scholarly journals A comprehensive county level model to identify factors affecting hospital capacity and predict future hospital demand

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tanmoy Bhowmik ◽  
Naveen Eluru
Keyword(s):  
County Level ◽  
Hospital System ◽  
Factors Affecting ◽  
Vaccination Rates ◽  
Department Of Health ◽  
The Us ◽  
Level Model ◽  
Future Hospital ◽  
Comprehensive Framework ◽  
Usage Rates

AbstractThe sustained COVID-19 case numbers and the associated hospitalizations have placed a substantial burden on health care ecosystem comprising of hospitals, clinics, doctors and nurses. However, as of today, only a small number of studies have examined detailed hospitalization data from a planning perspective. The current study develops a comprehensive framework for understanding the critical factors associated with county level hospitalization and ICU usage rates across the US employing a host of independent variables. Drawing from the recently released Department of Health and Human Services weekly hospitalization data, we study the overall hospitalization and ICU usage—not only COVID-19 hospitalizations. Developing a framework that examines overall hospitalizations and ICU usage can better reflect the plausible hospital system recovery path to pre-COVID level hospitalization trends. The models are subsequently employed to generate predictions for county level hospitalization and ICU usage rates in the future under several COVID-19 transmission scenarios considering the emergence of new COVID-19 variants and vaccination rates. The exercise allows us to identify vulnerable counties and regions under stress with high hospitalization and ICU rates that can be assisted with remedial measures. Further, the model will allow hospitals to understand evolving displaced non-COVID hospital demand.

scholarly journals Vaccine Education in the Library: Lessons Learned from a Hot Topic Program

2020 ◽  
Vol 18 (3) ◽  
pp. 21
Author(s):  
Katherine Hickey ◽  
Annie Emmons
Keyword(s):  
Human Services ◽  
Lessons Learned ◽  
World Health ◽  
Vaccine Hesitancy ◽  
Vaccination Rates ◽  
Health And Human Services ◽  
Department Of Health ◽  
The Us ◽  
The World ◽  
Health Organization

Recent data from the US Department of Health and Human Services indicate a small but growing number of unvaccinated children under the age of two. Low vaccination rates can result in outbreaks of preventable diseases and even death. The World Health Organization (WHO) identified vaccine hesitancy as one of the top ten threats to global health in 2019.

scholarly journals A Comprehensive County Level Framework to Identify Factors Affecting Hospital Capacity and Predict Future Hospital Demand

2021 ◽  
Author(s):  
Tanmoy Bhowmik ◽  
Naveen Eluru
Keyword(s):  
High Resolution ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Health Planning ◽  
Health Indicators ◽  
County Level ◽  
Remedial Measures ◽  
Factors Affecting ◽  
Per Capita ◽  
Usage Rates

