Purpose: The purpose of this project is to decrease the likelihood of receiving unwanted treatment at the end of life. The project is conducted in a skilled nursing facility where only approximately 40% of the residents have an Advance Directive (AD) despite experiencing multiple comorbidities and nearing the end of life. Aims: Implementing a quality improvement project, creating a system that addresses AD completion at admission with the outcome of increased recognition of residents’ end of life choices. Process: Residents and their families are presented with a tool to help guide the conversation about AD at the admission care plan meeting. AD will be readdressed at all subsequent care plan meetings, occurring every 45 days, and as needed. Results: Project is still in process and the expected completion date is April 8th, 2022. We anticipate improvement in AD completion rates in this facility. Limitations: This project is limited by the reluctance of some residents and families to discuss issues related to death and dying, which may affect the completion rate. Another limiting factor is the staff turnover and need to reeducate new staff members about the project. Conclusions: Residents in skilled nursing facilities are likely to receive unwanted treatment because families are unsure what their wishes and are left feeling obligated to do everything necessary to sustain life. The implementation of this project may increase the AD completion rates, while recognizing and carrying out residents’ end of life choices.

Staff in skilled nursing facilities (SNF) can experience physical and emotional strain via caregiving. The purpose of this study was to educate staff on the harm of compassion fatigue and a lack of emotional intelligence and provide steps that can be taken by administration to improve the quality of care provided. It was hypothesized for staff that having low compassion fatigue and high emotional intelligence would result in a higher quality of care. The study design utilized a quantitative approach and a purposive sample from a SNF. Participants were provided with The Professional Quality of Life 5 Scale (ProQoL 5), Wong & Law Emotional Intelligence Scale (WLEIS), and survey data received from Department of Public Health. A Multiple Regression test analyzed the relationship between compassion fatigue and emotional intelligence on the quality of care provided by staff members. The results of this study indicated that staff’s compassion fatigue was not indicative of quality of care; however, Self-Emotional Appraisal, a subscale of WLEIS, was found to predict the quality of care. This study assisted with informing SNF staff in recognizing how managing their emotions could be a useful tool to improve the quality of care they provide. Lastly, SNF administration could implement policies, procedures, and in-services to ensure that all staff members are educated in identifying emotions and practicing self-care

This qualitative phenomenological study examined central Florida's nursing home administrators' knowledge and perception of cultural competence and how they perceive that their knowledge and perceptions impact residents, families, and healthcare. The theoretical framework for this study was explained through Campinha-Bacote's interdisciplinary model of The Process of Cultural Competence in the Delivery of Healthcare Services. The theoretical framework was used to guide research questions, and individual interview questions to obtain nursing home administrator's knowledge and perception of cultural competence. The study explored the level of cultural competence reported by nursing home administrators, their individual perception of cultural competence, their degree of confidence in cultural competence, and how nursing home administrators describe their proficiencies and skills in cultural competence. Interviews with six nursing home administrators included licensed nursing home administrators (NHA) and assistant nursing home administrators, and the director of nursing (DON). The findings identified and analyzed the diverse levels of nursing home administrator's knowledge and perception of cultural competence. Some participants demonstrated difficulty expressing their knowledge and perception of cultural competence. Findings indicate that various factors influenced participants' overall degree of confidence in their knowledge and perception of cultural competence. Nursing home administrators also had a difficult time articulating particular skills that demonstrate their ability to adapt to the diverse residents in their facilities, and how they promote cultural competence in their nursing home facilities. This study identified the need for additional research and continuing education about cultural competence in healthcare.

Introduction: Nursing homes throughout the U.S. have faced unprecedented challenges in recent years because of staffing shortages, and under-investment, especially since the COVID-19 pandemic. To gain insight into the current state of nursing homes, this study examined nursing home characteristics, quality, and accessibility from a national, regional and state perspective. Methods: This cross-sectional study used secondary data from the Centers for Medicare and Medicaid Services, American Community Survey, Medicare Beneficiary, and the Tennessee Department of Health. Nursing home characteristics and quality were assessed through bivariate and multivariate regression in the U.S. Assessment of HHS Region 4 nursing home characteristics and quality by state cut points were conducted with bivariate analysis. Finally, spatial analysis was conducted to determine nursing home accessibility in Tennessee. Results: In multivariate analyses of all U.S. facilities, non-metropolitan facilities are at 1.27 higher odds of being a 1-star overall rated facility and 0.86 lower odds of being a 5-star facility as compared to metropolitan facilities. When weighted health inspection scores were recategorized by more strict state cut points, 42.45% of HHS Region 4 facilities decreased in health inspection star ratings, while those that were recategorized by more lenient state cut points lead to a 26.64% increase in star ratings. Finally, in the state of Tennessee a mean of 14.9% of county areas are not within 30-minute drive to any nursing home facilities, with 66.4% of county areas being further than a 30-minute drive to a 5-star rated (highest quality) facility. Discussion: Findings from this study show that differences exist in nursing home characteristics that relate to facility quality. Additionally, health inspection 5-star ratings can vary across states, which can make comparison of quality challenging from a consumer perspective. And finally, accessibility to nursing homes can vary throughout a state by metropolitan and non-metropolitan status of the county. An understanding of nursing homes in metropolitan and non-metropolitan communities along with nursing home quality, characteristics of the facility, and characteristics of county populations can enable policymakers to create more equitable policy solutions for nursing homes and the communities they serve.

<p dir="ltr">Recurrent stroke, infection, and seizure are some of the most common complications in stroke survivors. Recurrent stroke leads to death in 38.6% of survivors, and infections are the most common risk factor for seizures, with stroke survivors that experience an infection being at greater risk of experiencing a seizure. Two predictive models were generated, recurrent stroke and post-infection seizure, to determine stroke survivors at greatest risk to help providers focus on prevention in higher risk residents.</p><p dir="ltr">Predictive models were generated from a retrospective study of the Long-Term Care Minimum Data Set (MDS) 3.0 (2014-2018, n=262,301). Techniques included three data balancing methods (SMOTE for up sampling, ENN for down sampling, and SMOTEENN for up and down sampling) and three feature selection methods (LASSO, RFE, and PCA). The resulting datasets were then trained on four machine learning models (Logistic Regression, Random Forest, XGBoost, and Neural Network). Model performance was evaluated with AUC and accuracy, and interpretation used SHapley Addictive exPlanations.</p><p dir="ltr">Using data balancing methods improved the prediction performances of the machine learning models, but feature selection did not remove any features or affect performance. With all models having a high accuracy (78.6% to 99.9%), interpretation on all four models yielded the most holistic view. For recurrent stroke, SHAP values indicated that treatment combinations of occupational therapy, physical therapy, antidepressants, non-medical intervention for pain, therapeutic diet, anticoagulants, and diuretics contributed more to reducing recurrent stroke risk in the model when compared to individual treatments. For post-infection seizure, SHAP values indicated that therapy (speech, physical, occupational, and respiratory), independence (activities of daily living for walking, mobility, eating, dressing, and toilet use), and mood (severity score, anti-anxiety medications, antidepressants, and antipsychotics) features contributed the most. Meaning, stroke survivors who received fewer therapy hours, were less independent, and had a worse overall mood were at a greater risk of having a post-infection seizure.</p><p dir="ltr">The development of a tool to predict recurrent stroke and post-infection seizure in stroke survivors can be interpreted by providers to guide treatment and rehabilitation to prevent complications long-term. This promotes individualized plans that can increase the quality of resident care.</p>

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