Engaging Patients to Improve Documentation of Oral Intake on a Cardiac Telemetry Unit: A Quality Improvement Initiative

Background InformationIt is important for patients with heart failure to have awareness of their intake & output to effectively manage their disease. There is evidence that tracking intake & output is a component of missed nursing care resulting in discrepancies between the actual patient intake and what is documented in the patient’s electronic health record (EHR). Aim The aim of this quality improvement project was to engage patients in monitoring their intake by using teach-back and patient engagement techniques to track their own oral fluid intake throughout the day. MethodsThe Plan-Do-Study-Act (PDSA) model was used as the framework for this initiative. Patients meeting inclusion criteria were given a teach-back quiz to evaluate baseline knowledge. If patients were able to pass the teach-back quiz, they were given a tracking sheet with instructions on how to use it. After a period of eight hours, the sheet was collected and fluid intake volumes were compared with those documented in the EHR. ResultsUsing the Wilcox on non-parametric test, the mean difference between volume tracked by patient and volume documented by clinician was significant at pConclusion & Implications for CNL PracticeVariation between oral fluid intake volume documented in the EHR and patient stated volumes indicates that EHR documentation is less reliable than records kept by adequately educated and engaged patients. Implications for CNL practice include identification of opportunities to increase patient engagement and to utilize evidence-based techniques for this purpose. The CNL should explore barriers that contribute to inaccuracy of documentation. The CNL may explore more reliable methods for determining accurate patient fluid balance for at-risk populations

Palliative care needs in patients hospitalized with heart failure (PCHF) study: rationale and design

Abstract Aims The primary aim of this study is to provide data to inform the design of a randomized controlled clinical trial (RCT) of a palliative care (PC) intervention in heart failure (HF). We will identify an appropriate study population with a high prevalence of PC needs defined using quantifiable measures. We will also identify which components a specific and targeted PC intervention in HF should include and attempt to define the most relevant trial outcomes. Methods An unselected, prospective, near-consecutive, cohort of patients admitted to hospital with acute decompensated HF will be enrolled over a 2-year period. All potential participants will be screened using B-type natriuretic peptide and echocardiography, and all those enrolled will be extensively characterized in terms of their HF status, comorbidity, and PC needs. Quantitative assessment of PC needs will include evaluation of general and disease-specific quality of life, mood, symptom burden, caregiver burden, and end of life care. Inpatient assessments will be performed and after discharge outpatient assessments will be carried out every 4 months for up to 2.5 years. Participants will be followed up for a minimum of 1 year for hospital admissions, and place and cause of death. Methods for identifying patients with HF with PC needs will be evaluated, and estimates of healthcare utilisation performed. Conclusion By assessing the prevalence of these needs, describing how these needs change over time, and evaluating how best PC needs can be identified, we will provide the foundation for designing an RCT of a PC intervention in HF

Assessing Prevalence of Known Risk Factors in a Regional Central Kentucky Medical Center Heart Failure Population as an Approach to Assessment of Needs for Development of a Program to Provide Targeted Services to Reduce 30 Day Readmissions

Abstract Objectives: Determine demographic, physiologic, and laboratory characteristics at time of admission of the heart failure (HF) population in a regional acute care facility in Central Kentucky through review of patient electronic medical records. Determine which HF population characteristics are significantly associated with readmissions to the hospital. Provide identification of the statistically significant common characteristics of the HF population to this facility so that they may work towards development of an electronic risk for readmission predictive instrument. Design: Retrospective chart review. Setting: Regional acute care facility in Central Kentucky. Participants: All patients (n = 175) with a diagnosis or history of HF (to include diagnosis related group (DRG) codes 402.01, 402.11, 402.91, 404.01, 404.03, 404.11, 404.13, 404.91, 404.93, 428.1, 428.41, 428.23, 428.43, 428.31, 428.33, 428.1, 428.20, 428.22, 428.30, 428.32, 428.40, 428.40, 428.42, 428.0, and 428.9; The Joint Commission, 2013) admitted to the acute care setting of a regional hospital in the Central Kentucky area between the dates of January 1, 2013 and July 31, 2013. Eligible participants were identified via an electronic discharge report listing all patients discharged during the study time period with a HF code. Main Outcome Measure: A chart review was performed to define the HF population within the regional acute care facility. Abstracted information was collected on data instruments (Appendices A,B, and C) and analyzed to define the overall HF population (n = 175). The data was then analyzed to determine significance between patient characteristics (demographic, physiologic, and laboratory) and 30 day readmissions. The data was examined both on the individual patient level and independent of patient level looking at each admission independently. Results: An in depth description of the HF patient population in this facility was obtained. Several patient characteristics including a history of anemia, COPD, ischemic heart disease, diabetes, and the laboratory values creatinine and BNP outside of the reference range were found to have a significant association with 30 day readmissions. Discharge to a skilled nursing facility (SNF) was also found to be a significant predictor of 30 day readmissions. Some social variables such as marital status were not found to have a significant relationship to 30 day readmissions. Conclusion: This investigation is a stepping stone to creating an electronic tool designed to reflect the characteristics of HF population admitted to a single facility and predict risk of HF readmissions within 30 days at the time of admission. Implementation of a plan of care designed to meet the needs of this HF population as well as identify those patients at high risk for will allow for provision of a comprehensive and timely individualized plan of care to reduce the incidence of 30 day readmissions

