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.

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.

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.

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.

Hospital Occupancy and its Effect on Emergency Department Evaluation.

STUDY OBJECTIVE: To examine whether hospital occupancy was associated with increased testing and treatment during emergency department (ED) evaluations, resulting in reduced admissions. METHODS: We analyzed the electronic health records of an urban academic ED. We linked data from all ED visits from October 1, 2010, to May 29, 2015, with daily hospital occupancy (inpatients/total staffed beds). Outcome measures included the frequency of laboratory testing, advanced imaging, medication administration, and hospitalizations. We modeled each outcome using multivariable negative binomial or logistic regression, as appropriate, and examined their association with daily hospital occupancy quartiles, controlling for patient and visit characteristics. We calculated the adjusted outcome rates and relative changes at each daily hospital occupancy quartile using marginal estimating methods. RESULTS: We included 270,434 ED visits with a mean patient age of 48.1 (standard deviation 19.8) years; 40.1% were female, 22.8% were non-Hispanic Black, and 51.5% were commercially insured. Hospital occupancy was not associated with differences in laboratory testing, advanced imaging, or medication administration. Compared with the first quartile, the third and fourth quartiles of daily hospital occupancy were associated with decreases of 1.5% (95% confidence interval [CI] -2.9 to -0.2; absolute change -0.6 percentage points [95% CI -1.2 to -0.1]) and 4.6% (95% CI -6.0 to -3.2; absolute change -1.9 percentage points [95% CI -2.5 to -1.3]) in hospitalizations, respectively. CONCLUSION: The lack of association between hospital occupancy and laboratory testing, advanced imaging, and medication administration suggest that changes in ED testing or treatment did not facilitate the decrease in admissions during periods of high hospital occupancy.

The association between mechanical ventilator compatible bed occupancy and mortality risk in intensive care patients with COVID-19: a national retrospective cohort study.

BACKGROUND: The literature paints a complex picture of the association between mortality risk and ICU strain. In this study, we sought to determine if there is an association between mortality risk in intensive care units (ICU) and occupancy of beds compatible with mechanical ventilation, as a proxy for strain. METHODS: A national retrospective observational cohort study of 89 English hospital trusts (i.e. groups of hospitals functioning as single operational units). Seven thousand one hundred thirty-three adults admitted to an ICU in England between 2 April and 1 December, 2020 (inclusive), with presumed or confirmed COVID-19, for whom data was submitted to the national surveillance programme and met study inclusion criteria. A Bayesian hierarchical approach was used to model the association between hospital trust level (mechanical ventilation compatible), bed occupancy, and in-hospital all-cause mortality. Results were adjusted for unit characteristics (pre-pandemic size), individual patient-level demographic characteristics (age, sex, ethnicity, deprivation index, time-to-ICU admission), and recorded chronic comorbidities (obesity, diabetes, respiratory disease, liver disease, heart disease, hypertension, immunosuppression, neurological disease, renal disease). RESULTS: One hundred thirty-five thousand six hundred patient days were observed, with a mortality rate of 19.4 per 1000 patient days. Adjusting for patient-level factors, mortality was higher for admissions during periods of high occupancy (> 85% occupancy versus the baseline of 45 to 85%) [OR 1.23 (95% posterior credible interval (PCI): 1.08 to 1.39)]. In contrast, mortality was decreased for admissions during periods of low occupancy (< 45% relative to the baseline) [OR 0.83 (95% PCI 0.75 to 0.94)]. CONCLUSION: Increasing occupancy of beds compatible with mechanical ventilation, a proxy for operational strain, is associated with a higher mortality risk for individuals admitted to ICU. Further research is required to establish if this is a causal relationship or whether it reflects strain on other operational factors such as staff. If causal, the result highlights the importance of strategies to keep ICU occupancy low to mitigate the impact of this type of resource saturation.

The Impact of Halting Elective Admissions in Anticipation of a Demand Surge Due to the Coronavirus Pandemic (COVID-19).

