Evaluating the impact of a ward environment with 20 single occupancy rooms and two four-bedded bays on patient and staff experiences and outcomes in an acute NHS Trust: a mixed-methods study protocol.

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.

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.

Bed demand and occupancy within the Brazilian National Health System for the most common types of cancer in Brazil, from 2018 to 2021: a cross-sectional study.

OBJECTIVE: To analyze bed demand and occupancy within the Brazilian National Health System (Sistema Único de Saúde - SUS) for the main types of cancer in Brazil, from 2018 to 2021. METHODS: This was a descriptive cross-sectional study, using data from the Hospital Information System. Queuing theory model was used for calculating average admission rate, average hospitalization rate, probability of overload, and average number of people in the queue. RESULTS: The Southeast and South regions showed the highest average hospitalization rates, while the North region showed the lowest rates. The Southeast region presented a high probability of surgical bed overload, especially in the states of São Paulo (99.0%), Minas Gerais (97.0%) and Rio de Janeiro (97.0%). São Paulo state showed an overload above 95.0% in all types of beds analyzed. CONCLUSION: There was a high probability of oncology bed occupancy within the Brazilian National Health System, especially surgical and medical beds, and regional disparities in bed overload. MAIN RESULTS: The study found a high demand for hospital admissions to oncological bed in the Southeast region and a high probability of system overload in the states of the Southeast and Northeast regions of Brazil, thus highlighting the inequities in access to healthcare services in the country. IMPLICATIONS FOR SERVICES: This study presents a methodology for the improved allocation of resources and management of surgical and medical bed flows in areas with the highest bed overload and regions with low service availability. PERSPECTIVES: It is necessary to promote public policies that ensure the equitable supply of beds for oncological treatment within the SUS, especially in states with bed overload and healthcare service gaps.

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.

Information management of full-cycle inpatient bed reservation for cancer patients under the normalised prevention and control of the COVID-19 pandemic.

BACKGROUND: During the prolonged COVID-19 pandemic, hospitals became focal points for normalised prevention and control. In this study, we investigated the feasibility of an inpatient bed reservation system for cancer patients that was developed in the department?s public WeChat account. We also explored its role in improving operational efficiency and nursing quality management, as well as in optimising nursing workforce deployment. METHODS: We utilised WeChat to facilitate communication between cancer patients and health care professionals. Furthermore, we collected data on admissions, discharges, average number of hospitalisation days, bed utilisation rate, and the number of bed days occupied by hospitalised patients through the hospital information system and nurses? working hours and competency levels through the nurse scheduling system. The average nursing hours per patient per day were calculated. Through the inpatient bed reservation system, the number of accepted admissions, denied admissions, and cancelled admissions from the reservation system were collected. The impact of the bed reservation system on the department?s operational efficiency was analysed by comparing the number of hospitalisation discharges before and after reservations, as well as the average hospitalisation and bed utilisation rates. By comparing nurses? working hours per month and average nursing hours per patient per day, the system?s impact on nurses? working hours and nursing quality indicators was analysed. RESULTS: The average hospitalisation length, bed utilisation rate, and nurses? working hours were significantly lower, and the average number of nursing hours per patient per day was significantly higher after the implementation of the reservation system. The full-cycle bed information management model for cancer patients did not affect the number of discharged patients. CONCLUSION: Patients? ability to reserve bed types from home in advance using the department?s official WeChat-based inpatient bed reservation system allowed nurses to prepare for their work ahead of time. This in turn improved the operational efficiency of the department and nursing quality, and it optimised the deployment of the nursing workforce.

The impact of hospital saturation on non-COVID-19 hospital mortality during the pandemic in France: a national population-based cohort study.

