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Abstract Background Intensive Care Unit (ICU) capacity management is essential to provide high-quality healthcare for critically ill patients. Yet, consensus on the most favorable ICU design is lacking, especially whether ICUs should deliver dedicated or non-dedicated care. The decision for dedicated or non-dedicated ICU design considers a trade-off in the degree of specialization for individual patient care and efficient use of resources for society. We aim to share insights of a model simulating capacity effects for different ICU designs. Upon request, this simulation model is available for other ICUs. Methods A discrete event simulation model was developed and used, to study the hypothetical performance of a large University Hospital ICU on occupancy, rejection, and rescheduling rates for a dedicated and non-dedicated ICU design in four different scenarios. These scenarios either simulate the base-case situation of the local ICU, varying bed capacity levels, potential effects of reduced length of stay for a dedicated design and unexpected increased inflow of unplanned patients. Results The simulation model provided insights to foresee effects of capacity choices that should be made. The non-dedicated ICU design outperformed the dedicated ICU design in terms of efficient use of scarce resources. Conclusions The choice to use dedicated ICUs does not only affect the clinical outcome, but also rejection- rescheduling and occupancy rates. Our analysis of a large university hospital demonstrates how such a model can support decision making on ICU design, in conjunction with other operation characteristics such as staffing and quality management.

Hospital bed occupancy serves as an important indicator of healthcare system efficiency, directly impacting patient care quality and staff workload. This study delves into the efficacy of midnight census, a conventional method for assessing bed occupancy, in supporting hospital operational planning. Historically, the midnight census has been utilised to gauge bed occupancy; however, its reliability is debated due to fluctuations throughout the day. This paper presents an analysis of 5.5 years of patient flow data from one of the hospitals in Queensland, Australia, scrutinising the statistical associations between different occupancy levels, e.g., midnight, peak, average, and minimum. The findings shed light on the efficacy of the midnight census and suggest the adoption of an hourly-based occupancy rate for more accurate capacity planning and management. [ABSTRACT FROM AUTHOR]

Background: Intensive Care Unit (ICU) capacity management is essential to provide high-quality healthcare for critically ill patients. Yet, consensus on the most favorable ICU design is lacking, especially whether ICUs should deliver dedicated or non-dedicated care. The decision for dedicated or non-dedicated ICU design considers a trade-off in the degree of specialization for individual patient care and efficient use of resources for society. We aim to share insights of a model simulating capacity effects for different ICU designs. Upon request, this simulation model is available for other ICUs. Methods: A discrete event simulation model was developed and used, to study the hypothetical performance of a large University Hospital ICU on occupancy, rejection, and rescheduling rates for a dedicated and non-dedicated ICU design in four different scenarios. These scenarios either simulate the base-case situation of the local ICU, varying bed capacity levels, potential effects of reduced length of stay for a dedicated design and unexpected increased inflow of unplanned patients. Results: The simulation model provided insights to foresee effects of capacity choices that should be made. The non-dedicated ICU design outperformed the dedicated ICU design in terms of efficient use of scarce resources. Conclusions: The choice to use dedicated ICUs does not only affect the clinical outcome, but also rejection- rescheduling and occupancy rates. Our analysis of a large university hospital demonstrates how such a model can support decision making on ICU design, in conjunction with other operation characteristics such as staffing and quality management. [ABSTRACT FROM AUTHOR]

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Purpose: This article aims to identify the benefits and difficulties managers perceive regarding implementing digital transformation (DT) in hospital bed management. Originality/value: The article contributes to academia by analyzing a relevant topic, DT of health, that is scarcely explored in the multifactorial perspective of its technological, organizational, and social elements. No similar study was identified with application in a large private hospital located in a developing country. By analyzing managerial perceptions about implementing DT applied to hospital bed management, the study allows a better understanding and potential strengthening of this DT process by the professionals involved, which can contribute to expanding access to hospital care. Design/methodology/approach: A single case study was carried out to investigate the implementation of DT for managing hospital beds in a private general hospital in São Paulo, Brazil. We interviewed 19 managers responsible for hospital bed management. The interviews were analyzed using two methods: content analysis and data mining, and they were classified according to their technological, organizational, and social elements (Reis et al., 2018). Findings: DT's most cited potential benefits were efficiency gains, realtime information, and employee and patient satisfaction. The main dificulties reported were systems integration, data availability in realtime, instituting new processes, and challenges related to organizational culture and the necessary changes. DT can positively impact bed flow management, but it is crucial to consider the technological, organizational, and social elements involved in its success. [ABSTRACT FROM AUTHOR]

Intensive Care Unit (ICU) capacity can be significantly affected by disease outbreaks, epidemics, and pandemics, impeding the operational efficiency of healthcare systems and compromising patient care. This paper presents a multi-stage optimization approach to planning the location and distribution of ICU beds to increase accessibility and reduce mortality caused by a shortage of beds in a geographic region during epidemic events. Using a Brazilian state monthly hospital admissions due to Covid-19 from October 2020 to April 2021, we show the amount and the allocation of extra ICU beds that could reduce mortality, minimize patient travel and transportation, and increase accessibility while considering budget limitations. Our findings show coverage for 21 previously underserved municipalities, providing extra ICU beds for 69 municipalities, ranging from 880 to 1670 beds across seven months. On average, patients are displaced 56% less and access ICUs within 17 ± 2.3 kilometres (CI 95%). The strategy contributes to public health planning and the equitable allocation of hospital resources among the population.

