Predicting Hospital Resource Use During COVID-19 Surges: A Simple but Flexible Discretely Integrated Condition Event Simulation of Individual Patient-Hospital Trajectories.

OBJECTIVES: To assist with planning hospital resources, including critical care (CC) beds, for managing patients with COVID-19. METHODS: An individual simulation was implemented in Microsoft Excel using a discretely integrated condition event simulation. Expected daily cases presented to the emergency department were modeled in terms of transitions to and from ward and CC and to discharge or death. The duration of stay in each location was selected from trajectory-specific distributions. Daily ward and CC bed occupancy and the number of discharges according to care needs were forecast for the period of interest. Face validity was ascertained by local experts and, for the case study, by comparing forecasts with actual data. RESULTS: To illustrate the use of the model, a case study was developed for Guy's and St Thomas' Trust. They provided inputs for January 2020 to early April 2020, and local observed case numbers were fit to provide estimates of emergency department arrivals. A peak demand of 467 ward and 135 CC beds was forecast, with diminishing numbers through July. The model tended to predict higher occupancy in Level 1 than what was eventually observed, but the timing of peaks was quite close, especially for CC, where the model predicted at least 120 beds would be occupied from April 9, 2020, to April 17, 2020, compared with April 7, 2020, to April 19, 2020, in reality. The care needs on discharge varied greatly from day to day. CONCLUSIONS: The DICE simulation of hospital trajectories of patients with COVID-19 provides forecasts of resources needed with only a few local inputs. This should help planners understand their expected resource needs.

Más que internistas / More than just internists

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El análisis de la capacidad de respuesta sanitaria como elemento clave en la planificación ante emergencias epidémicas / Analyzing the health system's capacity to respond to epidemics: a key element in planningfor emergencies

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Surge capacity of intensive care units in case of acute increase in demand caused by COVID-19 in Australia.

OBJECTIVES: To assess the capacity of intensive care units (ICUs) in Australia to respond to the expected increase in demand associated with COVID-19. DESIGN: Analysis of Australian and New Zealand Intensive Care Society (ANZICS) registry data, supplemented by an ICU surge capability survey and veterinary facilities survey (both March 2020). SETTINGS: All Australian ICUs and veterinary facilities. MAIN OUTCOME MEASURES: Baseline numbers of ICU beds, ventilators, dialysis machines, extracorporeal membrane oxygenation machines, intravenous infusion pumps, and staff (senior medical staff, registered nurses); incremental capability to increase capacity (surge) by increasing ICU bed numbers; ventilator-to-bed ratios; number of ventilators in veterinary facilities. RESULTS: The 191 ICUs in Australia provide 2378 intensive care beds during baseline activity (9.3 ICU beds per 100 000 population). Of the 175 ICUs that responded to the surge survey (with 2228 intensive care beds), a maximal surge would add an additional 4258 intensive care beds (191% increase) and 2631 invasive ventilators (120% increase). This surge would require additional staffing of as many as 4092 senior doctors (245% increase over baseline) and 42 720 registered ICU nurses (269% increase over baseline). An additional 188 ventilators are available in veterinary facilities, including 179 human model ventilators. CONCLUSIONS: The directors of Australian ICUs report that intensive care bed capacity could be near tripled in response to the expected increase in demand caused by COVID-19. But maximal surge in bed numbers could be hampered by a shortfall in invasive ventilators and would also require a large increase in clinician and nursing staff numbers.

Translating COVID-19 Pandemic Surge Theory to Practice in the Emergency Department: How to Expand Structure.

Multiple professional societies, nongovernment and government agencies have studied the science of sudden onset disaster mass casualty incidents to create and promote surge response guidelines. The COVID-19 pandemic has presented the health-care system with challenges that have limited science to guide the staff, stuff, and structure surge response.This study reviewed the available surge science literature specifically to guide an emergency department's surge structural response using a translational science approach to answer the question: How does the concept of sudden onset mass casualty incident surge capability apply to the process to expand COVID-19 pandemic surge structure response?The available surge structural science literature was reviewed to determine the application to a pandemic response. The on-line ahead of print and print COVID-19 scientific publications, as well as gray literature were studied to learn the best available COVID-19 surge structural response science. A checklist was created to guide the emergency department team's COVID-19 surge structural response.