BACKGROUND: In anticipation of a demand surge for hospital beds attributed to the coronavirus pandemic (COVID-19) many US states have mandated that hospitals postpone elective admissions. OBJECTIVES: To estimate excess demand for hospital beds due to COVID-19, the net financial impact of eliminating elective admissions in order to meet demand, and to explore the scenario when demand remains below capacity. RESEARCH DESIGN: An economic simulation to estimate the net financial impact of halting elective admissions, combining epidemiological reports, the US Census, American Hospital Association Annual Survey, and the National Inpatient Sample. Deterministic sensitivity analyses explored the results while varying assumptions for demand and capacity. SUBJECTS: Inputs regarding disease prevalence and inpatient utilization were representative of the US population. Our base case relied on a hospital admission rate reported by the Center for Disease Control and Prevention of 137.6 per 100,000, with the highest rates in people aged 65 years and older (378.8 per 100,000) and 50-64 years (207.4 per 100,000). On average, elective admissions accounted for 20% of total hospital admissions, and the average rate of unoccupied beds across hospitals was 30%. MEASURES: Net financial impact of halting elective admissions. RESULTS: On average, hospitals COVID-19 demand for hospital bed-days fell well short of hospital capacity, resulting in a substantial financial loss. The net financial impact of a 90-day COVID surge on a hospital was only favorable under a narrow circumstance when capacity was filled by a high proportion of COVID-19 cases among hospitals with low rates of elective admissions. CONCLUSIONS: Hospitals that restricted elective care took on a substantial financial risk, potentially threatening viability. A sustainable public policy should therefore consider support to hospitals that responsibly served their communities through the crisis.

Impact of Hospital Strain on Excess Deaths During the COVID-19 Pandemic - United States, July 2020-July 2021.

Surges in COVID-19 cases have stressed hospital systems, negatively affected health care and public health infrastructures, and degraded national critical functions (1,2). Resource limitations, such as available hospital space, staffing, and supplies led some facilities to adopt crisis standards of care, the most extreme operating condition for hospitals, in which the focus of medical decision-making shifted from achieving the best outcomes for individual patients to addressing the immediate care needs of larger groups of patients (3). When hospitals deviated from conventional standards of care, many preventive and elective procedures were suspended, leading to the progression of serious conditions among some persons who would have benefitted from earlier diagnosis and intervention (4). During March-May 2020, U.S. emergency department visits declined by 23% for heart attacks, 20% for strokes, and 10% for diabetic emergencies (5). The Cybersecurity & Infrastructure Security Agency (CISA) COVID Task Force* examined the relationship between hospital strain and excess deaths during July 4, 2020-July 10, 2021, to assess the impact of COVID-19 surges on hospital system operations and potential effects on other critical infrastructure sectors and national critical functions. The study period included the months during which the highly transmissible SARS-CoV-2 B.1.617.2 (Delta) variant became predominant in the United States.† The negative binomial regression model used to calculate estimated deaths predicted that, if intensive care unit (ICU) bed use nationwide reached 75% capacity an estimated 12,000 additional excess deaths would occur nationally over the next 2 weeks. As hospitals exceed 100% ICU bed capacity, 80,000 excess deaths would be expected in the following 2 weeks. This analysis indicates the importance of controlling case growth and subsequent hospitalizations before severe strain. State, local, tribal, and territorial leaders could evaluate ways to reduce strain on public health and health care infrastructures, including implementing interventions to reduce overall disease prevalence such as vaccination and other prevention strategies, as well as ways to expand or enhance capacity during times of high disease prevalence.

Estimating lengths-of-stay of hospitalised COVID-19 patients using a non-parametric model: a case study in Galicia (Spain).

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.

Markov chain analysis for the neonatal inpatient flow in a hospital.

Discrete-time Markov chain and queueing-theoretic models are used to quantitatively formulate the flow of neonatal inpatients over several wards in a hospital. Parameters of the models are determined from the operational analysis of the record of the numbers of admission/departure for each ward every day and the order log of patient movement from ward to ward for two years provided by the Medical Information Department of the University of Tsukuba Hospital in Japan. Our formulation is based on the analysis of the precise routes (the route of an inpatient is defined as a sequence of the wards in which he/she stays from admission to discharge) and their length-of-stay (LoS) in days in each ward on their routes for all neonatal inpatients. Our theoretical model calculates the probability distribution for the number of patients staying in each ward per day which agrees well with the corresponding histogram observed for each ward as well as for the whole hospital. The proposed method can be used for the long-term capacity planning of hospital wards with respect to the probabilistic bed utilization.