SUMMARYBackgroundAs of February 19, 2021, our review yielded a small number of studies that investigated high resolution hospitalization demand data from a public health planning perspective. The earlier studies compiled were conducted early in the pandemic and do not include any analysis of the hospitalization trends in the last 3 months when the US experienced a substantial surge in hospitalization and ICU demand. The earlier studies also focused on COVID 19 transmission influence on COVID 19 hospitalization rates. While this emphasis is understandable, there is evidence to suggest that non COVID hospitalization demand is being displaced due to the hospitalization and ICU surge. Further, with the discovery of multiple mutated variants of COVID 19, it is important to remain vigilant in an effort to control the pandemic. Given these circumstances, the development of a high resolution framework that examines overall hospitalizations and ICU usage rate for COVID and non COVID patients would allow us to build a prediction system that can identify potential vulnerable locations for hospitalization capacity in the nation so that appropriate remedial measures can be planned.MethodThe current study recognizes that COVID 19 has affected overall hospitalizations – not only COVID 19 hospitalizations. Drawing from the recently released Department of Health and Human services (DHH) weekly hospitalization data (or the time period August 28th, 2020 to January 22nd, 2021.), we study the overall hospitalization and ICU usage as two components: COVID 19 hospitalization and ICU per capita rates; and non COVID hospitalization and ICU per capita rates. A mixed linear mixed model is adopted to study the response variables in our study. The estimated models are subsequently employed to generate predictions for county level hospitalization and ICU usage rates in the future under a host of COVID 19 transmission scenarios considering the new variants of COVID 19 and vaccination impacts.FindingsWe find a significant association of the virus transmissibility with COVID (positive) and non COVID (negative) hospitalization and ICU usage rates. Several county level factors including demographics, mobility and health indicators are also found to be strongly associated with the overall hospitalization and ICU demand. Among the various scenarios considered, the results indicate a small possibility of a new wave of infections that can substantially overload hospitalization and ICU usage. In the scenario where vaccinations proceed as expected reducing transmission, our results indicate that hospitalizations and ICU usage rates are likely to reduce significantly.InterpretationThe research exercise presents a framework to predict evolving hospitalization and ICU usage trends in response to COVID 19 transmission rates while controlling for other factors. Our work highlights how future hospitalization demand varies by location and time in response to a range of pessimistic and optimistic scenarios. Further, the exercise allows us to identify vulnerable counties and regions under stress with high hospitalization and ICU rates that can be assisted with remedial measures. The model will also allow hospitals to understand evolving displaced non COVID hospital demand.

scholarly journals Factors affecting the COVID-19 risk in the US counties: an innovative approach by combining unsupervised and supervised learning

2021 ◽  
Author(s):  
Samira Ziyadidegan ◽  
Moein Razavi ◽  
Homa Pesarakli ◽  
Amirhossein Javid ◽  
Madhav Erraguntla
Keyword(s):  
United States ◽  
Wind Speed ◽  
The United States ◽  
County Level ◽  
Classification Models ◽  
Critical Factors ◽  
Factors Affecting ◽  
Innovative Method ◽  
The Us ◽  
Us Counties

Abstract The COVID-19 disease spreads swiftly, and nearly three months after the first positive case was confirmed in China, Coronavirus started to spread all over the United States. Some states and counties reported high number of positive cases and deaths, while some reported lower COVID-19 related cases and mortality. In this paper, the factors that could affect the risk of COVID-19 infection and mortality were analyzed in county level. An innovative method by using K-means clustering and several classification models is utilized to determine the most critical factors. Results showed that mean temperature, percent of people below poverty, percent of adults with obesity, air pressure, population density, wind speed, longitude, and percent of uninsured people were the most significant attributes.

Chemistry instructors’ intentions toward developing, teaching, and assessing student representational competence skills

2021 ◽  
Author(s):  
Maia Popova ◽  
Tamera Jones
Keyword(s):  
Professional Development ◽  
Coding Theory ◽  
Assessment Practices ◽  
Self Awareness ◽  
Scientific Practices ◽  
Information Processing Model ◽  
Representational Competence ◽  
Evidence Based Practices ◽  
Factors Affecting ◽  
The Us

Representational competence is one's ability to use disciplinary representations for learning, communicating, and problem-solving. These skills are at the heart of engagement in scientific practices and were recognized by the ACS Examinations Institute as one of ten anchoring concepts. Despite the important role that representational competence plays in student success in chemistry and the considerable number of investigations into students’ ability to reason with representations, very few studies have examined chemistry instructors’ approaches toward developing student representational competence. This study interviewed thirteen chemistry instructors from eleven different universities across the US about their intentions to develop, teach, and assess student representational competence skills. We found that most instructors do not aim to help students develop any representational competence skills. At the same time, participants’ descriptions of their instructional and assessment practices revealed that, without realizing it, most are likely to teach and assess several representational competence skills in their courses. A closer examination of these skills revealed a focus on lower-level representational competence skills (e.g., the ability to interpret and generate representations) and a lack of a focus on higher-level meta-representational competence skills (e.g., the ability to describe affordances and limitations of representations). Finally, some instructors reported self-awareness about their lack of knowledge about effective teaching about representations and the majority expressed a desire for professional development opportunities to learn about differences in how experts and novices conceptualize representations, about evidence-based practices for teaching about representations, and about how to assess student mastery of representational competence skills. This study holds clear implications for informing chemistry instructors’ professional development initiatives. Such training needs to help instructors take cognizance of relevant theories of learning (e.g., constructivism, dual-coding theory, information processing model, Johnstone's triangle), and the key factors affecting students’ ability to reason with representations, as well as foster awareness of representational competence skills and how to support students in learning with representations.