Modelo de evaluación del plan de respuesta frente a la pandemia de COVID-19 en un hospital de tercer nivel / Assessment model for evaluating the preparedness plan for COVID-19 in a tertiary care hospital

ANTECEDENTES Y OBJETIVO: Durante la primera onda epidémica del SARS-CoV-2, los hospitales han soportado una importante presión asistencial. Este escenario de incertidumbre, baja evidencia científica y medios insuficientes ha generado una importante variabilidad de la práctica entre diferentes centros sanitarios. En este contexto, planteamos desarrollar un modelo basado en estándares para la evaluación del sistema de preparación y respuesta frente a la COVID-19 en un hospital terciario. MATERIALES Y MÉTODOS: El estudio se llevó a cabo en el Hospital Universitario Vall d'Hebron de Barcelona en dos fases: 1) desarrollo de modelo de estándares mediante revisión narrativa de la literatura, análisis de planes y protocolos del hospital, método Delphi por profesionales expertos y plan de actualización y 2) validación de aplicabilidad y utilidad del modelo mediante autoevaluación y auditoría. RESULTADOS: El modelo consta de 208 estándares distribuidos en nueve criterios: liderazgo y estrategia; prevención y control de la infección; gestión de profesionales y competencias; áreas públicas comunes; áreas asistenciales; áreas de apoyo asistencial; logística, tecnología y obras; comunicación y atención al paciente; sistemas de información e investigación. La evaluación alcanza un 85,2% de cumplimiento, y se identifican 42 áreas de mejora y 96 buenas prácticas. CONCLUSIONES: La implementación de un modelo basado en estándares es útil para identificar áreas de mejora y buenas prácticas en los planes de preparación y respuesta frente a la COVID-19 en un hospital. En el actual contexto, proponemos la conveniencia de adaptar esta metodología a otros ámbitos de atención sanitaria no hospitalaria o de salud pública

BACKGROUND AND PURPOSE: During the first wave of the epidemic caused by SARS-CoV-2, hospitals have come under significant pressure. This scenario of uncertainty, low scientific evidence, and insufficient resources, has generated significant variability in practice between different health organisations. In this context, it is proposed to develop a standards-based model for the evaluation of the preparedness and response system against COVID-19 in a tertiary hospital. MATERIALS AND METHODS: The study, carried out at the University Hospital of Vall d'Hebron in Barcelona (Spain), was designed in two phases: 1) development of the standards-based model, by means of a narrative review of the literature, analysis of plans and protocols implemented in the hospital, a review process by expert professionals from the centre, and plan of action, and 2) validation of usability and usefulness of the model through self-assessment and hospital audit. RESULTS: The model contains 208 standards distributed into nine criteria: leadership and strategy; prevention and infection control; management of professionals and skills; public areas; healthcare areas; areas of support for diagnosis and treatment; logistics, technology and works; communication and patient care; and information and research systems. The evaluation achieved 85.2% compliance, with 42 areas for improvement and 96 good practices identified. CONCLUSIONS: Implementing a standards-based model is a useful tool to identify areas for improvement and good practices in COVID-19 preparedness and response plans in a hospital. In the current context, it is recommended to repeat this methodology in other non-hospital and public health settings

Strategies for Improved Hospital Response to Mass Casualty Incidents.

Mass casualty incidents are a concern in many urban areas. A community's ability to cope with such events depends on the capacities and capabilities of its hospitals for handling a sudden surge in demand of patients with resource-intensive and specialized medical needs. This paper uses a whole-hospital simulation model to replicate medical staff, resources, and space for the purpose of investigating hospital responsiveness to mass casualty incidents. It provides details of probable demand patterns of different mass casualty incident types in terms of patient categories and arrival patterns, and accounts for related transient system behavior over the response period. Using the layout of a typical urban hospital, it investigates a hospital's capacity and capability to handle mass casualty incidents of various sizes with various characteristics, and assesses the effectiveness of designed demand management and capacity-expansion strategies. Average performance improvements gained through capacity-expansion strategies are quantified and best response actions are identified. Capacity-expansion strategies were found to have superadditive benefits when combined. In fact, an acceptable service level could be achieved by implementing only 2 to 3 of the 9 studied enhancement strategies. (Disaster Med Public Health Preparedness. 2018;12:778-790).

Analysis of the Three-Tiered Treatment Model for Emergency Medical Rescue Services After the Lushan Earthquake.