Achieving the Potential of Health Care Performance Measures: Timely Analysis of Immediate Health Policy issues

The United States is on the cusp of a new era, with greater demand for performance information, greater data availability, and a greater willingness to integrate performance information into public policy. This era has immense promise to deliver a learning health care system that encourages collaborative improvements in systems-based care, improves accountability, helps consumers make important choices, and improves quality at an acceptable cost. However, to curtail the possibility of unintended adverse consequences, it is important that we invest in developing sound measures, understand quality measures' strengths and limitations, study the science of quality measurement, and reduce inaccurate inferences about provider performance

Improving Health Care Provider Knowledge when Discharging Patients with Substance Abuse: A Quality Improvement Project

ANNOUNCEMENT Florida International University Nicole Wertheim College of Nursing & Health Sciences Doctor of Nursing Practice Project Presentation Abstract Improving Health Care Provider’s Knowledge when Discharging Patients with Substance Abuse. By Jacqueline Ustache For many substance abuse disorder (SUD) patients, the transition from psychiatric facilities to community settings is challenging. It can influence their mental health outcomes, continuity of care, and adjustment to community life. The first few weeks after discharge represents a crucial period, as difficulties can arise in their daily lives, such as difficulties in coping with symptoms, poor medication adherence, stigmatization, low self-esteem, loneliness, anxiety, craving, and suicidal ideation. A systematic review was conducted to determine which screening tool and cutoff score, if any, is best suited to decrease readmissions rates and prevent relapsed when discharging patient with substance abuse. A literature search was completed and only those studies that met the following inclusion criteria were included: published in the English language within the last five years, reported studies, systematic reviews, narrative reviews about discharge protocols or interventions for SUD patients, and included interventions involving both pre and post-discharge components. On these articles, two reported on the accuracy of forms of the CAGE Questionnaire and 3 studied the accuracy of forms of the Addiction Severity Index (ASI). Results were replicated across studies and the (ASI) showed good accuracy, and was widely used in the evaluation of SUD patients during discharge. This instrument was used to guide the development and implementation of the educational intervention program. The results of the systematic review were used in the development of a QI project to increase HCP’s knowledge when assessing patients with substance abuse at discharge. The literature review led the foundation for development of the pre/posttest questionnaire and educational intervention. An invitation letters was sent via email to all participants, informed consent was signed, and the components were completed virtually. The average scores on the pre/posttest were compared and improved overall after the completion of the educational intervention. Consequently, it was determined that knowledge of proper and informed discharge by HCP is inevitable not only to reduce cases of relapses and re admissions, also to improve patients quality of life. Date: June 29th, 2021 Department: Graduate Nursing Time: 1:30 PM EST Lead Professor: Dr. C. Buscemi Place: Zoom Clinical Instructor: Dr. F. Arma