From intensive care to step-down units: Managing patients throughput in response to COVID-19.

QUALITY PROBLEM OR ISSUE: The on-going COVID-19 pandemic may cause the collapse of healthcare systems because of unprecedented hospitalization rates. INITIAL ASSESSMENT: A total of 8.2 individuals per 1000 inhabitants have been diagnosed with COVID-19 in our province. The hospital predisposed 110 beds for COVID-19 patients: on the day of the local peak, 90% of them were occupied and intensive care unit (ICU) faced unprecedented admission rates, fearing system collapse. CHOICE OF SOLUTION: Instead of increasing the number of ICU beds, the creation of a step-down unit (SDU) close to the ICU was preferred: the aim was to safely improve the transfer of patients and to relieve ICU from the risk of overload. IMPLEMENTATION: A nine-bed SDU was created next to the ICU, led by intensivists and ICU nurses, with adequate personal protective equipment, monitoring systems and ventilators for respiratory support when needed. A second six-bed SDU was also created. EVALUATION: Patients were clinically comparable to those of most reports from Western Countries now available in the literature. ICU never needed supernumerary beds, no patient died in the SDU, and there was no waiting time for ICU admission of critical patients. SDU has been affordable from human resources, safety and economic points of view. LESSONS LEARNED: COVID-19 is like an enduring mass casualty incident. Solutions tailored on local epidemiology and available resources should be implemented to preserve the efficiency and adaptability of our institutions and provide the adequate sanitary response.

Impact of a prolonged COVID-19 lockdown on patterns of admission, mortality and performance indicators in a cardiovascular intensive care unit.

BACKGROUND: The effects of an early and prolonged lockdown during the coronavirus disease 2019 (COVID-19) pandemic on cardiovascular intensive care units (CICUs) are not well established. OBJECTIVES: This study analyses patterns of admission, mortality and performance indicators in a CICU before and during the Argentine lockdown in the COVID-19 pandemic. METHODS: This is a retrospective observational cross-sectional study of all consecutive patients aged 18 years or more admitted to the cardiac intensive care unit at a high-volume reference hospital in Buenos Aires, Argentina, comparing hospitalization rates, primary causes of admission, inpatient utilization indicators, pharmacy supplies' expenditures and in-hospital mortality between 5 March and 31 July 2020, with two corresponding control periods in 2019 and 2018. RESULTS: We included 722 female patients [mean age of 61.6 (SD 15.5) years; 237 (32.8%)]. Overall hospitalizations dropped 53.2% (95%CI: 45.3, 61.0%), from 295.5 patients/year over the periods 2018/2019 to 137 patients in 2020. Cardiovascular disease-related admissions dropped 59.9%, while admission for non-cardiac causes doubled its prevalence from 9.6% over the periods 2018/2019 to 22.6% in the study period (P < 0.001).In the period 2020, the bed occupancy rate fell from 82.2% to 77.4%, and the bed turnover rate dropped 50% from 7.88 to 3.91 monthly discharges/bed. The average length of stay doubled from 3.26 to 6.75 days, and the turnover interval increased from 3.8 to 8.39 days in 2020.Pharmacy supplies' expenditures per discharge increased 134% along with a rise in antibiotics usage from 6.5 to 11.4 vials/ampoules per discharge (P < 0.02).Overall mortality increased from 7% (n = 41) to 13.9% (n = 19) (P = 0.008) at the expense of non-cardiac-related admissions (3.6-19.4%, P = 0.01). CONCLUSIONS: This study found a significant reduction in overall and cardiovascular disease-related causes of admission to the cardiac intensive care unit, worse performance indicators and increased in-hospital mortality along the first 5 months of the early and long-lasting COVID-19 lockdown in Argentina. These results highlight the need to foster public awareness concerning the risks of avoiding hospital attendance. Moreover, health systems should follow strict screening protocols to prevent potential biases in the admission of patients with critical conditions unrelated to the COVID-19 pandemic.

