only required hospital bed management data to forecast critical care bed availability. This novel approach means no patient-sensitive data are required in the modelling and warrants further work to refine this approach in future bed availability forecast in other hospital wards. CONCLUSIONS: Data-driven critical care bed availability prediction was possible. Further investigations into its utility in multicentre critical care settings or in other clinical settings are warranted.

Emergency Department Visits And Hospital Capacity In The US: Trends In The Medicare Population During The COVID-19 Pandemic.

Although emergency department (ED) and hospital overcrowding were reported during the later parts of the COVID-19 pandemic, the true extent and potential causes of this overcrowding remain unclear. Using data on the traditional fee-for-service Medicare population, we examined patterns in ED and hospital use during the period 2019-22. We evaluated trends in ED visits, rates of admission from the ED, and thirty-day mortality, as well as measures suggestive of hospital capacity, including hospital Medicare census, length-of-stay, and discharge destination. We found that ED visits remained below baseline throughout the study period, with the standardized number of visits at the end of the study period being approximately 25 percent lower than baseline. Longer length-of-stay persisted through 2022, whereas hospital census was considerably above baseline until stabilizing just above baseline in 2022. Rates of discharge to postacute facilities initially declined and then leveled off at 2 percent below baseline in 2022. These results suggest that widespread reports of overcrowding were not driven by a resurgence in ED visits. Nonetheless, length-of-stay remains higher, presumably related to increased acuity and reduced available bed capacity in the postacute care system.

The impact of deprivation on colorectal cancer-stage distribution in a setting with high hospital bed density: A Japanese multilevel study.

BACKGROUND: A methodology for determining the appropriate balance between medical access and combating poverty remains undetermined. To address the boundary conditions for exceedingly good medical access, this study examined whether the impact of deprivation on cancer stage distribution could be eliminated in Japan, which has the highest hospital bed density in the world. METHODS: A nationwide medical claims-based database was used to evaluate the influence of municipality-level hospital bed density and the postal code-level areal deprivation index on cancer stage at diagnosis. Given the limited number of similar studies in Japan, we focused on colorectal cancer (CRC), for which disparities have been reported in a prefecture-level study. Multilevel multivariate logistic regression models were used, with odds ratios (ORs) and 95% confidence intervals (CIs) adjusted for baseline and socioeconomic factors. RESULTS: Regardless of the early/advanced-stage definitions, CRC consistently tended to be detected at more advanced stages in more deprived areas. In the analysis of stages 0-I/II-IV, the OR (95% CI) was 1.09 (1.05, 1.14) (p < 0.001). In the analyses of stages 0-I/II-IV and 0-II/III-IV, gradients were observed, and later detections were observed for more deprived segments. Hospital bed density was not significantly associated with the stage distribution. CONCLUSION: The results indicate that inequalities in CRC detection due to deprivation persist even in the country with the highest hospital bed density worldwide, suggesting that poverty measures remain indispensable regardless of hospital bed access. Further investigation of various regions and cancers is required to develop a practical framework.

Closer to Home: A Quality Improvement Initiative to Optimize Satellite Site Bed Capacity.

BACKGROUND: The coronavirus disease 2019 pandemic resulted in the underutilization of inpatient beds at our satellite location. A lack of clarity and standardized admission criteria for the satellite led to frequent transfers to the main campus, resulting in patients traveling larger distances to receive inpatient care. We sought to optimize inpatient resource use at the satellite campus and keep patients "closer to home" by admitting eligible patients to that inpatient unit (LA4). Our aim was to increase bed capacity use at the satellite from 45% to 70% within 10 months. Our process measure was to increase the proportion of patients needing hospitalization who presented to the satellite emergency department (ED) and were then admitted to LA4 from 76% to 85%. METHODS: A multidisciplinary team used quality improvement methods to optimize bed capacity use. Interventions included (1) the revision and dissemination of satellite admission guidelines, (2) steps to create shared understanding of appropriate satellite admissions between ED and inpatient providers, (3) directed provider feedback on preventable main campus admissions, and (4) consistent patient and family messaging about the potential for transfer. Data were collected via chart review. Annotated run charts were used to assess the impact of interventions over time. RESULTS: Average LA4 bed capacity use increased from 45% to 69%, which was sustained for 1 year. The average percentage of patients admitted from the satellite ED to LA4 increased from 76% to 84%. CONCLUSIONS: We improved bed capacity use at our satellite campus through transparent admission criteria and shared mental models of patient care needs between ED and inpatient providers.