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This project aimed to design and implement an emergency department-managed observation unit that improves inpatient bed and emergency department stretcher capacity, decreases observation patient length of stay, earns high patient satisfaction scores, and generates a positive fiscal impact on the organization. This quality improvement project followed a 1-group, pre- and postprogram implementation design. In the first year of operations, 40% of the total observation patients treated in this hospital were managed in the new observation unit. Emergency department observation unit length of stay across all patient complaints was half of the average length of stay for observation patients located on hospital inpatient units. In most cases, the emergency department observation unit was in the top 25 percentile of hospital Press Ganey inpatient satisfaction categories. The hospital estimates a contribution margin of three-quarters of a million dollars in the first year. This effective and efficient hybrid observation unit possessed specific aspects of inpatient and emergency department patient care models. Placing providers and nurses at the workstation for faster communication expedited care. Prioritizing all observation patient testing, transportation, phlebotomy, and intravenous (IV) services shortened disposition times. Emergency nurses transitioning to the observation unit were challenged to acquire inpatient care knowledge. Observation unit management struggled to maintain staffing while under an inpatient productivity model managed by the inpatient house supervisor. Reducing patient disposition time required clear communication between observation unit and inpatient staffing managers, between physician consultants and advanced practice nursing providers, and among nurses, patients, and providers. Observation units are 1 solution to decrease observation patient length of stay and improve emergency department capacity. [ABSTRACT FROM AUTHOR]

Some hospitals have faced a surge of patients with COVID-19, while others have not. We assessed whether COVID-19 burden (number of patients with COVID-19 admitted during April 2020 divided by hospital certified bed count) was associated with mortality in a large sample of US hospitals. Our study population included 14,226 patients with COVID-19 (median age 66 years, 45.2% women) at 117 hospitals, of whom 20.9% had died at 5 weeks of follow-up. At the hospital level, the observed mortality ranged from 0% to 44.4%. After adjustment for age, sex, and comorbidities, the adjusted odds ratio for in-hospital death in the highest quintile of burden was 1.46 (95% CI, 1.07-2.00) compared to all other quintiles. Still, there was large variability in outcomes, even among hospitals with a similar level of COVID-19 burden and after adjusting for age, sex, and comorbidities.

BACKGROUND: Among different tools, accreditation is widely used worldwide to improve the quality and safety of hospital services. In Iran, as in many other countries, the same accreditation standards apply to all hospitals, regardless of their size and type of activity. This has given rise to many problems for hospitals. MATERIALS AND METHODS: We will conduct this study in three phases: In the first phase, relevant individuals are interviewed to identify challenges caused to hospitals by applying the same standards for all types of hospitals and clarify issues that could be removed or changed in small hospitals. In the second phase, a scoping review is conducted on the literature about accreditation models worldwide. The first and second phases are conducted simultaneously, and a new accreditation model for Iran hospitals is derived by combining their results. In the final phase, using the Delphi technique, the obtained model and accreditation modules are verified during Delphi rounds. DISCUSSION: A more appropriate accreditation model that matches the characteristics of the target hospitals could be the output of this study. It is expected that the model could improve the process of evaluating the quality of hospital services through the accreditation tool.

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The coronavirus 2019 (COVID-19) pandemic requires drastic changes in allocation of resources, which can affect the delivery of stroke care, and many providers are seeking guidance. As caregivers, we are guided by 3 distinct principles that will occasionally conflict during the pandemic: (1) we must ensure the best care for those stricken with COVID-19, (2) we must provide excellent care and advocacy for patients with cerebrovascular disease and their families, and (3) we must advocate for the safety of health care personnel managing patients with stroke, with particular attention to those most vulnerable, including trainees. This descriptive review by a diverse group of experts in stroke care aims to provide advice by specifically addressing the potential impact of this pandemic on (1) the quality of the stroke care delivered, (2) ethical considerations in stroke care, (3) safety and logistic issues for providers of patients with stroke, and (4) stroke research. Our recommendations on these issues represent our best opinions given the available information, but are subject to revision as the situation related to the COVID-19 pandemic continues to evolve. We expect that ongoing emergent research will offer additional insights that will provide evidence that could prompt the modification or removal of some of these recommendations.

This study investigated legislative proposals on the single waiting list for hospitalizations and ICU beds within the scope of the Federal Legislative Branch in the first year of the pandemic (2020). This was an exploratory, qualitative, and document-based study, which analyzed bills analyzed in the Brazilian National Congress on the subject. The results were organized according to the authors' profile and qualitative content of the bills. There was a predominance of male parliamentarians, affiliated with left-wing parties and professional training in areas other than health. Most bills dealt with a general single waiting list, specifying hospital beds, the mixed management of hospital beds, and indemnity through the Brazilian Unified Health System's (SUS, in Portuguese) price table. The House of Representatives presented more bills, but no progress was made in their processing. Among the analyzed bills, only one was prioritized in the External Commission to Combat COVID-19. It was concluded that the Federal Legislative Branch once again missed the chance to legislate for the future and prepare the country with a normative framework capable of confronting health emergencies, which will demand much from health managers and SUS itself. [ABSTRACT FROM AUTHOR]

ObjectiveTo examine whether hospitals are more likely to temporarily close their emergency departments (EDs) to ambulances (through ambulance diversions) if neighboring diverting hospitals are public vs private.Data sources/study settingAmbulance diversion logs for California hospitals, discharge data, and hospital characteristics data from California's Office of Statewide Health Planning and Development and the American Hospital Association (2007).Study designWe match public and private (nonprofit or for-profit) hospitals by distance and size. We use random-effects models examining diversion probability and timing of private hospitals following diversions by neighboring public vs matched private hospitals.Data collection/extraction methodsN/A.Principal findingsHospitals are 3.6 percent more likely to declare diversions if neighboring diverting hospitals are public vs private (P

