A National Look at Hospital Bed Tower Design.

This study evaluates 171 hospital bed tower designs from the past decade. The Floor-building gross square feet (BGSF)/Bed, patient care area, ratio between them, and the bed count per unit were analyzed. The findings suggest that the average patient care area has decreased 5%-10% to a 305 departmental gross square feet (DGSF)/Bed average. The patient care area, support, circulation, and area grossing on floor were found to average 908 Floor-BGSF/Bed, and were impacted by the total beds/unit. It was determined that larger bed count per unit designs with 32-36 beds/unit average 21.9% less Floor-BGSF/Bed than designs with 24 beds/unit. The research evaluates design solutions impacted by a shifting environment of regulatory change and escalating costs. The hospital bed towers represent new facilities, horizontal/vertical expansions, and 25+ design teams. Design and/or construction took place during a 10-year period (2008-2018). The acute patient unit designs were reviewed and electronically quantified. The area measurement methodology aligns with the guidelines set forth in the "Area Calculation Method for Health Care" guidelines. Each project team was faced with a unique but similar set of circumstances. The balance between core values, guiding principles, budget, and quality of care was always present and included a diverse combination of owners, designers, construction delivery methods, profit models, and clinical approaches. In today's world, common solutions are grounded in providing the best value. Project teams face a number of challenges during design. The lack of information should never be one.

Artificial neural networks for short-term forecasting of cases, deaths, and hospital beds occupancy in the COVID-19 pandemic at the Brazilian Amazon.

The first case of the novel coronavirus in Brazil was notified on February 26, 2020. After 21 days, the first case was reported in the second largest State of the Brazilian Amazon. The State of Pará presented difficulties in combating the pandemic, ranging from underreporting and a low number of tests to a large territorial distance between cities with installed hospital capacity. Due to these factors, mathematical data-driven short-term forecasting models can be a promising initiative to assist government officials in more agile and reliable actions. This study presents an approach based on artificial neural networks for the daily and cumulative forecasts of cases and deaths caused by COVID-19, and the forecast of demand for hospital beds. Six scenarios with different periods were used to identify the quality of the generated forecasting and the period in which they start to deteriorate. Results indicated that the computational model adapted capably to the training period and was able to make consistent short-term forecasts, especially for the cumulative variables and for demand hospital beds.

Impact of Hospital Bed Shortages on the Containment of COVID-19 in Wuhan.

The global outbreak of COVID-19 has caused worrying concern amongst the public and health authorities. The first and foremost problem that many countries face during the outbreak is a shortage of medical resources. In order to investigate the impact of a shortage of hospital beds on the COVID-19 outbreak, we formulated a piecewise smooth model for describing the limitation of hospital beds. We parameterized the model while using data on the cumulative numbers of confirmed cases, recovered cases, and deaths in Wuhan city from 10 January to 12 April 2020. The results showed that, even with strong prevention and control measures in Wuhan, slowing down the supply rate, reducing the maximum capacity, and delaying the supply time of hospital beds all aggravated the outbreak severity by magnifying the cumulative numbers of confirmed cases and deaths, lengthening the end time of the pandemic, enlarging the value of the effective reproduction number during the outbreak, and postponing the time when the threshold value was reduced to 1. Our results demonstrated that establishment of the Huoshenshan, Leishenshan, and Fangcang shelter hospitals avoided 22,786 people from being infected and saved 6524 lives. Furthermore, the intervention of supplying hospital beds avoided infections in 362,360 people and saved the lives of 274,591 persons. This confirmed that the quick establishment of the Huoshenshan, Leishenshan Hospitals, and Fangcang shelter hospitals, and the designation of other hospitals for COVID-19 patients played important roles in containing the outbreak in Wuhan.

Forecast UTI: application for predicting intensive care unit beds in the context of the COVID-19 pandemic. / Forecast UTI: aplicativo para previsão de leitos de unidades de terapia intensiva no contexto da pandemia de COVID-19.

In view of the need to manage and forecast the number of Intensive Care Unit (ICU) beds for critically ill COVID-19 patients, the Forecast UTI open access application was developed to enable hospital indicator monitoring based on past health data and the temporal dynamics of the Coronavirus epidemic. Forecast UTI also enables short-term forecasts of the number of beds occupied daily by COVID-19 patients and possible care scenarios to be established. This article presents the functions, mode of access and examples of uses of Forecast UTI, a computational tool intended to assist managers of public and private hospitals within the Brazilian National Health System by supporting quick, strategic and efficient decision-making.