OBJECTIVE: To explore the 3-tiered treatment model for medical treatment after an earthquake. METHODS: Based on the practices of the national emergency medical rescue services in the Lushan earthquake zone, the 3-tiered treatment classification approach was retrospectively reviewed. RESULTS: Medical rescue teams assembled and reported quickly to the disaster areas after the earthquake. The number of injured people had reached 25,176 as of April 30; of these, 18,611 people were treated as outpatients, 6565 were hospitalized, and 977 were seriously or severely injured. CONCLUSIONS: The 3-tiered treatment model was the main approach used by rescue services after the Lushan earthquake. Primary and secondary treatments were of the highest importance and formed the basis of the Lushan model of earthquake rescue and treatment. (Disaster Med Public Health Preparedness. 2018; 12: 301-304).

Utilizing a Pediatric Disaster Coalition Model to Increase Pediatric Critical Care Surge Capacity in New York City.

A mass casualty event can result in an overwhelming number of critically injured pediatric victims that exceeds the available capacity of pediatric critical care (PCC) units, both locally and regionally. To address these gaps, the New York City (NYC) Pediatric Disaster Coalition (PDC) was established. The PDC includes experts in emergency preparedness, critical care, surgery, and emergency medicine from 18 of 25 major NYC PCC-capable hospitals. A PCC surge committee created recommendations for making additional PCC beds available with an emphasis on space, staff, stuff (equipment), and systems. The PDC assisted 15 hospitals in creating PCC surge plans by utilizing template plans and site visits. These plans created an additional 153 potential PCC surge beds. Seven hospitals tested their plans through drills. The purpose of this article was to demonstrate the need for planning for disasters involving children and to provide a stepwise, replicable model for establishing a PDC, with one of its primary goals focused on facilitating PCC surge planning. The process we describe for developing a PDC can be replicated to communities of any size, setting, or location. We offer our model as an example for other cities. (Disaster Med Public Health Preparedness. 2017;11:473-478).

Adaptación y validación de la Escala de gradación del dolor crónico al español / Adaptation and validation of the Spanish version of the graded chronic pain scale

Objetivo. Adaptar la Escala de gradación del dolor crónico en pacientes de Atención Primaria en España y evaluar sus propiedades psicométricas. Métodos. Estudio observacional de medición clínica de una escala de magnitud del dolor crónico. Se realizó un proceso de traducción-retrotraducción siguiendo las guías internacionales. Setenta y cinco sujetos con dolor lumbar de más de 6 meses de evolución derivados a las unidades de fisioterapia en Atención Primaria participaron en el estudio. Se analizaron: la consistencia interna, la validez del constructo, la fiabilidad test-retest, los efectos techo y suelo, y la capacidad de respuesta. Resultados. La Escala de gradación del dolor crónico posee una alta consistencia interna, el α de Cronbach fue de 0,87, similar al que presentan las versiones en otros idiomas, y el coeficiente de correlación intraclase fue 0,81. En cuanto a la validez del constructo, se extrajeron 2 factores que consiguieron explicar el 72,37% de la varianza. La validez convergente muestra una correlación positiva moderada con la escala visual analógica, la subescala de evitación de actividad de la Escala Tampa de kinesiofobia, la Escala del catastrofismo para el dolor, el Cuestionario de discapacidad por dolor lumbar de Roland-Morris, y el Cuestionario de conductas de miedo-evitación, y una correlación negativa moderada con el Cuestionario de autoeficacia en el dolor crónico. El tiempo medio de administración fue de 2 min y 28 s. Conclusiones. La versión española de la Escala de gradación del dolor crónico parece ser un instrumento válido, fiable y útil para medir de forma precoz el dolor crónico en la práctica clínica en Atención Primaria en España (AU)

Objective. To adapt the Graded Chronic Pain Scale for use in Primary care patients in Spain, and to assess its psychometric properties. Methods. Clinical measures observational study investigating the severity of chronic pain. The methodology included a process of translation and back-translation following the international guidelines. Study participants were 75 patients who experienced lower back pain for more than six months and were sent to Primary Care physiotherapy units. Internal consistency, construct validity, test-retest reliability, floor and ceiling effects, and answering capacity were analysed. Results. The Spanish version of the Graded Chronic Pain Scale had a high internal consistency, with a Cronbach's alpha of 0.87 and intraclass correlation coefficient of 0.81. Regarding construct validity, it was identified that two factors explained 72.37% of the variance. Convergent validity showed a moderate positive correlation with the Visual Analogue Scale, the activity avoidance subscale of the Tampa Scale of Kinesophobia, the Pain Catastrophizing Scale, the Roland-Morris Low Back Pain and Disability Questionnaire, and the FearAvoidance Beliefs Questionnaire. A moderate negative correlation was identified with the Chronic Pain Self-Efficacy Scale. The mean time of questionnaire administration was 2minutes and 28seconds. Conclusions. The Spanish version of the Graded Chronic Pain Scale appears to be a valid, reliable, and useful tool for measuring chronic pain at an early stage in Primary Care settings in Spain (AU)

Declining trends in local health department preparedness capacities.