Abstract Background Shortage of resources, such as hospital beds, needed for health care especially in times of crisis can be a serious challenge for many countries. Currently, there is no suitable model for optimal allocation of beds in different hospital wards. The Data Envelopment Analysis method (DEA) has been used in the present study to examine the evacuation and allocation of hospital beds during the covid-19 pandemic in order to contribute to effective planning for fighting the spread the covid-19 virus. Methods The present study was conducted in two stages in hospitals affiliated with Urmia University of Medical Sciences (UUMS) in 2021. First, the number of excess beds was determined by calculating the technical efficiency using the DEA method and Deap2.1 software. To reallocate excess beds to covid-19 patients, the types of hospital wards were considered. As a result of this analysis, the inefficient hospitals with excess beds in different wards, which could be used for covid-19 patients with more serious symptoms, were identified. Results The results of the study show that the average technical efficiency of the studied hospitals was 0.603. These hospitals did not operate efficiently in 2021 and their current output can be produced with less than 61% of the used input. Also, the potential of these hospitals, over a period of 1 year, for the evacuation of beds and reallocation of them to covid-19 patients was calculated to be 1781 beds, 450 of which belonged to general wards and 1331 belonged to specialized wards. Conclusions The DEA method can be used in the allocation of resources in hospitals. Depending on the type of hospital wards and the health condition of patients, this method can help policy-makers identify hospitals with the best potential but less emergency services for the purpose of reallocation of resources, which can help reduce the severe effects of crises on health resources.

Background : High-volume centers (HVCs) are classically associated with better outcomes. During the coronavirus disease 2019 (COVID-19) pandemic, there has been a decrease in the regular liver transplantation (LT) activity at our center. This study analyzed the effect of the decline in LT on posttransplant patient outcomes at our HVC. Methods : We compared the surgical outcomes of patients who underwent LT during the COVID-19 pandemic lockdown (April 1, 2020 to September 30, 2020) with outcomes in the pre-pandemic calendar year (April 1, 2019 to March 31, 2020). Results : During the 6 months of pandemic lockdown, 60 patients underwent LT (43 adults and 17 children) while 228 patients underwent LT (178 adults and 50 children) during the pre-pandemic calendar year. Patients in the pandemic group had significantly higher model for end-stage liver disease (MELD) scores (24.39±9.55 vs. 21.14±9.17, P=0.034), Child-Turcotte-Pugh scores (11.46±2.32 vs. 10.25±2.24, P=0.03), and incidence of acute-on-chronic liver failure (30.2% vs. 10.2%, P=0.002). Despite performing LT in sicker patients with COVID-19-related challenges, the 30-day (14% vs. 18.5%, P=0.479), 3-month (16.3% vs. 20.2%, P=0.557), and 6-month mortality rates (23.3% vs. 28.7%, P=0.477) were lower, but not statistically significant when compared to the pre-pandemic cohort. Conclusions: During the COVID-19 pandemic lockdown the number of LT procedures performed at our HVC declined by half because prevailing conditions allowed LT in very sick patients only. Despite these changes, outcomes were not inferior during the pandemic period compared to the pre-pandemic calendar year. Greater individualization of patient care contributed to non-inferior outcomes in these sick recipients.

BackgroundDuring the fight against COVID-19, China’s public hospitals played the main role in taking on the most urgent, dangerous and arduous medical treatment and work. Therefore, in order to promote the high-quality development of hospitals, it is necessary to support some potential public hospitals to build and develop a “One Hospital with Multiple Campuses System” (OHMC) based on controlling the size of single hospitals, and to quickly convert their functions in the event of a severe epidemic.MethodsThe Cobb–Douglas production function and log-transformed production function were used to measure the appropriate hospital size for 22 public hospitals in a region of China.ResultsThe eight OHMC hospitals that planned to be build are basically qualified to handle the conditions and potential of multi-districts from the perspective of economy of scale. The OHMC hospitals in operation appear to have weakened incremental scale rewards, because they are in the process of development, but they are still higher than the overall level of single-campus hospitals.ConclusionThe expansion of hospital scale may bring the advantages of group development, but it may also bring about problems including rising hospital cost, increasing management and operation cost, inefficient allocation of medical resources and unbalanced development.

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CONTEXT:Evidence suggests an association between rurality and decreased life expectancy. OBJECTIVE:To determine whether rural hospitals have higher hospital mortality, given that very sick patients may be transferred to regional hospitals. DESIGN:In this ecologic study, we combined Medicare hospital mortality ratings (N = 1267) with US census data, critical access hospital classification, and National Center for Health Statistics urban-rural county classifications. Ratings included mortality for coronary artery bypass grafting, stroke, chronic obstructive pulmonary disease, heart attack, heart failure, and pneumonia across 277 California hospitals between July 2011 and June 2014. We used generalized estimating equations to evaluate the association of urban-rural county classifications on mortality ratings. MAIN OUTCOME MEASURES:Unfavorable Medicare hospital mortality rating "worse than the national rate" compared with "better" or "same." RESULTS:Compared with large central "metro" (metropolitan) counties, hospitals in medium-sized metro counties had 6.4 times the odds of rating "worse than the national rate" for hospital mortality (95% confidence interval = 2.8-14.8, p < 0.001). For hospitals in small metro counties, the odds of having such a rating were 3.7 times greater (95% confidence interval = 0.7-23.4, p = 0.12), although not statistically significant. Few ratings were provided for rural counties, and analysis of rural counties was underpowered. CONCLUSION:Hospitals in medium-sized metro counties are associated with unfavorable Medicare mortality ratings, but current methods to assign mortality ratings may hinder fair comparisons. Patient transfers from rural locations to regional medical centers may contribute to these results, a potential factor that future research should examine.