Healthcare indicators associated with COVID-19 death rates in the European Union.

OBJECTIVES: Identification of environmental and hospital indicators that may influence coronavirus disease 2019 (COVID-19) mortality in different countries is essential for better management of this infectious disease. STUDY DESIGN: Correlation analysis between healthcare system indicators and COVID-19 mortality rate in Europe. METHODS: For each country in the European Union (EU), the date of the first diagnosed case and the crude death rate for COVID-19 were retrieved from the John Hopkins University website. These data were then combined with environmental, hospital and clinical indicators extracted from the European Health Information Gateway of the World Health Organization. RESULTS: The COVID-19 death rate in EU countries (mean 1.9 ± 0.8%) was inversely associated with the number of available general hospitals, physicians and nurses. Significant positive associations were also found with the rate of acute care bed occupancy, as well as with the proportion of population who were aged older than 65 years, overweight or who had cancer. Total healthcare expenditure, public sector health expenditure and the number of hospital and acute care beds did not influence COVID-19 death rate. CONCLUSIONS: Some common healthcare system inadequacies, such as limited numbers of general hospitals, physicians and nurses, in addition to high acute care bed occupancy, may be significant drivers of nationwide COVID-19 mortality rates in EU countries.

Higher ICU Capacity Strain Is Associated With Increased Acute Mortality in Closed ICUs.

OBJECTIVES: To determine whether patients admitted to an ICU during times of strain, when compared with its own norm (i.e. accommodating a greater number of patients, higher acuity of illness, or frequent turnover), is associated with a higher risk of death in ICUs with closed models of intensivist staffing. DESIGN: We conducted a large, multicenter, observational cohort study. Multilevel mixed effects logistic regression was used to examine relationships for three measures of ICU strain (bed census, severity-weighted bed census, and activity-weighted bed census) on the day of admission with risk-adjusted acute hospital mortality. SETTING: Pooled case mix and outcome database of adult general ICUs participating in the Intensive Care National Audit and Research Centre Case Mix Programme. MEASUREMENTS AND MAIN RESULTS: The analysis included 149,310 patients admitted to 215 adult general ICUs in 213 hospitals in United Kingdom, Wales, and Northern Ireland. A relative lower strain in ICU capacity as measured by bed census on the calendar day (daytime hours) of admission was associated with decreased risk-adjusted acute hospital mortality (odds ratio, 0.94; 95% CI, 0.90-0.99; p = 0.01), whereas a nonsignificant association was seen between higher strain and increased acute hospital mortality (odds ratio, 1.04; 95% CI, 1.00-1.10; p = 0.07). The relationship between periods of high ICU strain and acute hospital mortality was strongest when bed census was composed of higher acuity patients (odds ratio, 1.05; 95% CI, 1.01-1.10; p = 0.03). No relationship was seen between high strain and ICU mortality. CONCLUSIONS: In closed staffing models of care, variations in bed census within individual ICUs was associated with patient's predicted risk of acute hospital mortality, particularly when its standardized bed census consisted of sicker patients.

Projecting demand for critical care beds during COVID-19 outbreaks in Canada.

BACKGROUND: Increasing numbers of coronavirus disease 2019 (COVID-19) cases in Canada may create substantial demand for hospital admission and critical care. We evaluated the extent to which self-isolation of mildly ill people delays the peak of outbreaks and reduces the need for this care in each Canadian province. METHODS: We developed a computational model and simulated scenarios for COVID-19 outbreaks within each province. Using estimates of COVID-19 characteristics, we projected the hospital and intensive care unit (ICU) bed requirements without self-isolation, assuming an average number of 2.5 secondary cases, and compared scenarios in which different proportions of mildly ill people practised self-isolation 24 hours after symptom onset. RESULTS: Without self-isolation, the peak of outbreaks would occur in the first half of June, and an average of 569 ICU bed days per 10 000 population would be needed. When 20% of cases practised self-isolation, the peak was delayed by 2-4 weeks, and ICU bed requirement was reduced by 23.5% compared with no self-isolation. Increasing self-isolation to 40% reduced ICU use by 53.6% and delayed the peak of infection by an additional 2-4 weeks. Assuming current ICU bed occupancy rates above 80% and self-isolation of 40%, demand would still exceed available (unoccupied) ICU bed capacity. INTERPRETATION: At the peak of COVID-19 outbreaks, the need for ICU beds will exceed the total number of ICU beds even with self-isolation at 40%. Our results show the coming challenge for the health care system in Canada and the potential role of self-isolation in reducing demand for hospital-based and ICU care.

