The Effect of Patient and Hospital-level Factors on 30-Day Readmission After Initial Hospitalization Due to Stroke

Background: Hospital readmissions account for a large part of healthcare costs, especially among stroke patients. Readmission is common among disabled stroke survivors because they often suffer some neurological deficits, functional impairment, and other preexisting cardiovascular conditions. Although previous studies have explored the relationship between hospital readmissions after initial hospitalization due to stroke and a set of predictors using various analytical models, it often remains uncertain which predictors are most influential or essential. This study aimed to assess the effect of patient and hospital-levels factors on 30-day readmission after initial hospitalization due to stroke using the Anderson model of healthcare utilization as a guide. Methods: Data for this study was the 2014 National Readmissions Database. A generalized mixed-effect linear regression using a hierarchical modeling approach was run based on the Andersen model\u27s main block to assess the predictive capabilities of both individual and hospital-level factors on 30-day readmission. Models also assessed geographic differences that may exist among stroke patients. Results: Overall, the addition of variables blocks corresponding to the Anderson model of health utilization accounted for only a small variance in 30-day readmission. However, the addition of the enabling and need factors resulted in the most significant R2 change for hospitals in rural areas and urban areas, respectively. Conclusion: The predictive powers of individual and hospital factors on readmission within 30 days of initial stroke-caused hospitalization is weak. The results of this study suggest a holistic approach should be the goal for policymakers and legislators when developing policies to reduce readmissions

Promoting Self-Determined Motivation For Exercise In Stroke Rehabilitation: The Role Of Autonomy Support

Rehabilitation Interventionists (RIs) usually spend a great deal of time and effort trying to improve the functional abilities of stroke patients. Stroke rehabilitation through current studies has been recognized as an important and effective modality in the treatment of stroke. Despite the known benefits of SR to stroke patients, a number of them drop out resulting in a decline the benefits from the program. Objective: To examine the predictive relationship between perceived autonomy support provided by Stroke Rehabilitation Interventionists, and the participants\u27 subsequent stroke rehabilitation program attendance rate. The study also examines the predictive relationship between participants\u27 perceived autonomy support and their motivation to exercise, which in turn, would predict higher stroke rehabilitation program attendance rate. Research Method: Stroke rehabilitation outpatients (N = 35; Male = 20; Female = 15; Mage = 52.79 years: SD = 12.16). This study examined the predictive relationship between participants\u27 perceived autonomy support and motivation for exercise at weeks 2 and 3 of stroke rehabilitation participation. It also examined the predictive relationship between participants\u27 perceived autonomy support and stroke rehabilitation attendance rate. Stroke rehabilitation attendance was tracked for a period of 5 weeks. Descriptive statistics, bivariate correlations and hierarchical linear regression were calculated to assess the predictive relationships between perceived autonomy support, self-determined motivation and stroke rehabilitation program attendance rate. Results: Perceived autonomy support was not positively correlated with relative autonomy index, r(35) = .13, p \u3e .05. The relationship between perceived autonomy support and all other forms of controlled motivation was also not significant. The regression model predicting program attendance showed significant positive effect for perceived autonomy support (β = .56, R2 = .32, p \u3c .001). However, the regression model predicting program attendance showed a non-significant effect for self-determined motivation (β = .56, R2 = .32, p = NS). Conclusion: Results supports Self-Determination Theory in predicting the attendance rate of participants in SR. The higher the perceived autonomy support provided by RI to stroke patients, the higher their attendance rate in a stroke rehabilitation program. RIs supporting stroke patients\u27 autonomy support rather than interfering with their autonomy or neglecting them during the SR process helps create an environment where stroke patients can feel an engagement-fostering balance between what they want to do and what they are actually told to do

Detailed Review on The Denial of Service (DoS) and Distributed Denial of Service (DDoS) Attacks in Software Defined Networks (SDNs) and Defense Strategies

The development of Software Defined Networking (SDN) has altered the landscape of computer networking in recent years. Its scalable architecture has become a blueprint for the design of several advanced future networks. To achieve improve and efficient monitoring, control and management capabilities of the network, software defined networks differentiate or decouple the control logic from the data forwarding plane. As a result, logical control is centralized solely in the controller. Due to the centralized nature, SDNs are exposed to several vulnerabilities such as Spoofing, Flooding, and primarily Denial of Service (DoS) and Distributed Denial of Service (DDoS) among other attacks. In effect, the performance of SDN degrades based on these attacks. This paper presents a comprehensive review of several DoS and DDoS defense/mitigation strategies and classifies them into distinct classes with regards to the methodologies employed. Furthermore, suggestions were made to enhance current mitigation strategies accordingly