BACKGROUND:Hospital charges and lengths of stay may be greater when adults with chronic conditions are admitted to children's hospitals. Despite multiple efforts to improve pediatric-adult healthcare transitions, little guidance exists for transitioning inpatient care. OBJECTIVE:This study sought to characterize pediatricadult inpatient care transitions across general pediatric services at US children's hospitals. DESIGN, SETTING AND PARTICIPANTS:National survey of inpatient general pediatric service leaders at US children's hospitals from January 2016 to July 2016. MEASUREMENTS:Questionnaires assessed institutional characteristics, presence of inpatient transition initiatives (having specific process and/or leader), and 22 inpatient transition activities. Scales of highly correlated activities were created using exploratory factor analysis. Logistic regression identified associations between institutional characteristics, transition activities, and presence of an inpatient transition initiative. RESULTS:Ninety-six of 195 children's hospitals responded (49.2% response rate). Transition initiatives were present at 38% of children's hospitals, more often when there were dual-trained internal medicine-pediatrics providers or outpatient transition processes. Specific activities were infrequent and varied widely from 2.1% (systems to track youth in transition) to 40.5% (addressing potential insurance problems). Institutions with initiatives more often consistently performed the majority of activities, including using checklists and creating patient-centered transition care plans. Of remaining activities, half involved transition planning, the essential step between readiness and transfer. CONCLUSIONS:Relatively few inpatient general pediatric services at US children's hospitals have leaders or dedicated processes to shepherd transitions to adultoriented inpatient care. Across institutions, there is a wide variability in performance of activities to facilitate this transition. Feasible process and outcome measures are needed.

Our aim was to examine the numbers of practicing physicians and total numbers of hospital beds in European Organisation for Economic Co-operation and Development countries. Data analyzed were derived from the "Organisation for Economic Co-operation and Development Health Statistics 2020" database between 1980 and 2018. The selected countries were compared according to the type of healthcare system and geographical location by parametric and nonparametric tests. In 1980, Bismarck-type systems showed an average number of physicians of 2.3 persons/1000 population; in Beveridge-type systems, it was 1.7 persons. By 2018, it leveled out reaching 3.9 persons in both healthcare system types. In 1980, average physician number/1000 was 2.5 persons in Eastern Europe; in Western Europe, it was 1.9 persons. By 2018 this proportion changed with Western Europe having the higher number (3.7 persons; 3.9 persons). In 1980, average number of hospital beds/1000 population was 9.6 in Bismarck-type systems whereas in Beveridge-type systems it was 8.8. By 2018, it decreased to 5.6 in Bismarck-type systems (−42%) and to 3.1 in Beveridge-type systems (−65%). In 1980, the average number of hospital beds/1000 population in Eastern Europe was 10.3; in Western Europe, it was 8.5. By 2018, the difference between the 2 regions did not change. Although the number of physicians was 33% higher in 1980 in Eastern Europe than in Western Europe, by 2018 the number of physicians was 5% higher in Western Europe. In general, regardless of the healthcare system and geographical location, the proportion of physicians per 1000 population has improved due to a larger decrease in the number of hospital beds. • Although the number of physicians was 33% higher in 1980 in Eastern Europe than in Western Europe, by 2018 the number of physicians was 5% higher in Western Europe. • COVID-19 pointed to how important it is to improve hospital bed capacity and to develop human resources. [ABSTRACT FROM AUTHOR]

Objective: To characterize the quantity and quality of hospital capacity across the United States. Data Sources: We combine a 2017 near‐census of US hospital inpatient discharges from the Healthcare Cost and Utilization Project (HCUP) with American Hospital Association Survey, Hospital Compare, and American Community Survey data. Study Design: This study produces local hospital capacity quantity and care quality measures by allocating capacity to zip codes using market shares and population totals. Disparities in these measures are examined by race and ethnicity, income, age, and urbanicity. Data Collection/Extraction Methods: All data are derived from pre‐existing sources. All hospitals and zip codes in states, including the District of Columbia, contributing complete data to HCUP in 2017 are included. Principal Findings: Non‐Hispanic Black individuals living in zip codes supplied, on average, 0.11 more beds per 1000 population (SE = 0.01) than places where non‐Hispanic White individuals live. However, the hospitals supplying this capacity have 0.36 fewer staff per bed (SE = 0.03) and perform worse on many care quality measures. Zip codes in the most urban parts of America have the least hospital capacity (2.11 beds per 1000 persons; SEM = 0.01) from across the rural–urban continuum. While more rural areas have markedly higher capacity levels, urban areas have advantages in staff and capital per bed. We do not find systematic differences in care quality between rural and urban areas. Conclusions: This study highlights the importance of lower hospital care quality and resource intensity in driving racial and ethnic, as well as income, disparities in hospital care‐related outcomes. This study also contributes an alternative approach for measuring local hospital capacity that accounts for cross‐hospital service area flows. Adjusting for these flows is necessary to avoid underestimating the supply of capacity in rural areas and overestimating it in places where non‐Hispanic Black individuals tend to live. [ABSTRACT FROM AUTHOR]

Pandemics place a new type of demand from patients affected by the pandemic, imposing significant strain on hospital departments, particularly the intensive care unit. A crucial challenge during pandemics is the imbalance in addressing the needs of both pandemic patients and general patients. Often, the community's focus shifts toward the pandemic patients, causing an imbalance that can result in severe issues. Simultaneously considering both demands, pandemic-related and general healthcare needs, has been largely overlooked. In this article, we propose a bi-objective mathematical model for locating temporary hospitals and allocating patients to existing and temporary hospitals, considering both demand types during pandemics. Hospital departments, such as emergency beds, serve both demand types, but due to infection risks, accommodating a pandemic patient and a general patient in the same department is not feasible. The first objective function is to minimize the bed shortages considering both types of demands, whereas the second objective is cost minimization, which includes the fixed and variable costs of temporary facilities, the penalty cost of changing the allocation of existing facilities (between general and pandemic demand), the cost of adding expandable beds to existing facilities, and the service cost for different services and beds. To show the applicability of the model, a real case study has been conducted on the COVID-19 pandemic in the city of Qom, Iran. Comparing the model results with real data reveals that using the proposed model can increase demand coverage by 16%., (© 2024 Society for Risk Analysis.)