BACKGROUND: A previous study reported significant excess mortality among non-COVID-19 patients due to disrupted surgical care caused by resource prioritization for COVID-19 cases in France. The primary objective was to investigate if a similar impact occurred for medical conditions and determine the effect of hospital saturation on non-COVID-19 hospital mortality during the first year of the pandemic in France. METHODS: We conducted a nationwide population-based cohort study including all adult patients hospitalized for non-COVID-19 acute medical conditions in France between March 1, 2020 and 31 May, 2020 (1st wave) and September 1, 2020 and December 31, 2020 (2nd wave). Hospital saturation was categorized into four levels based on weekly bed occupancy for COVID-19: no saturation (< 5%), low saturation (> 5% and ≤ 15%), moderate saturation (> 15% and ≤ 30%), and high saturation (> 30%). Multivariate generalized linear model analyzed the association between hospital saturation and mortality with adjustment for age, sex, COVID-19 wave, Charlson Comorbidity Index, case-mix, source of hospital admission, ICU admission, category of hospital and region of residence. RESULTS: A total of 2,264,871 adult patients were hospitalized for acute medical conditions. In the multivariate analysis, the hospital mortality was significantly higher in low saturated hospitals (adjusted Odds Ratio/aOR = 1.05, 95% CI [1.34-1.07], P < .001), moderate saturated hospitals (aOR = 1.12, 95% CI [1.09-1.14], P < .001), and highly saturated hospitals (aOR = 1.25, 95% CI [1.21-1.30], P < .001) compared to non-saturated hospitals. The proportion of deaths outside ICU was higher in highly saturated hospitals (87%) compared to non-, low- or moderate saturated hospitals (81-84%). The negative impact of hospital saturation on mortality was more pronounced in patients older than 65 years, those with fewer comorbidities (Charlson 1-2 and 3 vs. 0), patients with cancer, nervous and mental diseases, those admitted from home or through the emergency room (compared to transfers from other hospital wards), and those not admitted to the intensive care unit. CONCLUSIONS: Our study reveals a noteworthy "dose-effect" relationship: as hospital saturation intensifies, the non-COVID-19 hospital mortality risk also increases. These results raise concerns regarding hospitals' resilience and patient safety, underscoring the importance of identifying targeted strategies to enhance resilience for the future, particularly for high-risk patients.

Decreased patient discharges on weekends part 1: what do the data tell us?

BACKGROUND: We believe that hospital and emergency department (ED) crowding is exacerbated on Mondays because fewer in-patients are discharged on the weekend. In part 1 of 3 concurrent studies, we documented the number of weekend discharges and the extent of hospital and ED crowding on the days following weekends. METHODS: We conducted a data analysis study at The Ottawa Hospital, a major academic health sciences center with two EDs. We created reports of the 18-month period (January 1, 2022-June 30, 2023) regarding the status of in-patients at the two campuses. We compared the total admissions, discharges, and hospital occupancy on weekends (or long weekends), the Monday following weekends (or Tuesday following long weekends), or Tuesdays-Fridays. For these three time periods, we also compared the proportion of ED beds occupied by admitted patients to all ED beds, as well as the proportion of days with > 70% admitted patients housed in the ED at 8:00am. RESULTS: Our data for 55,692 patients demonstrated that on weekends compared to weekdays, there were almost 50% fewer discharges with the ratio of admissions to discharges averaging 1.16 (95% CI 1.10-1.22). This was accompanied by a 2.4% absolute increase (P < 0.001) in hospital occupancy on Mondays or Tuesdays, often exceeding 100%. Both EDs are particularly crowded on these Mondays and Tuesdays with the proportion of admitted patients to regular ED beds averaging 68%. We observed serious crowding with > 70% occupancy with admitted patients on almost 50% of Mondays. INTERPRETATION: We have demonstrated that there are much fewer discharges on weekends, and this is associated with significant hospital and ED crowding on Mondays. This blocks safe and timely access to beds for newly arriving patients in the ED. These results should spur Canadian hospitals to evaluate their own data and seek solutions to this important problem.