Association Between Hospital Bed Occupancy and Outcomes in Emergency Care: A Cohort Study in Stockholm Region, Sweden, 2012 to 2016.

STUDY OBJECTIVE: We evaluate the importance of hospital bed occupancy for 30-day mortality, inhospital mortality, readmission for inpatient care within 30 days, and revisits to the emergency department (ED) within 7 days among all adult patients visiting the ED. METHODS: This was an observational cohort study including all adult patients visiting 6 EDs in Stockholm Region, Sweden. ED visits from 2012 to 2016 were categorized into groups by hospital bed occupancy in 5% intervals between 85% and 105%. A proportional hazards model was used to estimate adjusted hazard ratios with 95% confidence intervals (CIs). The model was stratified by hospital and adjusted for age, sex, comorbidities, hospital stays in the year preceding the index visit, marital status, length of education, and weekday/weekend timing of visit. RESULTS: A total of 816,832 patients with 2,084,554 visits were included. Mean hospital bed occupancy was 93.3% (SD 3.3%). In total, 28,112 patients died within 30 days, and 17,966 patients died inhospital. Hospital bed occupancy was not associated with 30-day mortality (hazard ratio for highest category of occupancy ≥105% was 1.10; 95% CI 0.96 to 1.27) or inhospital mortality. Patients discharged from the ED at occupancy levels greater than 89% had a 2% to 4% higher risk of revisits to the ED within 7 days. A 10% increase in hospital bed occupancy was associated with a 16-minute increase (95% CI 16 to 17 minutes) in ED length of stay and 1.9-percentage-point decrease (95% CI 1.7 to 2.0 percentage points) in admission rate. CONCLUSION: We did not find an association between increasing hospital bed occupancy and mortality in our sample of 6 EDs in Stockholm Region, Sweden, despite increased length of stay in the ED and a decline in admissions for inpatient care.

Multi-objective admission planning problem: a two-stage stochastic approach.

Effective admission planning can improve inpatient throughput and waiting times, resulting in better quality of service. The uncertainty in the patient arrival and the availability of resources makes the patient's allocation difficult to manage. Thus, in the admission process hospitals aim to accomplish targets of resource utilization and to lower the cost of service. Both objectives are related and in conflict. In this paper, we present a bi-objective stochastic optimization model to study the trade-off between the resource utilization and the cost of service, taking into account demand and capacity uncertainties. Real data from the surgery and medical areas of a Chilean public hospital are used to illustrate the approach. The results show that the solutions of our approach outperform the actual practice in the Chilean hospital.

Emergency short-stay wards and boarding time in emergency departments: A propensity-score matching study.

OBJECTIVES: This study aimed to validate the effectiveness of an emergency short-stay ward (ESSW) and its impact on clinical outcomes. METHODS: This retrospective observational study was performed at an urban tertiary hospital. An ESSW has been operating in this hospital since September 2017 to reduce emergency department (ED) boarding time and only targets patients indicated for admission to the general ward from the ED. Propensity-score matching was performed for comparison with the control group. The primary outcome was ED boarding time, and the secondary outcomes were subsequent intensive care unit (ICU) admission and 30-day in-hospital mortality. RESULTS: A total of 7461 patients were enrolled in the study; of them, 1523 patients (20.4%) were admitted to the ESSW. After propensity-score matching, there was no significant difference in the ED boarding time between the ESSW group and the control group (P = 0.237). Subsequent ICU admission was significantly less common in the ESSW group than in the control group (P < 0.001). However, the 30-day in-hospital mortality rate did not differ significantly between the two groups (P = 0.292). When the overall hospital bed occupancy ranged from 90% to 95%, the proportion of hospitalization was the highest in the ESSW group (29%). An interaction effect test using a general linear model confirmed that the ESSW served as an effect modifier with respect to bed occupancy and boarding time (P < 0.001). CONCLUSION: An ESSW can alleviate prolonged boarding time observed with hospital bed saturation. Moreover, the ESSW is associated with a low rate of subsequent ICU admission.