Objective: Analysing and evaluating how efficiently health resources are allocated to county-level Traditional Chinese Medicine (TCM) hospitals in Zhejiang Province, this study aims to provide empirical evidence for improving operational efficiency and optimising resource allocation in these hospitals., Design and Setting: The study employed a three-stage Data Envelopment Analysis (DEA) model to assess efficiency, using data from 68 county-level TCM hospitals. Four input and five output variables related to TCM services were selected for the analysis., Results: The first-stage DEA results indicated that in 2022, the technical efficiency (TE) of TCM hospitals in Zhejiang Province was 0.788, the pure technical efficiency (PTE) was 0.876 and the scale efficiency (SE) was 0.903. The classification of hospitals into four groups based on the bed size showed statistically significant differences in returns to scale (p<0.001). The Stochastic Frontier Analysis regression results were significant at the 1% level across four regressions, showing that environmental variables such as per capita GDP, population density and the number of hospitals impacted efficiency. In the third stage DEA, after adjusting the input variables, the TE, PTE and SE improved to 0.809, 0.833 and 0.917, respectively. The adjusted mean TE rankings by region were West (0.860) > East (0.844) > South (0.805) > North (0.796) > Central (0.731)., Conclusion: There is an imbalance between the inputs and outputs of county-level TCM hospitals. Each region must consider factors such as the local economy, population and medical service levels, along with the specific development characteristics of hospitals, to reasonably determine the scale of county-level TCM hospital construction. Emphasis should be placed on improving hospital management and technical capabilities, coordinating regional development, promoting the rational allocation and efficient use of TCM resources and enhancing the efficiency of resource allocation in county-level TCM hospitals., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

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.

Background: One of the upcoming challenges in hospital accreditation is using the same and similar standards for all types of hospitals in size and type of activity. We aimed to identify the accreditation modules for all types of hospitals in size (small hospitals) and type of activity (special hospitals)., Methods: This research was conducted as a scoping review from Mar to May 2023. "Arsky and O'Malley" six-step protocol was used to conduct this study. "Preferred Reporting Items for Systematic Reviews and Meta-Analyses" (PRISMA) was used to identify, evaluate, and select research articles. The "framework analysis" method was used to analyze the data., Results: Of 14 articles, 64% have been published in peer-reviewed scientific journals. Moreover, 36% of them were accreditation organizations' standards at the national level. The accreditation modules of small hospitals are Responsibilities of Management, Care of Patients, Management of Medication, Patient Safety, Infection Control, Continuous Quality Improvement, Patient Rights and Education, Blood and Blood Products, and Partnering with Consumers. The accreditation modules of special hospitals are Governing Body and Administration, Clinical Management, Prevention and Health, Care and Treatment, Diagnostic Services, Patient Rights, and Quality Improvement., Conclusion: Identifying the main modules of accreditation for small and special hospitals can help policymakers and hospital managers improve the quality and safety of their hospitals by using appropriate standards and help improve the services provided to patients and increase their satisfaction., (Copyright© 2024 Ghaffarian et al. Published by Tehran University of Medical Sciences.)

Background: Shortage of resources, such as hospital beds, needed for health care especially in times of crisis can be a serious challenge for many countries. Currently, there is no suitable model for optimal allocation of beds in different hospital wards. The Data Envelopment Analysis method (DEA) has been used in the present study to examine the evacuation and allocation of hospital beds during the covid-19 pandemic in order to contribute to effective planning for fighting the spread the covid-19 virus.Methods: The present study was conducted in two stages in hospitals affiliated with Urmia University of Medical Sciences (UUMS) in 2021. First, the number of excess beds was determined by calculating the technical efficiency using the DEA method and Deap2.1 software. To reallocate excess beds to covid-19 patients, the types of hospital wards were considered. As a result of this analysis, the inefficient hospitals with excess beds in different wards, which could be used for covid-19 patients with more serious symptoms, were identified.Results: The results of the study show that the average technical efficiency of the studied hospitals was 0.603. These hospitals did not operate efficiently in 2021 and their current output can be produced with less than 61% of the used input. Also, the potential of these hospitals, over a period of 1 year, for the evacuation of beds and reallocation of them to covid-19 patients was calculated to be 1781 beds, 450 of which belonged to general wards and 1331 belonged to specialized wards.Conclusions: The DEA method can be used in the allocation of resources in hospitals. Depending on the type of hospital wards and the health condition of patients, this method can help policy-makers identify hospitals with the best potential but less emergency services for the purpose of reallocation of resources, which can help reduce the severe effects of crises on health resources. [ABSTRACT FROM AUTHOR]

Abstract Background The coronavirus outbreak (COVID-19) in China has influenced every aspect of life worldwide. Given the unique characteristics of the dental setting, the risk of cross-infection between dental practitioners and patients is high in the absence of adequate protective measures, and dentists may develop severe anxiety in relation to the current pandemic. The limited provision of services and widespread closure of dental practices have raised concerns among dental professionals about the financial impact. The present study assessed the frequency of dental practice closure during the pandemic’s first wave in several countries and whether closures and their associated factors differ between the private and non-private sectors. Methods An electronic cross-sectional survey questionnaire was sent to dentists in several countries, from April to May 2020. The survey assessed professional, practice related and country-level structural factors elucidating the reason for practice closure. Multilevel logistic regression was used to assess the association between practice closure and these factors, and differences were evaluated by sector type. Results Dentists from 29 countries (n = 3243) participated in this study. Most of the participants (75.9%) reported practice closure with significantly higher percentage in the private sector than the non-private sector. Greater pandemic-related fears were associated with a significantly higher likelihood of practice closure in the private (odds ratio [OR] = 1.54, 95% confidence interval [CI] 1.24, 1.92) and non-private (OR = 1.38, 95% CI 1.04, 1.82) sectors. Dentists in non-private rural areas (OR = 0.58, 95% CI 0.42, 0.81), and those in hospitals (overall OR = 0.60, 95% CI 0.36, 0.99) reported a low likelihood of closure. A high likelihood of closure was reported by dentists in the academia (OR = 2.13, 95% CI 1.23, 3.71). More hospital beds at the country-level were associated with a lower likelihood of closure in the non-private sector (OR = 0.65, 95% CI 0.46, 0.91). Private- sector dentists in high- income countries (HICs) reported fewer closures than those in non-HICs (OR = 0.55, 95% CI 0.15, 1.93). Conclusions Most dentists reported practice closure because of COVID-19, and greater impacts were reported in the private sector than in the non-private sector. Closure was associated with professional, practice, and country-levels factors.