ABSTRAIT: CONTEXTE: Nous croyons que le surpeuplement des hôpitaux et des services d'urgence (SU) est exacerbé le lundi parce que moins de patients hospitalisés reçoivent leur congé le week-end. Dans la partie 1 de trois études simultanées, nous avons documenté le nombre de congés de fin de semaine et l'ampleur du surpeuplement des hôpitaux et des urgences les jours suivants. MéTHODES: Nous avons mené une étude d'analyse des données à l'Hôpital d'Ottawa, un important centre universitaire des sciences de la santé qui compte deux urgences. Nous avons créé des rapports sur la période de 18 mois (du 1er janvier 2022 au 30 juin 2023) concernant l'état des patients hospitalisés sur les deux campus. Nous avons comparé le total des admissions, des sorties et de l'occupation de l'hôpital les fins de semaine (ou les longues fins de semaine), le lundi suivant les fins de semaine (ou le mardi suivant les longues fins de semaine) ou les mardis et vendredis. Pour ces trois périodes, nous avons également comparé la proportion de lits d'urgence occupés par des patients admis à tous les lits d'urgence, ainsi que la proportion de jours avec plus de 70 % de patients admis logés à l'urgence à 8 h. RéSULTATS: Nos données pour 55692 patients ont démontré que les week-ends par rapport aux jours de semaine, il y avait près de 50% moins de sorties avec un ratio d'admissions par rapport aux sorties de 1,16 (IC à 95% 1,10-1,22). Cela s'est accompagné d'une augmentation absolue de 2,4 % (p<0,001) de l'occupation des hôpitaux le lundi ou le mardi, souvent supérieure à 100 %. Les deux urgences sont particulièrement bondées ces lundis et mardis, la proportion de patients admis dans les lits réguliers d'urgence s'établissant en moyenne à 68 %. Nous avons observé un surpeuplement sérieux avec >70% d'occupation chez les patients admis sur près de 50% des lundis. INTERPRéTATION: Nous avons démontré qu'il y a beaucoup moins de congés la fin de semaine, ce qui est associé à une importante affluence d'hôpitaux et d'urgences le lundi. Cela bloque l'accès sécuritaire et rapide aux lits pour les patients nouvellement arrivés à l'urgence. Ces résultats devraient inciter les hôpitaux canadiens à évaluer leurs propres données et à chercher des solutions à ce problème important.

Real-time forecasting of COVID-19-related hospital strain in France using a non-Markovian mechanistic model.

Projects such as the European Covid-19 Forecast Hub publish forecasts on the national level for new deaths, new cases, and hospital admissions, but not direct measurements of hospital strain like critical care bed occupancy at the sub-national level, which is of particular interest to health professionals for planning purposes. We present a sub-national French framework for forecasting hospital strain based on a non-Markovian compartmental model, its associated online visualisation tool and a retrospective evaluation of the real-time forecasts it provided from January to December 2021 by comparing to three baselines derived from standard statistical forecasting methods (a naive model, auto-regression, and an ensemble of exponential smoothing and ARIMA). In terms of median absolute error for forecasting critical care unit occupancy at the two-week horizon, our model only outperformed the naive baseline for 4 out of 14 geographical units and underperformed compared to the ensemble baseline for 5 of them at the 90% confidence level (n = 38). However, for the same level at the 4 week horizon, our model was never statistically outperformed for any unit despite outperforming the baselines 10 times spanning 7 out of 14 geographical units. This implies modest forecasting utility for longer horizons which may justify the application of non-Markovian compartmental models in the context of hospital-strain surveillance for future pandemics.

Did COVID-19 ICU patient mortality risk increase as Colorado hospitals filled? A retrospective cohort study.

OBJECTIVES: To assess whether increasing levels of hospital stress-measured by intensive care unit (ICU) bed occupancy (primary), ventilators in use and emergency department (ED) overflow-were associated with decreasing COVID-19 ICU patient survival in Colorado ICUs during the pre-Delta, Delta and Omicron variant eras. DESIGN: A retrospective cohort study using discrete-time survival models, fit with generalised estimating equations. SETTING: 34 hospital systems in Colorado, USA, with the highest patient volume ICUs during the COVID-19 pandemic. PARTICIPANTS: 9196 non-paediatric SARS-CoV-2 patients in Colorado hospitals admitted once to an ICU between 1 August 2020 and 1 March 2022 and followed for 28 days. OUTCOME MEASURES: Death or discharge to hospice. RESULTS: For Delta-era COVID-19 ICU patients in Colorado, the odds of death were estimated to be 26% greater for patients exposed every day of their ICU admission to a facility experiencing its all-era 75th percentile ICU fullness or above, versus patients exposed for none of their days (OR: 1.26; 95% CI: 1.04 to 1.54; p=0.0102), adjusting for age, sex, length of ICU stay, vaccination status and hospital quality rating. For both Delta-era and Omicron-era patients, we also detected significantly increased mortality hazard associated with high ventilator utilisation rates and (in a subset of facilities) states of ED overflow. For pre-Delta-era patients, we estimated relatively null or even protective effects for the same fullness exposures, something which provides a meaningful contrast to previous studies that found increased hazards but were limited to pre-Delta study windows. CONCLUSIONS: Overall, and especially during the Delta era (when most Colorado facilities were at their fullest), increasing exposure to a fuller hospital was associated with an increasing mortality hazard for COVID-19 ICU patients.