Patient Inventory: a quality improvement method.

Providing high quality care requires that patient care pathways are organized according to the needs of the patient. The organization of high-quality integrated patient care requires methods to assess 'appropriateness' of the care pathways to identify challenges in delivering the right procedure, for the right person at the right time and setting and with the most appropriate use of resources. There is a need for methods to assess appropriateness that can easily be implemented in daily clinical practice. The Patient Inventory method is such a method. Patient Inventory is a special type of audit that provides a 'snapshot' of the patient population in an entire hospital, a ward or another clinical unit. It maps the bed occupancy situation, as well as coordination, continuity and communication associated with the individual patient pathway. The aim is to identify inappropriate or wasteful events and to facilitate reflections on the underlying causes. These reflections are used to identify focus areas for quality improvement efforts. The method answers the question: 'Is it the right patient in the right place at the right time, and is the correct pathway for the patient organized with the most appropriate use of resources?' The aim of this method paper is to describe the background, definitions and methodologies for Patient Inventory, to offer a practical guidance for application of the method and to describe the current experiences with the method.

Determinants of Boarding of Patients with Severe Mental Illness in Hospital Emergency Departments.

BACKGROUND: Boarding of patients in hospital emergency departments (EDs) occurs routinely across the U.S. ED patients with behavioral health conditions are more likely to be boarded than other patients. However, the existing literature on ED boarding of psychiatric patients remains largely descriptive and has not empirically related mental health system capacity to psychiatric boarding. Nor does it show how the mental health system could better address the needs of populations at the highest risk of ED boarding. AIMS OF THE STUDY: We examined extent and determinants of "boarding" of patients with severe mental illness (SMI) in hospital emergency departments (ED) and tested whether greater mental health system capacity may mitigate the degree of ED boarding. METHODS: We linked Oregon's ED Information Exchange, hospital discharge, and Medicaid data to analyze encounters in Oregon hospital EDs from October 2014 through September 2015 by 7,103 persons aged 15 to 64 with SMI (N = 34,207). We additionally utilized Medicaid claims for years 2010-2015 to identify Medicaid beneficiaries with SMI. Boarding was defined as an ED stay over six hours. We estimated a recursive simultaneous-equation model to test the pathway that mental health system capacity affects ED boarding via psychiatric visits. RESULTS: Psychiatric visits were more likely to be boarded than non-psychiatric visits (30.2% vs. 7.4%). Severe psychiatric visits were 1.4 times more likely to be boarded than non-severe psychiatric visits. Thirty-four percent of psychiatric visits by children were boarded compared to 29.6% for adults. Statistical analysis found that psychiatric visit, substance abuse, younger age, black race and urban residence corresponded with an elevated risk of boarding. Discharge destinations such as psychiatric facility and acute care hospitals also corresponded with a higher probability of ED boarding. Greater supply of mental health resources in a county, both inpatient and intensive community-based, corresponded with a reduced risk of ED boarding via fewer psychiatric ED visits. DISCUSSION: Psychiatric visit, severity of psychiatric diagnosis, substance abuse, and discharge destinations are among important predictors of psychiatric ED boarding by persons with SMI. A greater capacity of inpatient and intensive community mental health systems may lead to a reduction in psychiatric ED visits by persons with SMI and thereby decrease the extent of psychiatric ED boarding. IMPLICATIONS FOR HEALTH POLICIES: Continued investment in mental health system resources may reduce psychiatric ED visits and mitigate the psychiatric ED boarding problem.