scholarly journals Analysis of factors affecting IoT-based smart hospital design

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Banu Çalış Uslu ◽  
Ertuğ Okay ◽  
Erkan Dursun
Keyword(s):  
High Efficiency ◽  
Health Sector ◽  
Transformation Process ◽  
Healthcare Management ◽  
Hospital System ◽  
Intelligent Computing ◽  
Factors Affecting ◽  
Hospital Design ◽  
Road Map ◽  
Smart Hospital

AbstractCurrently, rapidly developing digital technological innovations affect and change the integrated information management processes of all sectors. The high efficiency of these innovations has inevitably pushed the health sector into a digital transformation process to optimize the technologies and methodologies used to optimize healthcare management systems. In this transformation, the Internet of Things (IoT) technology plays an important role, which enables many devices to connect and work together. IoT allows systems to work together using sensors, connection methods, internet protocols, databases, cloud computing, and analytic as infrastructure. In this respect, it is necessary to establish the necessary technical infrastructure and a suitable environment for the development of smart hospitals. This study points out the optimization factors, challenges, available technologies, and opportunities, as well as the system architecture that come about by employing IoT technology in smart hospital environments. In order to do that, the required technical infrastructure is divided into five layers and the system infrastructure, constraints, and methods needed in each layer are specified, which also includes the smart hospital’s dimensions and extent of intelligent computing and real-time big data analytic. As a result of the study, the deficiencies that may arise in each layer for the smart hospital design model and the factors that should be taken into account to eliminate them are explained. It is expected to provide a road map to managers, system developers, and researchers interested in optimization of the design of the smart hospital system.

The Effects of Production Performance Model and Competitive Environment of Hospital Systems

2013 ◽  
Vol 300-301 ◽  
pp. 1623-1627
Author(s):  
Ching Kuo Wei
Keyword(s):  
Performance Model ◽  
Production Performance ◽  
Competitive Environment ◽  
Data Envelopment ◽  
Hospital System ◽  
Department Of Health ◽  
Competitive Environments ◽  
Significant Difference ◽  
Efficiency Performance ◽  
Hospital Systems

This study investigated the efficiency performance of the production technology of the Department of Health (DOH)-affiliated hospital system in Taiwan in different competitive environments. This study used Data Envelopment Analysis (DEA) to analyze a total of 396 hospitals of different systems in Taiwan. The results indicated that, in terms of the internal competitive environment, the production performance of hospital Q was the best, while that of hospital N was the worst. This study also analyzed the production performance and scale of DOH-affiliated hospitals and provided hospitals with a direction for scale development. Finally, this study proposed suggestions on improvement direction for hospitals with poor production performance. As for external competitive environment, there is no significant difference in the average efficiency among various hospital systems. However, there are a lot to be improved in DOH-affiliated hospitals, especially in the aspect of technology efficiency. The improvement of technology efficiency should be more beneficial to the overall efficiency.

A County-Level Analysis of the US Radiologist Workforce: Physician Supply and Subspecialty Characteristics

2018 ◽  
Vol 15 (4) ◽  
pp. 601-606 ◽  
Author(s):  
Andrew B. Rosenkrantz ◽  
Wenyi Wang ◽  
Danny R. Hughes ◽  
Richard Duszak
Keyword(s):  
County Level ◽  
Physician Supply ◽  
The Us ◽  
Level Analysis
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