A national survey of current practice on patients with long term and additional needs in paediatric intensive care units.

BACKGROUND: Patients with long term and additional needs (LEAP) in paediatric intensive care units (PICUs) are a growing and heterogenous cohort that provide unique challenges to clinicians. Currently no standard approach to define and manage this cohort exists. AIM: To analyse bed occupancy, examine current practice, and explore ideas to improve PICU care of patients with long term and additional needs. STUDY DESIGN: Patients with LEAP were defined as meeting two or more of the following criteria: length of stay >14 days; life limiting condition; ≥2 failed extubations; hospital stay >1 month prior to PICU admission; likely to require long-term ventilation. An electronic survey was then sent to all UK PICUs, via the UK Paediatric Critical Care Society, to collect quantitative and qualitative data relating to bed occupancy, length of stay, multidisciplinary and family involvement, and areas of possible improvement. Data collection were occurred between 8 February 2022 and 14 March 2022. Quantitative data were analysed using Microsoft Excel 365 and SPSS Statistics version 28.0. Raw data and descriptive statistics were reported, including percentages and median with interquartile range for non-parametric data. Qualitative raw data were examined using thematic analysis. Analysis was undertaken independently by two authors and results assessed for concordance. RESULTS: 70.1% (17/24) PICUs responded. 25% (67/259) of PICU beds were occupied by patients with long term and additional needs. 29% (5/17) of responding units have tailored management plans to this cohort of patient. A further 11% (2/17) have guidelines for children with generic chronic illness. 12% (2/16) of responding units had a designated area and 81% (13/16) of responding units had designated professionals. The majority (68% and 62%) of responding units engaged families and community professionals in multidisciplinary meetings. When asked how the care of long term and additional needs patients might be improved five themes were identified: consistent, streamlined care pathways; designated transitional care units; designated funding and hospital-to-home commissioning; development of roles to facilitate collaboration between hospital and community teams; proactive discharge planning and parallel planning. CONCLUSIONS: This survey provides a snapshot of UK practice for a cohort of patients that occupies a considerable proportion (29%) of PICU beds. While only a minority of responding PICUs offer specifically tailored management plans, the majority of units have designated professionals. RELEVANCE TO CLINICAL PRACTICE: Opportunities exist to improve PICU care in LEAP patients in areas such as: streamlined care pathways, designated clinical areas, designated funding, and development of defined collaborative roles. Next steps may involve working group convention to develop a consensus definition and share good practice examples.

Forecasting local hospital bed demand for COVID-19 using on-request simulations.

Accurate forecasting of hospital bed demand is crucial during infectious disease epidemics to avoid overwhelming healthcare facilities. To address this, we developed an intuitive online tool for individual hospitals to forecast COVID-19 bed demand. The tool utilizes local data, including incidence, vaccination, and bed occupancy data, at customizable geographical resolutions. Users can specify their hospital's catchment area and adjust the initial number of COVID-19 occupied beds. We assessed the model's performance by forecasting ICU bed occupancy for several university hospitals and regions in Germany. The model achieves optimal results when the selected catchment area aligns with the hospital's local catchment. While expanding the catchment area reduces accuracy, it improves precision. However, forecasting performance diminishes during epidemic turning points. Incorporating variants of concern slightly decreases precision around turning points but does not significantly impact overall bed occupancy results. Our study highlights the significance of using local data for epidemic forecasts. Forecasts based on the hospital's specific catchment area outperform those relying on national or state-level data, striking a better balance between accuracy and precision. These hospital-specific bed demand forecasts offer valuable insights for hospital planning, such as adjusting elective surgeries to create additional bed capacity promptly.

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.

Assessing the impact of COVID-19 on outpatient and inpatient revenues: A comparative analysis of large and small hospitals in Taiwan.