Exploring COVID-19 census burdens by US hospital characteristics: Implications of quality reporting at rural and critical access hospitals.

PURPOSE: By assessing longitudinal associations between COVID-19 census burdens and hospital characteristics, such as bed size and critical access status, we can explore whether pandemic-era hospital quality benchmarking requires risk-adjustment or stratification for hospital-level characteristics. METHODS: We used hospital-level data from the US Department of Health and Human Services including weekly total hospital and COVID-19 censuses from August 2020 to August 2023 and the 2021 American Hospital Association survey. We calculated weekly percentages of total adult hospital beds containing COVID-19 patients. We then calculated the number of weeks each hospital spent at Extreme (≥20% of beds occupied by COVID-19 patients), High (10%-19%), Moderate (5%-9%), and Low (<5%) COVID-19 stress. We assessed longitudinal hospital-level COVID-19 stress, stratified by 15 hospital characteristics including joint commission accreditation, bed size, teaching status, critical access hospital status, and core-based statistical area (CBSA) rurality. FINDINGS: Among n = 2582 US hospitals, the median(IQR) weekly percentage of hospital capacity occupied by COVID-19 patients was 6.7%(3.6%-13.0%). 80,268/213,383 (38%) hospital-weeks experienced Low COVID-19 census stress, 28% Moderate stress, 22% High stress, and 12% Extreme stress. COVID-19 census burdens were similar across most hospital characteristics, but were significantly greater for critical access hospitals. CONCLUSIONS: US hospitals experienced similar COVID-19 census burdens across multiple institutional characteristics. Evidence-based inclusion of pandemic-era outcomes in hospital quality reporting may not require significant hospital-level risk-adjustment or stratification, with the exception of rural or critical access hospitals, which experienced differentially greater COVID-19 census burdens and may merit hospital-level risk-adjustment considerations.

Effect of the Presence of Emergency Departments With 300 or More Hospital Beds in Health Service Areas on 30-Day Mortality in Korea: A Nationwide Retrospective Cross-sectional Study.

BACKGROUND: Disparities in emergency care accessibility exist between health service areas (HSAs). There is limited evidence on whether the presence of an emergency department (ED) that exceeds a certain hospital bed capacity is associated with emergency patient outcomes at the regional level. The objective of this study was to evaluate the effect of HSAs with or without of regional or local emergency centers with 300 or more hospital beds (EC300 or nEC300, respectively) by comparing the 30-day mortality of patients with severe emergency diseases (SEDs) admitted to the hospital through the ED. METHODS: The study retrospectively evaluated data from the National Health Information Database (NHID) of the National Health Insurance Service (NHIS) Claims database and enrolled patients who were admitted from the ED for SEDs. SEDs were defined using ICD-10 (International Classification of Diseases 10th Revision) codes for 28 disease categories with high severity, and 56 HSAs were designated as published by the NHIS. We performed hierarchical logistic regression analysis using multilevel models with the generalized linear mixed model (GLIMMIX) procedure to evaluate whether EC300 was associated with the 30-day mortality of SED patients, adjusting for patient-level, prehospital-level, hospital-level, and HSA-level variables. RESULTS: In total, 662 478 patients were analyzed, of whom 54 839 (8.3%) died within 30 days after hospital discharge. Of the 56 HSAs, 46 (82.1%) were included in the EC300 group. After adjustment for patient-level, prehospital-level, hospital-level, and HSA-level variables, nEC300 was significantly associated with increased 30-day mortality in SED patients (adjusted odds ratio [AOR]: 1.33, 95% CI: 1.137-1.153). In addition, patients who visited EDs with fewer annual SED admissions were associated with higher 30-day mortality. CONCLUSION: nEC300 had a greater risk of 30-day mortality in patients treated with SEDs than EC300. The results indicate that not only the number of EDs in each HSA is important for ensuring adequate patient outcomes but also the presence of EDs with adequate receiving capacity.