ABSTRACT BACKGROUND: The fragility of healthcare systems worldwide had not been exposed by any pandemic until now. The lack of integrated methods for bed capacity planning compromises the effectiveness of public and private hospitals’ services. OBJECTIVES: To estimate the impact of the COVID-19 pandemic on the provision of intensive care unit and clinical beds for Brazilian states, using an integrated model. DESIGN AND SETTING: Experimental study applying healthcare informatics to data on COVID-19 cases from the official electronic platform of the Brazilian Ministry of Health. METHODS: A predictive model based on the historical records of Brazilian states was developed to estimate the need for hospital beds during the COVID-19 pandemic. RESULTS: The proposed model projected in advance that there was a lack of 22,771 hospital beds for Brazilian states, of which 38.95% were ICU beds, and 61.05% were clinical beds. CONCLUSIONS: The proposed approach provides valuable information to help hospital managers anticipate actions for improving healthcare system capacity.

Background: Stanford Type A Aortic Dissection (TAAD) is associated with high in-hospital mortality and the need for immediate surgical intervention. Larger hospital size may be associated with better patient care and surgical outcomes. This study aimed to examine the effect of hospital size on TAAD outcomes., Method: Patients who underwent TAAD repair were identified in National Inpatient Sample (NIS) from Q4 2015-2020. NIS stratifies hospital size into small, medium, and large based on the number of hospital beds, geographical location, and the teaching status of the hospitals. Patients admitted to small/medium and large hospitals were stratified into two cohorts. Multivariable logistic regressions were performed to compare in-hospital outcomes, adjusted for demographics, comorbidity, primary payer status, and hospital characteristics including procedural volume., Results: There were 1106 and 3752 TAAD admitted to small/medium and large hospitals, respectively. Among patients admitted to small/medium hospitals, there was higher mortality (17.27% vs 14.37%, aOR = 1.32, P < 0.01), but shorter length of stay ( P < 0.01) and lower cost ( P = 0.03) compared to larger hospitals. There was no difference in morbidities., Conclusions: Marked higher mortality is associated with admission to smaller hospitals among patients with TAAD, which may in turn decrease the average hospital stay and cost. Given that a significant percentage of patients are already being transferred out of the initial hospital and small/medium hospital is associated with higher mortality, centralization of care in centers of excellence may decrease the high mortality associated with TAAD., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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

This article examines territorial inequalities in health resources in relation to the COVID-19 pandemic. The typology built differentiates between the following four categories: (1) counties that succeeded in ensuring good access to both COVID-19 patients and patients with chronic illness; (2) counties with good access to COVID-19 patients but rather low access to patients with chronic illness; (3) counties with mixed situations and (4) poor access for COVID-19 patients and patients with chronic illness. Counties that succeeded in providing good access for both categories of patients are to a significantly greater extent from counties with high GDP per capita. [ABSTRACT FROM AUTHOR]

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., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)

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., Competing Interests: Die Autorinnen/Autoren geben an, dass kein Interessenkonflikt besteht., (Thieme. All rights reserved.)

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., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2024. Published by Elsevier Inc.)

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., (Copyright © 2024 by the American Academy of Pediatrics.)

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., Competing Interests: Declaration of competing interest We declare no competing interests., (Copyright © 2024. Published by Elsevier Inc.)

Before coronavirus disease 2019 (COVID-19), few hospitals had fully tested emergency surge plans. Uncertainty in the timing and degree of surge complicates planning efforts, putting hospitals at risk of being overwhelmed. Many lack access to hospital-specific, data-driven projections of future patient demand to guide operational planning. Our hospital experienced one of the largest surges in New England. We developed statistical models to project hospitalizations during the first wave of the pandemic. We describe how we used these models to meet key planning objectives. To build the models successfully, we emphasize the criticality of having a team that combines data scientists with frontline operational and clinical leadership. While modeling was a cornerstone of our response, models currently available to most hospitals are built outside of their institution and are difficult to translate to their environment for operational planning. Creating data-driven, hospital-specific, and operationally relevant surge targets and activation triggers should be a major objective of all health systems. [ABSTRACT FROM AUTHOR]

Copyright of Croatian Journal of Infection / Infektoloski Glasnik is the property of Croatian Society for Infectious Diseases and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives: The aim of this study was to quantify immediate bed availability (IBA) in a United States children's hospital and treatment needs of hospitalized patients whose needs could be met outside a traditional hospital setting. Methods: Using a novel tool to capture census, scheduled discharges, and resource needs for hospitalized patients, we surveyed our hospital's 5 non-neonatal inpatient pediatric units on 4 d over 1 y. Results: Median ward occupancy was 81% (range, 58-79), median intensive care unit occupancy was 80% (range, 7-19), and median IBA was 42% (range, 34-59). A median of 14 patients per day (13% of total capacity) had treatment needs that could be met by providing limited support in a nontraditional setting; the most common reason for requiring ongoing hospitalization in this group of patients was a safe discharge plan. Conclusions: Our median IBA of 42% exceeds federal recommendations, but varies widely between days surveyed. Even on days when IBA percentage is high, our total number of available beds is unlikely to meet pediatric population needs in a large-scale public health emergency. [ABSTRACT FROM AUTHOR]