BACKGROUND: The COVID-19 pandemic has had profound effects on healthcare systems worldwide, not only by straining medical resources but also by significantly impacting hospital revenues. These economic repercussions have varied across different hospital departments and facility sizes. This study posits that outpatient (OPD) revenues experienced greater reductions than inpatient (IPD) revenues and that the financial impact was more profound in larger hospitals than in smaller hospitals. METHODS: We collected data on patient case numbers and associated revenues for 468 hospitals from the Taiwan government-run National Health Insurance Administration website. We then employed Microsoft Excel to construct scatter plots using the trigonometric function (=DEGREES (Atan (growth rate))) for each hospital. Our analysis scrutinized 4 areas: the case numbers and the revenues (represented by medical fees) submitted to the Taiwan government-run National Health Insurance Administration in both March and April of 2019 and 2020 for OPD and IPD departments. The validity of our hypotheses was established through correlation coefficients (CCs) and chi-square tests. Moreover, to visualize and substantiate the hypothesis under study, we utilized the Kano diagram. A higher CC indicates consistent counts and revenues between 2019 and 2020. RESULTS: Our findings indicated a higher impact on OPDs, with CCs of 0.79 and 0.83, than on IPDs, which had CCs of 0.40 and 0.18. Across all hospital types, there was a consistent impact on OPDs (P = .14 and 0.46). However, a significant variance was observed in the impact on IPDs (P < .001), demonstrating that larger hospitals faced greater revenue losses than smaller facilities, especially in their inpatient departments. CONCLUSION: The two hypotheses confirmed that the COVID-19 pandemic impacted outpatient departments more than inpatient departments. Larger hospitals in Taiwan faced greater financial challenges, especially in inpatient sectors, underscoring the pandemic's varied economic effects. The COVID-19 pandemic disproportionately affected outpatient departments and larger hospitals in Taiwan. Policymakers must prioritize support for these areas to ensure healthcare resilience in future epidemics. The research approach used in this study can be utilized as a model for similar research in other countries affected by COVID-19.

Six-month post-intensive care outcomes during high and low bed occupancy due to the COVID-19 pandemic: A multicenter prospective cohort study.

INTRODUCTION: The COVID-19 pandemic can be seen as a natural experiment to test how bed occupancy affects post-intensive care unit (ICU) patient's functional outcomes. To compare by bed occupancy the frequency of mental, physical, and cognitive impairments in patients admitted to ICU during the COVID-19 pandemic. METHODS: Prospective cohort of adults mechanically ventilated >48 hours in 19 ICUs from seven Chilean public and private hospitals. Ninety percent of nationwide beds occupied was the cut-off for low versus high bed occupancy. At ICU discharge, 3- and 6-month follow-up, we assessed disability using the World Health Organization Disability Assessment Schedule 2.0. Quality of life, mental, physical, and cognitive outcomes were also evaluated following the core outcome set for acute respiratory failure. RESULTS: We enrolled 252 participants, 103 (41%) during low and 149 (59%) during high bed occupancy. Patients treated during high occupancy were younger (P50 [P25-P75]: 55 [44-63] vs 61 [51-71]; p<0.001), more likely to be admitted due to COVID-19 (126 [85%] vs 65 [63%]; p<0.001), and have higher education qualification (94 [63%] vs 48 [47%]; p = 0.03). No differences were found in the frequency of at least one mental, physical or cognitive impairment by bed occupancy at ICU discharge (low vs high: 93% vs 91%; p = 0.6), 3-month (74% vs 63%; p = 0.2) and 6-month (57% vs 57%; p = 0.9) follow-up. CONCLUSIONS: There were no differences in post-ICU outcomes between high and low bed occupancy. Most patients (>90%) had at least one mental, physical or cognitive impairment at ICU discharge, which remained high at 6-month follow-up (57%). CLINICAL TRIAL REGISTRATION: NCT04979897 (clinicaltrials.gov).

Fitting a stochastic model of intensive care occupancy to noisy hospitalization time series during the COVID-19 pandemic.

Intensive care occupancy is an important indicator of health care stress that has been used to guide policy decisions during the COVID-19 pandemic. Toward reliable decision-making as a pandemic progresses, estimating the rates at which patients are admitted to and discharged from hospitals and intensive care units (ICUs) is crucial. Since individual-level hospital data are rarely available to modelers in each geographic locality of interest, it is important to develop tools for inferring these rates from publicly available daily numbers of hospital and ICU beds occupied. We develop such an estimation approach based on an immigration-death process that models fluctuations of ICU occupancy. Our flexible framework allows for immigration and death rates to depend on covariates, such as hospital bed occupancy and daily SARS-CoV-2 test positivity rate, which may drive changes in hospital ICU operations. We demonstrate via simulation studies that the proposed method performs well on noisy time series data and apply our statistical framework to hospitalization data from the University of California, Irvine (UCI) Health and Orange County, California. By introducing a likelihood-based framework where immigration and death rates can vary with covariates, we find, through rigorous model selection, that hospitalization and positivity rates are crucial covariates for modeling ICU stay dynamics and validate our per-patient ICU stay estimates using anonymized patient-level UCI hospital data.