Association between the quantity of stroke care units and the complement of neurosurgical and neurology specialists in Japan: A retrospective study.

BACKGROUND: Stroke care units provide advanced intensive care for unstable patients with acute stroke. We conducted a survey to clarify the differences in stroke care units between urban and regional cities and the relationship between the number of stroke care unit beds and neurologists. METHODS: This retrospective observational study was conducted in 2,857 and 4,184 hospitals in urban and regional cities in 47 provinces of Japan, respectively, between January 2020 and August 2023. Tokyo and ordinance-designated cities in provinces were defined as urban cities, and those without such cities were defined as regional cities. The primary endpoint was the presence or absence of a stroke care unit. RESULTS: Multiple linear regression analysis revealed that the presence of stroke care units was significantly associated with the number of neurosurgical specialists. Receiver operating characteristic curve analysis was performed to predict the number of personnel required for stroke care unit installation based on the number of neurosurgical specialists. The area under the receiver operating characteristic curve, Youden index, sensitivity, and specificity were 0.721, 0.483, 0.783, and 0.700, respectively. CONCLUSIONS: Our study underscores the indispensability of SCUs in stroke treatment, advocating for a strategic allocation of medical resources, heightened accessibility to neurosurgical specialists, and a concerted effort to address geographic and resource imbalances. The identified cutoff value of 8.99 neurosurgical specialists per 100,000 population serves as a practical benchmark for optimizing SCU establishment, thereby potentially mitigating stroke-related mortality.

Association of medical care capacity and the patient mortality of septic shock: a cross-sectional study.

BACKGROUND: Hospitals with higher septic shock case volume demonstrated lower hospital mortality. We conducted this study to investigate whether this phenomenon was only caused by the increase in the number of admissions or the need to improve the medical care capacity in septic shock at the same time. METHODS: Seven-hundred and eighty-seven hospitals from China collected in a survey from January 1, 2021 to December 31, 2021. Medical care capacity for septic shock was explored by patients with septic shock in intensive care units (ICU) divided into beds, intensivists, and nurses respectively. MAIN RESULTS: The proportion of ICU patients with septic shock was negatively associated with the patient mortality of septic shock (Estimate [95%CI], -0.2532 [-0.5038, -0.0026]) (p-value 0.048). The ratios of patients with septic shock to beds, intensivists, and nurses were negatively associated with mortality of septic shock (Estimate [95%CI], -0.370 [-0.591, -0.150], -0.136 [-0.241, -0.031], and -0.774 [-1.158, -0.389]) (p-value 0.001, 0.011 and < 0.001). Severe pneumonia, the most common infection that caused a septic shock, correlated positively with its mortality (Estimate [95%CI], 0.1002 [0.0617, 0.1387]) (p-value < 0.001). CONCLUSIONS: Hospitals with higher medical care capacity for septic shock were associated with lower hospital mortality.

The role of stroke care infrastructure on the effectiveness of a hub-and-spoke telestroke model in South Carolina.