OBJECTIVES: We examined local health department (LHD) preparedness capacities in the context of participation in accreditation and other performance improvement efforts. MetHODS: We analyzed preparedness in 8 domains among LHDs responding to a preparedness capacity instrument from 2010 through 2012. Study groups included LHDs that (1) were exposed to a North Carolina state-based accreditation program, (2) participated in 1 or more performance improvement programs, and (3) had not participated in any performance improvement programs. We analyzed mean domain preparedness scores and applied a series of nonparametric Mann-Whitney Wilcoxon tests to determine whether preparedness domain scores differed significantly between study groups from 2010 to 2012. RESULTS: Preparedness capacity scores fluctuated and decreased significantly for all study groups for 2 domains: surveillance and investigation and legal preparedness. Significant decreases also occurred among participants for plans and protocols, communication, and incident command. Declines in capacity scores were not as great and less likely to be significant among North Carolina LHDs. CONCLUSIONS: Decreases in preparedness capacities over the 3 survey years may reflect multiple years of funding cuts and job losses, specifically for preparedness. An accreditation program may have a protective effect against such contextual factors.

A better prediction model for patient surges from influenza? New Internet-based tool shows promise, say researchers.

In a study focused on Baltimore, MD, researchers have found that data culled from Google Flu Trends, a free Internet-based influenza surveillance system, shows strong correlation with hikes in ED visits from patients with flu-like symptoms. While the approach has yet to be validated in other cities or regions, experts recommend that ED administrators and providers familiarize themselves with the new surveillance tool and stay abreast of developments regarding similar surveillance mechanisms. Google Flu Trends (www.google.org/flutrends/) is a free Internet-based tool that monitors Internet-based searches for flu information. Users can customize their search by location (city, state, country). Researchers say the advantage of this approach over traditional surveillance methods is that it provides real-time data about flu-related activity in a city or region. Traditional approaches, which rely on case reports from the Centers for Disease Control and Prevention, are delayed. Researchers hope to eventually leverage this tool, and perhaps other surveillance data, into a powerful early-warning mechanism that EDs can use to better plan for patient surges due to influenza.

Hospital-based pandemic influenza preparedness and response: strategies to increase surge capacity.

UNLABELLED: In the spring of 2009, the first patients infected with 2009 H1N1 virus were arriving for care in hospitals in the United States. Anticipating a second wave of infection, our hospital leaders initiated multidisciplinary planning activities to prepare to increase capacity by expansion of emergency department (ED) and inpatient functional space and redeployment of medical personnel. EXPERIENCE: During the fall pandemic surge, this urban, tertiary-care children's hospital experienced a 48% increase in ED visits and a 12% increase in daily peak inpatient census. However, several strategies were effective in mitigating the pandemic's impact including using a portion of the hospital's lobby for ED waiting, using a subspecialty clinic and a 24-hour short stay unit to care for ED patients, and using physicians not board certified in pediatric emergency medicine and inpatient-unit medical nurses to care for ED patients. The average time patients waited to be seen by an ED physician and the proportion of children leaving the ED without being seen by a physician was less than for the period when seasonal influenza peaked in the winter of 2008-2009. Furthermore, the ED did not go on divert status, no elective medical or surgical admissions required cancellation, and there were no increases in serious patient safety events. SUMMARY: Our health center successfully met the challenges posed by the 2009 H1N1 outbreak. The intent in sharing the details of our planning and experience is to allow others to determine which elements of this planning might be adapted for managing a surge of patients in their setting.

Detecting road maps for capacity utilization decisions by Clustering Analysis and CHAID Decision Trees.

The aims of this study are to provide a standard CUR value, to determine financial and organizational factors which affect the capacity utilization and develop road maps for increasing capacity utilization. To reach these aims by an objective method, we used data mining method that discovers hidden and useful pattern in a large amount of data. Two different method of data mining were used in two stages for this study. In first step, standard value of CUR was determined by K-means Clustering Analysis. CHAID Decision Tree Algorithm as a second method was implemented for determination of impact factors that provided steps for road maps. The study was concerned Turkish Ministry of Health public hospitals. 592 hospitals were covered and financial and operational data of the year 2004 were used in the study. Finally two different road maps were developed and suggestions were made according the results of the study.