Difference in determinants of ICU admission and death among COVID-19 hospitalized patients in two epidemic waves in Portugal: possible impact of healthcare burden and hospital bed occupancy on clinical management and outcomes, March-December 2020.

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.

RSV Burden and Its Impact on Pediatric Inpatient Bed Occupancy in Belgium: An Analysis of National Hospital Claims Data.

BACKGROUND: Respiratory syncytial virus (RSV) infections represent a substantial burden on pediatric services during winter. While the morbidity and financial burden of RSV are well studied, less is known about the organizational impact on hospital services (ie, impact on bed capacity and overcrowding and variation across hospitals). METHODS: Retrospective analysis of the population-wide Belgian Hospital Discharge Data Set for the years 2017 and 2018 (including all hospital sites with pediatric inpatient services), covering all RSV-associated (RSV-related International Classification of Diseases, 10th Version, Clinical Modification diagnoses) inpatient hospitalization by children under 5 years old as well as all-cause acute hospitalizations in pediatric wards. RESULTS: RSV hospitalizations amount to 68.3 hospitalizations per 1000 children less than 1 year and 5.0 per 1000 children 1-4 years of age and are responsible for 20%-40% of occupied beds during the peak period (November-December). The mean bed occupancy rate over the entire year (2018) varies across hospitals from 22.8% to 85.1% and from 30.4% to 95.1% during the peak period. Small-scale pediatric services (<25 beds) are more vulnerable to the volatility of occupancy rates. Forty-six hospital sites have daily occupancy rates above 100% (median of 9 days). Only in 1 of 23 geographically defined hospital networks these high occupancy rates are on the same calendar days. CONCLUSIONS: Pediatric services tend to be over-dimensioned to deal with peak activity mainly attributable to RSV. RSV immunization can substantially reduce pediatric capacity requirements. Enhanced collaboration in regional networks is an alternative strategy to deal with peaks and reduce capacity needs.

Gerenciamento de Leitos: do Planejamento à Implementação em um Serviço Hospitalar / Hospital bed management: from planning to implementation in a hospital service

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)

Gestión de camas hospitalarias durantela pandemia en Navarra con el apoyo demétodos matemáticos de predicción / Management of hospital beds during the pandemic in Navarra with the support of mathematical prediction methods

Durante la pandemia por coronavirus, en Navarra se utilizaron modelos matemáticos depredicción para estimar las camas necesarias, convencionales y de críticos, para atender alos pacientes COVID-19. Las seis ondas pandémicas presentaron distinta incidencia en la población, ocasionandovariabilidad en los ingresos hospitalarios y en la ocupación hospitalaria. La respuesta a laenfermedad de los pacientes no fue constante en cada onda, por lo que, para la predicción decada una, se utilizaron los datos correspondientes de esa onda.El método de predicción constó de dos partes: una describió la entrada de pacientes alhospital y la otra su estancia dentro del mismo. El modelo requirió de la alimentación a tiempo real de los datos actualizados. Los resultados delos modelos de predicción fueron posteriormente volcados al sistema de información corporativotipo Business Intelligence. Esta información fue utilizada para planificar el recurso cama y lasnecesidades de profesionales asociadas a la atención de estos pacientes en el ámbito hospitalario.En la cuarta onda se realizó un análisis para cuantificar el grado de acierto de los modelospredictivos. Los modelos predijeron adecuadamente el pico, la meseta y el cambio detendencia, pero sobreestimaron los recursos necesarios para la atención de los pacientes enla parte descendente de la curva. El principal punto fuerte de la sistemática utilizada para la construcción de modelospredictivos fue proporcionar modelos en tiempo real con datos recogidos con precisión porlos sistemas de información que consiguieron un grado de acierto aceptable permitiendo unautilización inmediata.(AU)