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Nexus of thermal resilience and energy efficiency in buildings: A case study of a nursing home
Extreme weather events become more frequent and severe due to climate change. Although energy efficiency technologies can influence thermal resilience of buildings, they are traditionally studied separately, and their interconnections are rarely quantified. This study developed a methodology of modeling and analysis to provide insights into the nexus of thermal resilience and energy efficiency of buildings. We conducted a case study of a real nursing home in Florida, where 12 patients died during Hurricane Irma in 2017 due to HVAC system power loss, to understand and quantify how passive and active energy efficiency measures (EEMs) can improve thermal resilience to reduce heat-exposure risk of patients. Results show that passive measures of opening windows and doors for natural ventilation, as well as miscellaneous load reduction, are very effective in eliminating the extreme dangerous occasions. However, to maintain safe conditions, active measures such as on-site power generators and thermal storage are also needed. The nursing home was further studied by changing its location to two other cities: San Francisco (mild climate) and Chicago (cold winter and hot summer). Results revealed that the EEMs' impacts on thermal resilience vary significantly by climate and building characteristics. The study also estimated the costs of EEMs to help stakeholders prioritize the measures. Passive measures that may not save energy may greatly improve thermal resilience, and thus should be considered in building design or retrofit. Findings from this study indicate energy efficiency technologies should be evaluated not only by their energy savings performance but also by their influence on a building's resilience to extreme weather events
Enhancing the Braden Scale Pressure Ulcer Risk Assessment in Long-Term Care Facilities: A Cohort Study
The development of pressure ulcers remains challenging as they are associated with overwhelming costs, pain and suffering, prolonged hospitalization, and morbidity and mortality. In the United States, the Braden scale is the most widely used risk assessment tool among all healthcare organizations to identify high-risk individuals for pressure ulcer development. The objective of risk assessment is to detect high-risk patients, implement immediate interventions, and evaluate patients not at risk who do not require intervention. The purpose of this cohort study is to determine the pressure ulcer predictability of the Braden score in comparison to the Braden score with additional predictor factors. Predictor factors explored in this research incorporate those described in current literature as pressure ulcer risk factors: age, gender, comorbidities, and history of previous pressure ulcer. This study utilized an observational cohort research design to appraise the effectiveness of an intervention based on evidence and data. Logistic regression statistical analysis was applied to establish how successful Braden total scores were in a pressure ulcer predictive model with and without the addition of additional predictor factors. A separate relative risk model was tested using only the most applicable predictor factors correlated with pressure ulcer prevalence in this model without the Braden score in an effort to cultivate the most relative model. The research was conducted solely at Symphony of Lincoln Park in Chicago, Illinois. All patient records from January 2020 through July 2020 were reviewed and identified to attain data recorded on the data collection sheet of patients who developed pressure ulcers during that time frame along with the same number of patients who did not develop pressure ulcers but were classified as at-risk as evidenced by a Braden score of 18 or less, resulting in a total of 119 adult long-term care residents. The general assumption from this analysis was that a logistic regression model of pressure ulcer development in long-term care residents indicated 9 predictors able to determine a statistically significant risk of pressure ulcer development. Specifically, the analysis suggested high risk Braden total scores (mean=15), history of pressure ulcer, anemia, limb paralysis, osteoporosis, malnutrition, incontinence, CHF, Alzheimer’s, and DM2 can be predictive of the development of pressure ulcers in long-term care residents. The analysis integrated a predictive model using binary logistic regression, which revealed that the Braden total score alone was accurately able to predict 75.6% (76.6% subjects that did develop pressure ulcers were accurately predicted and 74.5% of subjects which did not develop pressure ulcers were accurately predicted in the Braden score only model). Adding the presence of history of pressure ulcer, anemia, limb paralysis, osteoporosis, malnutrition, incontinence, CHF, Alzheimer’s, and DM2 was able to accurately predict 98.3% (96.9% with PU, 100% without PU). More research is needed to substantiate these findings and investigate contemporary risk assessment methods, with the ultimate objective of reducing the incidence of pressure ulcers
Data Science Methods for Nursing-Relevant Patient Outcomes and Clinical Processes The 2019 Literature Year in Review
