These findings provide evidence of the importance of nurse attitudes in improving perceived and actual performance across facilities and health care systems; in addition to practical steps, managers can take to improve satisfaction and retention.
The authors review empirical literature from 1980 to 2005 on relationships between healthcare organizations' finances and quality of care. They found only 16 studies of this topic that employed statistical methods. This research indicates cumulatively that expenses, fiscal margin, and asset and liability management all affect healthcare outcome quality. There is less evidence about how organizational finance factors affect structural or process quality, and there is no information about how structural or process quality mediates between finances and outcomes. The authors note what patterns have emerged from previous studies and make specific suggestions about what future research is necessary and why.
Coronavirus (COVID-19) is a potentially fatal viral infection. This study investigates geography, demography, socioeconomics, health conditions, hospital characteristics, and politics as potential explanatory variables for death rates at the state and county levels. Data from the Centers for Disease Control and Prevention, the Census Bureau, Centers for Medicare and Medicaid, Definitive Healthcare, and USAfacts.org were used to evaluate regression models. Yearly pneumonia and flu death rates (state level, 2014–2018) were evaluated as a function of the governors’ political party using a repeated measures analysis. At the state and county level, spatial regression models were evaluated. At the county level, we discovered a statistically significant model that included geography, population density, racial and ethnic status, three health status variables along with a political factor. A state level analysis identified health status, minority status, and the interaction between governors’ parties and health status as important variables. The political factor, however, did not appear in a subsequent analysis of 2014–2018 pneumonia and flu death rates. The pathogenesis of COVID-19 has a greater and disproportionate effect within racial and ethnic minority groups, and the political influence on the reporting of COVID-19 mortality was statistically relevant at the county level and as an interaction term only at the state level.
The physical demands on U.S. service members have increased significantly over the past several decades as the number of military operations requiring overseas deployment have expanded in frequency, duration, and intensity. These elevated demands from military operations placed upon a small subset of the population may be resulting in a group of individuals more at-risk for a variety of debilitating health conditions. To better understand how the U.S Veterans health outcomes compared to non-Veterans, this study utilized the U.S. Centers for Disease Control and Prevention (CDC) Behavioral Risk Factor Surveillance System (BRFSS) dataset to examine 10 different self-reported morbidities. Yearly age-adjusted, population estimates from 2003 to 2019 were used for Veteran vs. non-Veteran. Complex weights were used to evaluate the panel series for each morbidity overweight/obesity, heart disease, stroke, skin cancer, cancer, COPD, arthritis, mental health, kidney disease, and diabetes. General linear models (GLM’s) were created using 2019 data only to investigate any possible explanatory variables associated with these morbidities. The time series analysis showed that Veterans have disproportionately higher self-reported rates of each morbidity with the exception of mental health issues and heart disease. The GLM showed that when taking into account all the variables, Veterans disproportionately self-reported a higher amount of every morbidity with the exception of mental health. These data present an overall poor state of the health of the average U.S. Veteran. Our study findings suggest that when taken as a whole, these morbidities among Veterans could prompt the U.S. Department of Veteran Affairs (VA) to help develop more effective health interventions aimed at improving the overall health of the Veterans.
The timely coordination of care in clinics that require frequent assessments by multiple specialists can be challenging for both patients and providers. The cornerstone of care at cystic fibrosis (CF) centers with superior clinical outcomes, as with reduced acuity of episodic disease and incidence of hospitalizations, is frequent clinical encounters coupled with aggressive therapies. However, inefficiencies in the clinical practice structure prevent optimal utilization of resources. To decrease non-value-added time, defined as time a patient spends alone in an examination room, without altering the time providers spend caring for a patient, the application of Lean methods was used to see whether reducing variation could significantly decrease lead time, considered the length of a patient visit, within a CF clinic setting. Baseline capability analyses revealed only 19.3% of patient visits were completed in 60min or less, with mean and median visit times of 84 and 81min, respectively. Final capability analyses demonstrated that 41.5% of patient visits were completed in 60min or less, 23% greater than the baseline capability. Mean and median visit times decreased by 10min per visit. Research efforts increased the available capacity by 500 patient visits per year, representing additional revenue of over US$165,000 annually with no additional administrative costs incurred.
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