BackgroundComputerized provider order entry (CPOE) with clinical decision support is a basic criterion for hospitals' meaningful use of electronic health record systems. A study was conducted to evaluate from the societal perspective the cost-utility of implementing CPOE in acute care hospitals in the United States.MethodsA decision-analytical model compared CPOE with paper ordering among patients admitted to acute care hospitals with >25 beds. Parameters included start-up and maintenance costs, as well as costs for provider time use, medication and laboratory test ordering, and preventable adverse drug events. Probabilistic analyses produced incremental costs, effectiveness, and cost-effectiveness ratios for hospitals in four bed-size categories (25-72, 72-141, 141-267, 267-2,249).ResultsRelative to paper ordering and using typical estimates of implementation costs, CPOE had, on average, >99% probability of yielding savings to society and improving health. Per hospital in each size category, mean life-time savings -in millions-were $11.6 (standard deviation, $9.30), $34.4 ($21.2), $71.8 ($43.8), and $170 ($119) (2012 dollars), respectively, and quality-adjusted life-years (QALYs) gained were 19.9 (16.9), 53.7 (38.7), 109 (79.6), and 249 (205). Incremental effectiveness and costs were less favorable in certain circumstances, such as high implementation costs. Nationwide, anticipated increases in CPOE implementation from 2009 through 2015 could save $133 billion and 201,000 QALYs.ConclusionsIn addition to improving health, implementing CPOE with clinical decision support could yield substantial long-term savings to society in the United States, although results for individual hospitals are likely to vary.

Across 366 California hospitals, we identified hospital-level characteristics predicting increased hospital-associated Clostridium difficile infection (HA-CDI) rates including more licensed beds, teaching and long-term acute care (LTAC) hospitals, and polymerase chain reaction testing. Adjustment for these characteristics impacted rankings in 24% of teaching hospitals, 13% of community hospitals, and 11% of LTAC hospitals.

PurposeThe purpose of the study is to compare demographics, intensive care unit (ICU) admission characteristics, and ICU outcomes among adults with childhood-onset chronic conditions (COCCs) admitted to US pediatric and adult ICUs.Materials and methodsRetrospective cross-sectional analyses of 6088 adults aged 19 to 40 years admitted in 2008 to 70 pediatric ICUs that participated in the Virtual Pediatric Intensive Care Unit Performance Systems and 50 adult ICUs that participated in Project IMPACT.ResultsChildhood-onset chronic conditions were present in 53% of young adults admitted to pediatric units, compared with 9% of those in adult units. The most common COCC in both groups were congenital cardiac abnormalities, cerebral palsy, and chromosomal abnormalities. Adults with COCC admitted to pediatric units were significantly more likely to be younger, have lower functional status, and be nontrauma patients than those in adult units. The median ICU length of stay was 2 days, and the intensive care unit mortality rate was 5% for all COCC patients with no statistical difference between pediatric or adult units.ConclusionsThere are marked differences in characteristics between young adults with COCC admitted to pediatric ICUs and adult ICUs. Barriers to accommodating these young adults may be reasons why many such adults have not transitioned from pediatric to adult critical care.

ObjectivesEmergency department (ED) crowding is an increasing problem associated with adverse patient outcomes. ED expansion is one method advocated to reduce ED crowding. The objective of this analysis was to determine the effect of ED expansion on measures of ED crowding.MethodsThis was a retrospective study using administrative data from two 11-month periods before and after the expansion of an ED from 33 to 53 adult beds in an academic medical center. ED volume, staffing, and hospital admission and occupancy data were obtained either from the electronic health record (EHR) or from administrative records. The primary outcome was the rate of patients who left without being treated (LWBT), and the secondary outcome was total ED boarding time for admitted patients. A multivariable robust linear regression model was used to determine whether ED expansion was associated with the outcome measures.ResultsThe mean (±SD) daily adult volume was 128 (±14) patients before expansion and 145 (±17) patients after. The percentage of patients who LWBT was unchanged: 9.0% before expansion versus 8.3% after expansion (difference = 0.6%, 95% confidence interval [CI] = -0.16% to 1.4%). Total ED boarding time increased from 160 to 180 hours/day (difference = 20 hours, 95% CI = 8 to 32 hours). After daily ED volume, low-acuity area volume, daily wait time, daily boarding hours, and nurse staffing were adjusted for, the percentage of patients who LWBT was not independently associated with ED expansion (p = 0.053). After ED admissions, ED intensive care unit (ICU) admissions, elective surgical admissions, hospital occupancy rate, ICU occupancy rate, and number of operational ICU beds were adjusted for, the increase in ED boarding hours was independently associated with the ED expansion (p = 0.005).ConclusionsAn increase in ED bed capacity was associated with no significant change in the percentage of patients who LWBT, but had an unintended consequence of an increase in ED boarding hours. ED expansion alone does not appear to be an adequate solution to ED crowding.