Characteristics of the Health Information Technology Workforce in Georgia

Background: Advancement in medical technology, as well as the Health Information Technology for Economic and Clinical Health Act, has in part influenced the demand for health information technology (HIT) workers. While other sectors have experienced a tremendous increase in the information technology workforce, the health sector lags in this regard. The aim of this study was to describe the characteristics of the HIT workforce in Georgia, relative to surrounding states and the United States. Methods: The supply of the HIT workforce in Georgia, surrounding states, and the United States was estimated using data from the 2014-2016 American Community Survey (ACS). The 2010 ACS Occupation Codes and 2012 ACS Industry Codes were used to identify the HIT workforce. Population data for 2015, obtained from the US Census Bureau was used for standardization of the total supply of the HIT workforce. Data were analyzed using Stata 14.0. Results: The number of HIT workforce supply for Georgia (206.4 per 100,000 population) trails national (275.4 per 100,000) and regional (233 per 100,000) estimates. In terms of demographic characteristics, Georgia has a more racially diverse HIT workforce, compared to the surrounding states and the nation but lacked Hispanic representation. Additionally, compared to the surrounding states and the US, Georgia has a higher proportion of females in this workforce (80.9%). Most HIT workers are employed in hospitals and work full-time. Conclusions: The supply of the HIT workforce in Georgia currently trails regional and national estimates. With the advancements in medical technology and the HITECH Act, there is an increasing demand for health information technology workers. As such, attention should be paid to recruitment and retention efforts. This report may serve as a reference for future evaluation and monitoring of trends in the HIT workforce in the state

Examining the Characteristics of Physicians That Leave Georgia After Medical School Training

Background: We sought to examine the characteristics of physicians who leave Georgia after graduating from a Georgia medical school. Methods: Using the 2017 National Physician Compare data merged with the 2015-2016 Area Resource File, we compared the individual and practice location characteristics of physicians who went to medical school in Georgia and practice in the state to those who left. Results: Less than half of physicians who had their medical school training in Georgia still practice here; those who leave are typically specialists practicing in older, affluent and less racially diverse counties. Conclusions: Strategies to retain physicians in Georgia after their medical school training will go a long way to enhance Georgia’s physician-to-population ratios

Solo Practice Physicians in Georgia

Background: Office-based physicians can practice in a solo or group setting. Solo physician practices are staffed by a single physician who is responsible for all the care of the patients. Physicians in solo practices are also responsible for the infrastructure, personnel and investment cost of their practices. Further, evidence suggests that physicians in solo practices are more likely to be dissatisfied with their medical career compared to those in group practices. Given these challenges, current trend suggests a shift away from solo physician practices. However, there are still physicians in solo practices in Georgia but little is known about them. This study attempts to characterize the physicians working in solo practices and in so doing, add to the growing knowledge of the healthcare workforce in Georgia. Methods: The 2014 Physician Compare data were used for this study. This database contains information on individual physician level characteristics including gender, credential, primary specialty and practice type. The data were linked to the 2014 Area Resource File to provide information on the rural/urban location of physician practices. Physician practices were classified as rural or urban based on the Economic Research Service classification. Chi square and t-tests were carried out to examine the characteristics of physicians practicing in solo practices. Statistical analyses were conducted in StataMP 14. Results: Of the 13,499 Georgia physicians studied, 1448 physicians were in solo practices. The majority of these physicians were in urban areas (78.30%; p\u3c0.001), male (72.18% p\u3c0.001), had primary care specialties (46.31% p\u3c0.001) and more experience in practice (27.6 years; p\u3c0.001). In addition, almost three quarters did not use electronic health records (71.97% p\u3c0.001) and the majority did not report on quality measures to the Centers for Medicare and Medicaid Services (66.7%; p\u3c0.001). Conclusions: There are a large number of physicians in solo practices in Georgia. Given the challenges facing these physicians, it is important for Georgia to consider approaches to decrease the burden on physicians working in solo practices

Feasibility study of rehabilitation for cardiac patients aided by an artificial intelligence web-based programme: a randomised controlled trial (RECAP trial)—a study protocol

Introduction: Cardiac rehabilitation (CR) delivered by rehabilitation specialists in a healthcare setting is effective in improving functional capacity and reducing readmission rates after cardiac surgery. It is also associated with a reduction in cardiac mortality and recurrent myocardial infarction. This trial assesses the feasibility of a home-based CR programme delivered using a mobile application (app).Methods: The Rehabilitation through Exercise prescription for Cardiac patients using an Artificial intelligence web-based Programme (RECAP) randomised controlled feasibility trial is a single-centre prospective study, in which patients will be allocated on a 1:1 ratio to a home-based CR programme delivered using a mobile app with accelerometers or standard hospital-based rehabilitation classes. The home-based CR programme will employ artificial intelligence to prescribe exercise goals to the participants on a weekly basis. The trial will recruit 70 patients in total. The primary objectives are to evaluate participant recruitment and dropout rates, assess the feasibility of randomisation, determine acceptability to participants and staff, assess the rates of potential outcome measures and determine hospital resource allocation to inform the design of a larger randomised controlled trial for clinical efficacy and health economic evaluation. Secondary objectives include evaluation of health-related quality of life and 6 minute walk distance.Ethics and dissemination: RECAP trial received a favourable outcome from the Berkshire research ethics committee in September 2022 (IRAS 315483)