Frente à necessidade de gerenciamento e previsão do número de leitos de unidades de terapia intensiva (UTIs) para pacientes graves de COVID-19, foi desenvolvido o Forecast UTI, um aplicativo de livre acesso, que permite o monitoramento de indicadores hospitalares com base em dados históricos do serviço de saúde e na dinâmica temporal da epidemia por coronavírus. O Forecast UTI também possibilita realizar previsões de curto prazo do número de leitos ocupados pela doença diariamente, e estabelecer possíveis cenários de atendimento. Este artigo apresenta as funções, modo de acesso e exemplos de uso do Forecast UTI, uma ferramenta computacional destinada a auxiliar gestores de hospitais da rede pública e privada do Sistema Único de Saúde (SUS) no subsídio à tomada de decisão, de forma rápida, estratégica e eficiente.

En vista de la necesidad de administrar y prever el número de camas en la Unidad de Cuidados Intensivos para pacientes graves de COVID-19, se desarrolló Forecast UTI: una aplicación de acceso abierto que permite el monitoreo de indicadores hospitalarios basados en datos históricos del servicio salud y la dinámica temporal de esta epidemia por coronavirus También es posible hacer pronósticos a corto plazo del número de camas ocupadas diariamente por la enfermedad y establecer posibles escenarios de atención. Este artículo presenta las funciones, el modo de acceso y ejemplos de uso de Forecast UTI, una herramienta computacional capaz de ayudar a los gestores de hospitales públicos y privados en el Sistema Único de Salud, ya que apoyan la toma de decisiones de manera rápida, estratégica y eficiente.

A DEA-Based Complexity of Needs Approach for Hospital Beds Evacuation during the COVID-19 Outbreak.

Data envelopment analysis (DEA) is a powerful nonparametric engineering tool for estimating technical efficiency and production capacity of service units. Assuming an equally proportional change in the output/input ratio, we can estimate how many additional medical resource health service units would be required if the number of hospitalizations was expected to increase during an epidemic outbreak. This assessment proposes a two-step methodology for hospital beds vacancy and reallocation during the COVID-19 pandemic. The framework determines the production capacity of hospitals through data envelopment analysis and incorporates the complexity of needs in two categories for the reallocation of beds throughout the medical specialties. As a result, we have a set of inefficient healthcare units presenting less complex bed slacks to be reduced, that is, to be allocated for patients presenting with more severe conditions. The first results in this work, in collaboration with state and municipal administrations in Brazil, report 3772 beds feasible to be evacuated by 64% of the analyzed health units, of which more than 82% are moderate complexity evacuations. The proposed assessment and methodology can provide a direction for governments and policymakers to develop strategies based on a robust quantitative production capacity measure.

An evaluation of cleaning practices at a teaching hospital.

BACKGROUND: The COVID-19 outbreak has highlighted the role of hospital-acquired infections in spreading epidemics. Adequately cleaning surfaces in patient rooms is an essential part of this fight to reduce the spread. Traditional audits, however, are insufficient. This study assesses surface cleaning practices using ultravoilet (UV) marker technology and the extent to which this technology can help improve cleaning audits and practices. METHODS: One hundred and forty-four audits (1,235 surfaces) were retrieved. UV-marker cleaning audits conducted at a major teaching hospital in 2018 after implementing a new cleaning protocol. In addition, semi-structured interviews were conducted with cleaning staff and supervisors. RESULTS: On average, 63% of surfaces were appropriately cleaned. Toilet handles (80%) and toilet seats underside (83%) scored highest while main room sink fixtures (54%), light switch (55%), and bedrails (56%) scored lowest. Training, staffing and time constraints may play a role in low cleaning rates. DISCUSSION: The high-touch patient surfaces in the bedroom remain neglected and a potential source of infections. UV marker audits provided an objective measure of cleaning practices that managers and staff were unaware of. CONCLUSIONS: UV-markers audits can play a key role in revealing deficiencies in cleaning practices and help in raising awareness of these deficiencies and improving cleaning practices.

Ethical aspects of the COVID-19 crisis: How to deal with an overwhelming shortage of acute beds.

The current outbreak of SARS-CoV-2 has and continues to put huge pressure on intensive care units (ICUs) worldwide. Many patients with COVID-19 require some form of respiratory support and often have prolonged ICU stays, which results in a critical shortage of ICU beds. It is therefore not always physically possible to treat all the patients who require intensive care, raising major ethical dilemmas related to which patients should benefit from the limited resources and which should not. Here we consider some of the approaches to the acute shortages seen during this and other epidemics, including some guidelines for triaging ICU admissions and treatments.