Data science continues to be recognized and used within healthcare due to the increased availability of large data sets and advanced analytics. It can be challenging for nurse leaders to remain apprised of this rapidly changing landscape. In this article, we describe our findings from a scoping literature review of papers published in 2019 that use data science to explore, explain, and/or predict 15 phenomena of interest to nurses. Fourteen of the 15 phenomena were associated with at least one paper published in 2019. We identified the use of many contemporary data science methods (eg, natural language processing, neural networks) for many of the outcomes. We found many studies exploring Readmissions and Pressure Injuries. The topics of Artificial Intelligence/Machine Learning Acceptance, Burnout, Patient Safety, and Unit Culture were poorly represented. We hope that the studies described in this article help readers: (1) understand the breadth and depth of data science\u27s ability to improve clinical processes and patient outcomes that are relevant to nurses and (2) identify gaps in the literature that are in need of exploratio
Literature-Augmented Clinical Outcome Prediction
We present BEEP (Biomedical Evidence-Enhanced Predictions), a novel approach
for clinical outcome prediction that retrieves patient-specific medical
literature and incorporates it into predictive models. Based on each individual
patient's clinical notes, we train language models (LMs) to find relevant
papers and fuse them with information from notes to predict outcomes such as
in-hospital mortality. We develop methods to retrieve literature based on
noisy, information-dense patient notes, and to augment existing outcome
prediction models with retrieved papers in a manner that maximizes predictive
accuracy. Our approach boosts predictive performance on three important
clinical tasks in comparison to strong recent LM baselines, increasing F1 by up
to 5 points and precision@Top-K by a large margin of over 25%.Comment: To appear in Findings of NAACL 2022. Code available at:
https://github.com/allenai/BEE
England's Approach to Improving End-of-Life Care: A Strategy for Honoring Patients' Choices
Outlines England's evidence-based End of Life Care Strategy, its impact, and possible lessons for palliative care in the United States, such as the use of death at home as a metric for progress and Web-based training for clinical and caregiving personnel
An Exploratory Study of Patient Falls
Debate continues between the contribution of education level and clinical expertise in the nursing practice environment. Research suggests a link between Baccalaureate of Science in Nursing (BSN) nurses and positive patient outcomes such as lower mortality, decreased falls, and fewer medication errors. Purpose: To examine if there a negative correlation between patient falls and the level of nurse education at an urban hospital located in Midwest Illinois during the years 2010-2014? Methods: A retrospective crosssectional cohort analysis was conducted using data from the National Database of Nursing Quality Indicators (NDNQI) from the years 2010-2014. Sample: Inpatients aged ≥ 18 years who experienced a unintentional sudden descent, with or without injury that resulted in the patient striking the floor or object and occurred on inpatient nursing units. Results: The regression model was constructed with annual patient falls as the dependent variable and formal education and a log transformed variable for percentage of certified nurses as the independent variables. The model overall is a good fit, F (2,22) = 9.014, p = .001, adj. R2 = .40. Conclusion: Annual patient falls will decrease by increasing the number of nurses with baccalaureate degrees and/or certifications from a professional nursing board-governing body
Rehabilitation Therapy in Older Acute Heart Failure Patients (REHAB-HF) trial: Design and rationale.
BACKGROUND: Acute decompensated heart failure (ADHF) is a leading cause of hospitalization in older persons in the United States. Reduced physical function and frailty are major determinants of adverse outcomes in older patients with hospitalized ADHF. However, these are not addressed by current heart failure (HF) management strategies and there has been little study of exercise training in older, frail HF patients with recent ADHF.
HYPOTHESIS: Targeting physical frailty with a multi-domain structured physical rehabilitation intervention will improve physical function and reduce adverse outcomes among older patients experiencing a HF hospitalization.
STUDY DESIGN: REHAB-HF is a multi-center clinical trial in which 360 patients ≥60 years hospitalized with ADHF will be randomized either to a novel 12-week multi-domain physical rehabilitation intervention or to attention control. The goal of the intervention is to improve balance, mobility, strength and endurance utilizing reproducible, targeted exercises administered by a multi-disciplinary team with specific milestones for progression. The primary study aim is to assess the efficacy of the REHAB-HF intervention on physical function measured by total Short Physical Performance Battery score. The secondary outcome is 6-month all-cause rehospitalization. Additional outcome measures include quality of life and costs.
CONCLUSIONS: REHAB-HF is the first randomized trial of a physical function intervention in older patients with hospitalized ADHF designed to determine if addressing deficits in balance, mobility, strength and endurance improves physical function and reduces rehospitalizations. It will address key evidence gaps concerning the role of physical rehabilitation in the care of older patients, those with ADHF, frailty, and multiple comorbidities
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