Extensions and Applications of Ensemble-of-trees Methods in Machine Learning

Ensemble-of-trees algorithms have emerged to the forefront of machine learning due to their ability to generate high forecasting accuracy for a wide array of regression and classification problems. Classic ensemble methodologies such as random forests (RF) and stochastic gradient boosting (SGB) rely on algorithmic procedures to generate fits to data. In contrast, more recent ensemble techniques such as Bayesian Additive Regression Trees (BART) and Dynamic Trees (DT) focus on an underlying Bayesian probability model to generate the fits. These new probability model-based approaches show much promise versus their algorithmic counterparts, but also offer substantial room for improvement. The first part of this thesis focuses on methodological advances for ensemble-of-trees techniques with an emphasis on the more recent Bayesian approaches. In particular, we focus on extensions of BART in four distinct ways. First, we develop a more robust implementation of BART for both research and application. We then develop a principled approach to variable selection for BART as well as the ability to naturally incorporate prior information on important covariates into the algorithm. Next, we propose a method for handling missing data that relies on the recursive structure of decision trees and does not require imputation. Last, we relax the assumption of homoskedasticity in the BART model to allow for parametric modeling of heteroskedasticity. The second part of this thesis returns to the classic algorithmic approaches in the context of classification problems with asymmetric costs of forecasting errors. First we consider the performance of RF and SGB more broadly and demonstrate its superiority to logistic regression for applications in criminology with asymmetric costs. Next, we use RF to forecast unplanned hospital readmissions upon patient discharge with asymmetric costs taken into account. Finally, we explore the construction of stable decision trees for forecasts of violence during probation hearings in court systems

Prediction Screening to Identify Heart Failure Patients at High Risk for Readmission

Background: There is an increased need to identify factors associated with higher risk for excessive HF re-hospitalizations due to hospitals receiving financial penalties related to these re-hospitalizations and poorer patient outcomes. Identifying HF patients at highest risk for re-hospitalization with a screening instrument upon admission to the hospital would allow for early implementation of interventions tailored around reducing risk factors for re-hospitalization. Objectives: The specific aims of this study were to 1) identify characteristics that were predictive of HF re-hospitalization; and 2) use those characteristics to create a screening instrument. Methods: A total of 158 patients (age=63±13; 50.6% female; 73.4% Caucasian; 63.3% NYHA class III/IV) admitted with a primary or secondary diagnosis of HF were included in this study. Patient’s knowledge of HF symptoms, along with socio-demographic, biophysical, and cognitive information was assessed by data collected with validated instruments as well as the electronic medical record. Chi square tests and independent t-tests were used to examine bivariate differences in the readmitted and the non readmitted groups. Cox proportional hazards modeling was used to predict the outcome, or time to hospitalization, based on the predictor variables. Results: The mean time to re-hospitalization was 68 days. Only 8 patients were re-hospitalized within the first 30 days. Depressive symptoms scores was the only variable identified as being significantly different (p Conclusions: Screening HF patients at highest risk for re-hospitalization and those with depressive symptoms will allow healthcare providers to individualize interventions to improve HF patient outcomes and reduce costly hospital re-hospitalizations

Nutritional interventions for heart failure patients who are malnourished or at risk of malnutrition or cachexia: a systematic review and meta-analysis

Malnutrition is common in heart failure (HF), and it is associated with higher hospital readmission and mortality rates. This review aims to answer the question whether nutritional interventions aiming to increase protein and energy intake are effective at improving outcomes for patients with HF who are malnourished or at risk of malnutrition or cachexia. Systematic searches of four databases (Medline, Embase, CINAHL and Cochrane Central Register of Controlled Trials (CENTRAL)) were conducted on 21 June 2019. Randomized controlled trials (RCTs) or other interventional studies using protein or energy supplementation for adult HF patients who are malnourished or at risk of malnutrition or cachexia were included. Two independent reviewers assessed study eligibility and risk of bias. Five studies (four RCTs and one pilot RCT) met the inclusion criteria. The majority of studies were small and of limited quality. The pooled weighted mean difference (WMD) for body weight showed a benefit from the nutritional intervention by 3.83 kg (95% confidence interval (CI) 0.17 to 7.50, P = 0.04) from three trials with no significant benefit for triceps skinfold thickness (WMD = - 2.14 mm, 95% CI - 9.07 to 4.79, P = 0.55) from two trials. The combination of personalized nutrition intervention with conventional treatment led to a decrease in all-cause mortality and hospital readmission in one study. Findings of this review suggest that nutritional interventions could potentially improve outcomes in HF patients who are malnourished or at risk of malnutrition. However, the strength of the evidence is poor, and more robust studies with a larger number of participants are needed

Predicting risk: developing and testing of a nomogram to predict hospitalisation in chronic heart failure (CHF- Risk Study)

Chronic heart failure (CHF) is the leading cause of hospital admission in the elderly. Currently, no absolute risk model for rehospitalisation exists. The CHF-Risk Study was a 3 phase study that led to the development of a nomogram using a derivation cohort of a contemporaneous Australian CHF population. Factors associated with an increased risk of cardiovascular rehospitalisation were: age; living alone; a sedentary lifestyle and the presence of multiple co-morbid conditions