Background: The coronavirus outbreak (COVID-19) in China has influenced every aspect of life worldwide. Given the unique characteristics of the dental setting, the risk of cross-infection between dental practitioners and patients is high in the absence of adequate protective measures, and dentists may develop severe anxiety in relation to the current pandemic. The limited provision of services and widespread closure of dental practices have raised concerns among dental professionals about the financial impact. The present study assessed the frequency of dental practice closure during the pandemic's first wave in several countries and whether closures and their associated factors differ between the private and non-private sectors. Methods: An electronic cross-sectional survey questionnaire was sent to dentists in several countries, from April to May 2020. The survey assessed professional, practice related and country-level structural factors elucidating the reason for practice closure. Multilevel logistic regression was used to assess the association between practice closure and these factors, and differences were evaluated by sector type. Results: Dentists from 29 countries (n = 3243) participated in this study. Most of the participants (75.9%) reported practice closure with significantly higher percentage in the private sector than the non-private sector. Greater pandemic-related fears were associated with a significantly higher likelihood of practice closure in the private (odds ratio [OR] = 1.54, 95% confidence interval [CI] 1.24, 1.92) and non-private (OR = 1.38, 95% CI 1.04, 1.82) sectors. Dentists in non-private rural areas (OR = 0.58, 95% CI 0.42, 0.81), and those in hospitals (overall OR = 0.60, 95% CI 0.36, 0.99) reported a low likelihood of closure. A high likelihood of closure was reported by dentists in the academia (OR = 2.13, 95% CI 1.23, 3.71). More hospital beds at the country-level were associated with a lower likelihood of closure in the non-private sector (OR = 0.65, 95% CI 0.46, 0.91). Private- sector dentists in high- income countries (HICs) reported fewer closures than those in non-HICs (OR = 0.55, 95% CI 0.15, 1.93). Conclusions: Most dentists reported practice closure because of COVID-19, and greater impacts were reported in the private sector than in the non-private sector. Closure was associated with professional, practice, and country-levels factors. [ABSTRACT FROM AUTHOR]

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Background Dexamethasone and remdesivir have the potential to reduce coronavirus disease 2019 (COVID)–related mortality or recovery time, but their cost-effectiveness in countries with limited intensive care resources is unknown. Methods We projected intensive care unit (ICU) needs and capacity from August 2020 to January 2021 using the South African National COVID-19 Epi Model. We assessed the cost-effectiveness of (1) administration of dexamethasone to ventilated patients and remdesivir to nonventilated patients, (2) dexamethasone alone to both nonventilated and ventilated patients, (3) remdesivir to nonventilated patients only, and (4) dexamethasone to ventilated patients only, all relative to a scenario of standard care. We estimated costs from the health care system perspective in 2020 US dollars, deaths averted, and the incremental cost-effectiveness ratios of each scenario. Results Remdesivir for nonventilated patients and dexamethasone for ventilated patients was estimated to result in 408 (uncertainty range, 229–1891) deaths averted (assuming no efficacy [uncertainty range, 0%–70%] of remdesivir) compared with standard care and to save $15 million. This result was driven by the efficacy of dexamethasone and the reduction of ICU-time required for patients treated with remdesivir. The scenario of dexamethasone alone for nonventilated and ventilated patients requires an additional $159 000 and averts 689 [uncertainty range, 330–1118] deaths, resulting in $231 per death averted, relative to standard care. Conclusions The use of remdesivir for nonventilated patients and dexamethasone for ventilated patients is likely to be cost-saving compared with standard care by reducing ICU days. Further efforts to improve recovery time with remdesivir and dexamethasone in ICUs could save lives and costs in South Africa. [ABSTRACT FROM AUTHOR]

ObjectiveAs healthcare systems continue to expand and interconnect with each other through patient sharing, administrators, policy makers, infection control specialists, and other decision makers may have to take account of the entire healthcare 'ecosystem' in infection control.Materials and methodsWe developed a software tool, the Regional Healthcare Ecosystem Analyst (RHEA), that can accept user-inputted data to rapidly create a detailed agent-based simulation model (ABM) of the healthcare ecosystem (ie, all healthcare facilities, their adjoining community, and patient flow among the facilities) of any region to better understand the spread and control of infectious diseases.ResultsTo demonstrate RHEA's capabilities, we fed extensive data from Orange County, California, USA, into RHEA to create an ABM of a healthcare ecosystem and simulate the spread and control of methicillin-resistant Staphylococcus aureus. Various experiments explored the effects of changing different parameters (eg, degree of transmission, length of stay, and bed capacity).DiscussionOur model emphasizes how individual healthcare facilities are components of integrated and dynamic networks connected via patient movement and how occurrences in one healthcare facility may affect many other healthcare facilities.ConclusionsA decision maker can utilize RHEA to generate a detailed ABM of any healthcare system of interest, which in turn can serve as a virtual laboratory to test different policies and interventions.

ObjectiveWe sought to identify hospital characteristics associated with community-associated methicillin-resistant Staphylococcus aureus (CA-MRSA) carriage among inpatients.Design Prospective cohort study.Setting Orange County, California.Participants Thirty hospitals in a single county.Methods We collected clinical MRSA isolates from inpatients in 30 of 31 hospitals in Orange County, California, from October 2008 through April 2010. We characterized isolates by spa typing to identify CA-MRSA strains. Using California's mandatory hospitalization data set, we identified hospital-level predictors of CA-MRSA isolation.Results CA-MRSA strains represented 1,033 (46%) of 2,246 of MRSA isolates. By hospital, the median percentage of CA-MRSA isolates was 46% (range, 14%-81%). In multivariate models, CA-MRSA isolation was associated with smaller hospitals (odds ratio [OR], 0.97, or 3% decreased odds of CA-MRSA isolation per 1,000 annual admissions; P < .001, hospitals with more Medicaid-insured patients (OR, 1.2; P = .002), and hospitals with more patients with low comorbidity scores (OR, 1.3; P < .001). Results were similar when restricted to isolates from patients with hospital-onset infection.Conclusions Among 30 hospitals, CA-MRSA comprised nearly half of MRSA isolates. There was substantial variability in CA-MRSA penetration across hospitals, with more CA-MRSA in smaller hospitals with healthier but socially disadvantaged patient populations. Additional research is needed to determine whether infection control strategies can be successful in targeting CA-MRSA influx.