OBJECTIVE: To examine the relationship between stroke care infrastructure and stroke quality-of-care outcomes at 29 spoke hospitals participating in the Medical University of South Carolina (MUSC) hub-and-spoke telestroke network. MATERIALS AND METHODS: Encounter-level data from MUSC's telestroke patient registry were filtered to include encounters during 2015-2022 for patients aged 18 and above with a clinical diagnosis of acute ischemic stroke, and who received intravenous tissue plasminogen activator. Unadjusted and adjusted generalized estimating equations assessed associations between time-related stroke quality-of-care metrics captured during the encounter and the existence of the two components of stroke care infrastructure-stroke coordinators and stroke center certifications-across all hospitals and within hospital subgroups defined by size and rurality. RESULTS: Telestroke encounters at spoke hospitals with stroke coordinators and stroke center certifications were associated with shorter door-to-needle (DTN) times (60.9 min for hospitals with both components and 57.3 min for hospitals with one, vs. 81.2 min for hospitals with neither component, p <.001). Similar patterns were observed for the percentage of encounters with DTN time of ≤60 min (63.8% and 68.9% vs. 32.0%, p <.001) and ≤45 min (34.0% and 38.4% vs. 8.42%, p <.001). Associations were similar for other metrics (e.g., door-to-registration time), and were stronger for smaller (vs. larger) hospitals and rural (vs. urban) hospitals. CONCLUSIONS: Stroke coordinators or stroke center certifications may be important for stroke quality of care, especially at spoke hospitals with limited resources or in rural areas.

Addressing the Knowledge Deficit in Hospital Bed Planning and Defining an Optimum Region for the Number of Different Types of Hospital Beds in an Effective Health Care System.

Based upon 30-years of research by the author, a new approach to hospital bed planning and international benchmarking is proposed. The number of hospital beds per 1000 people is commonly used to compare international bed numbers. This method is flawed because it does not consider population age structure or the effect of nearness-to-death on hospital utilization. Deaths are also serving as a proxy for wider bed demand arising from undetected outbreaks of 3000 species of human pathogens. To remedy this problem, a new approach to bed modeling has been developed that plots beds per 1000 deaths against deaths per 1000 population. Lines of equivalence can be drawn on the plot to delineate countries with a higher or lower bed supply. This method is extended to attempt to define the optimum region for bed supply in an effective health care system. England is used as an example of a health system descending into operational chaos due to too few beds and manpower. The former Soviet bloc countries represent a health system overly dependent on hospital beds. Several countries also show evidence of overutilization of hospital beds. The new method is used to define a potential range for bed supply and manpower where the most effective health systems currently reside. The method is applied to total curative beds, medical beds, psychiatric beds, critical care, geriatric care, etc., and can also be used to compare different types of healthcare staff, i.e., nurses, physicians, and surgeons. Issues surrounding the optimum hospital size and the optimum average occupancy will also be discussed. The role of poor policy in the English NHS is used to show how the NHS has been led into a bed crisis. The method is also extended beyond international benchmarking to illustrate how it can be applied at a local or regional level in the process of long-term bed planning. Issues regarding the volatility in hospital admissions are also addressed to explain the need for surge capacity and why an adequate average bed occupancy margin is required for an optimally functioning hospital.

A stochastic programming approach to perform hospital capacity assessments.

This article introduces a bespoke risk averse stochastic programming approach for performing a strategic level assessment of hospital capacity (QAHC). We include stochastic treatment durations and length of stay in the analysis for the first time. To the best of our knowledge this is a new capability, not yet provided in the literature. Our stochastic programming approach identifies the maximum caseload that can be treated over a specified duration of time subject to a specified risk threshold in relation to temporary exceedances of capacity. Sample averaging techniques are applied to handle probabilistic constraints, but due to the size and complexity of the resultant mixed integer programming model, a novel two-stage hierarchical solution approach is needed. Our two-stage hierarchical solution approach is novel as it combines the application of a meta-heuristic with a binary search. It is also computationally fast. A case study of a large public hospital has been considered and extensive numerical tests have been undertaken to highlight the nuances and intricacies of the analysis. We conclude that the proposed approach is effective and can provide extra clarity and insights around hospital outputs. It provides a way to better calibrate hospitals and other health care infrastructure to future demands and challenges, like those created by the COVID pandemic.