Georgia’s Rural Hospital Closures: The Common-Good Approach to Ethical Decision-Making

Background: Critical access hospitals provide several essential services to local communities. Along with the functions associated with providing necessary medical care, they also offer employment opportunities and other economic benefits to the communities they serve. Since 2010, the number of rural hospitals closures has steadily increased. The common-good approach to ethical decision-making provides a framework that aids in evaluation of the effects that hospital closures have on rural residents and communities. Methods: This analysis includes results of a systematic overview of peer-reviewed literature to address the following research questions: 1) How have state policies and the adoption of Medicaid expansion influenced the viability or rural hospitals? 2) What are the ethical implications of Medicaid expansion and state policy reform/adoption pertaining to viability of rural hospitals? and 3) What are the ethical implications of critical access hospitals closures on rural communities in Georgia? Information related to these questions is presented, along with tactics to addressing these in an ethical manner. Results: This descriptive analysis shows that the largest number of state-specific closures have occurred in states with a federal exchange and which chose not to expand Medicaid. Characteristics of the state of Georgia and the counties with recent closures show that these counties typically have smaller populations with a high minority presence, lower education and income levels, and higher numbers of medically uninsured. Conclusions: The common-good approach to ethical decision-making is suitable for evaluating the ethical implications of policy-level decisions impacting the closure of critical access hospitals serving the rural communities of Georgia

Georgia’s Rural Hospital Closures: The Common-Good Approach to Ethical Decision-Making

Background: Critical access hospitals provide several essential services to local communities. Along with the functions associated with providing necessary medical care, they also offer employment opportunities and other economic benefits to the communities they serve. Since 2010, the number of rural hospitals closures has steadily increased. The common-good approach to ethical decision-making provides a framework that aids in evaluation of the effects that hospital closures have on rural residents and communities. Methods: This analysis includes results of a systematic overview of peer-reviewed literature to address the following research questions: 1) How have state policies and the adoption of Medicaid expansion influenced the viability or rural hospitals? 2) What are the ethical implications of Medicaid expansion and state policy reform/adoption pertaining to viability of rural hospitals? and 3) What are the ethical implications of critical access hospitals closures on rural communities in Georgia? Information related to these questions is presented, along with tactics to addressing these in an ethical manner. Results: This descriptive analysis shows that the largest number of state-specific closures have occurred in states with a federal exchange and which chose not to expand Medicaid. Characteristics of the state of Georgia and the counties with recent closures show that these counties typically have smaller populations with a high minority presence, lower education and income levels, and higher numbers of medically uninsured. Conclusions: The common-good approach to ethical decision-making is suitable for evaluating the ethical implications of policy-level decisions impacting the closure of critical access hospitals serving the rural communities of Georgia

Estimating Passenger Demand Using Machine Learning Models: A Systematic Review

This article investigated machine learning models used to estimate passenger demand. These models have the potential to provide valuable insights into passenger trip behaviour and other inferences. The estimate of passenger demand using machine learning model research and the methodologies used are fragmented. To synchronise these studies, this paper conducts a systematic review of machine learning models to estimate passenger demand. The review investigates how passenger demand is estimated using machine learning models. A comprehensive search strategy is conducted across the three main online publishing databases to locate 911 unique records. Relevant record titles, abstracts, and publication information are extracted, leaving 102 articles. Furthermore, articles are evaluated according to eligibility requirements. This procedure yields 21 full-text papers for data extraction. 3 research thematic questions covering passenger data collection techniques, passenger demand interventions, and intervention performance are reviewed in detail. The results of this study suggest that mobility records, LSTM-based models, and performance metrics play a critical role in conducting passenger demand prediction studies. The model evaluation was mostly restricted to 3 performance metrics which needs improved metric for evaluation. Furthermore, the review determined an overreliance on the longand short-term memory model to estimate passenger demand. Therefore, minimising the limitation of the LSTM model will generally improve the estimation models. Furthermore, having an acceptable trainset to avoid overfitting is crucial. In addition, it is advisable to consider multiple metrics to have a more comprehensive evaluation