The outpatient care of the heart failure (HF) patient is fragmented due to the lack of evidence-based practice guidelines use. The primary goal of this project was to improve the care of the HF patient in the outpatient arena through use of clinical pathways using the logic model as the project framework. The intervention was carried out over a 4-week period on a convenience, random sample of patients (n = 80) attending a cardiology practice. The patients were recruited from 2 physicians' patient populations and were selected based on an adult diagnosis of HF, reduced ejection fraction of <40% at some point in time, and the New York Heart Association (NYHA) functional class II-V. Comparisons were made in the documentation of care between patients on or off the pathway. The intervention included documentation of patient education, care follow-up, medications, NYHA functional class, and symptom exacerbation, documented in the electronic medical record. The quality of care data were evaluated based on 3 of the Joint Commission core measures for outpatient care of the HF patient. Additional data were collected regarding use of the clinical pathway based on provider and week of implementation. Data were analyzed via a Chi-square test of independence comparing pathway use by provider and use of pathway as study progressed. The comparative results show statistically significant differences in use of the pathway by provider and a statistically significant increase in use during the project . The quality of care results varied in statistical significance. The pathway utilization increased over time and provided a method for standardizing documentation of care for the HF patient in this outpatient clinic, a benefit for HF patients and providers in this cardiology practice and beyond.

Hospital readmissions related to chronic heart failure (CHF) are costly, widespread, and often avoidable. Patient education that includes diagnosis, causes, medications, diet, exercise, and exacerbation warning signs has been shown to reduce the number of CHF readmissions. The purpose of this study was to use risk stratification to identify CHF patients at high risk for 30-day readmission. Once a high-risk CHF patient was identified, nursing interventions would be triggered to reduce readmissions and close the gaps in the continuum of care following acute care admission. Transitions of care theory was used as the framework for this project. The methodology had a quality improvement focus. The patient population consisted of high-risk CHF patients (n = 25) with NYHA classification of II-IV using the risk identification tool. Patients were identified using the tool, were followed for 30 days, and received nursing interventions to reduce the possibility of readmission. Only one of the identified patients was readmitted within 30 days for a diagnosis unrelated to CHF, resulting in no readmissions within this sub group. This study suggests that risk stratification can identify and direct resources to CHF patients, decreasing their likelihood for readmission. Nurse leaders can use standardized tools such as the risk identification tool, thereby reducing readmissions along with associated costs for readmissions.

In elderly women, discharge after gynecologic surgery is often associated with increased morbidity. Little information exists about elderly women's discharge destination after gynecologic surgery and the outcome of early hospital readmission. The purpose of this study, conceptualized using the quality health outcomes model, was to examine whether post hysterectomy discharge destination is an independent predictor of 30-day hospital readmission in women age 65 and older. Examination of covariates included patient age, race, medical comorbidity and complications of care, as well as surgical anatomic approach and operative technique. This study involved use of a retrospective cohort design and data from 10,598 cases contained in the Healthcare Cost and Utilization Project 2010 and 2011 California State Inpatient Databases. Results of the bivariate analysis showed a statistically significant association between discharge destination after hysterectomy and 30-day hospital readmission. Additionally, the results of multivariate logistic regression revealed the odds of readmission after discharge with home care were 2.99, p < .001, 95% CI [2.29, 3.67] times greater when compared with discharge home for self-care and 5.99, p < .001, 95% CI [4.68, 7.43] times greater with discharge to continuing inpatient care versus home for self-care. This study may lead to positive social change for elderly women by informing health care providers about the odds of early hospital readmission associated with discharge destination after hysterectomy. Further, this information may stimulate development of interventions to improve health care practices for elderly women preparing for hospital discharge after hysterectomy.

Magister Curationis - MCur / The frequent readmission of patients in psychiatric hospitals is caused by the relapse in their different psychiatric conditions. With a shortage of professional nurses, lack of resources, and an ever-increasing workload, the frequent readmission of psychiatric patients further worsens the situation.

Thesis (Ph. D.)--University of Wisconsin-Madison, 2002. / Includes bibliographical references (p. 240-252). Also available on the Internet.

Infant health is an important comprehensive measure of the health in a society. Experiences during infancy can create durable and heritable patterns of social deprivation and illness ultimately producing health disparities in a population. This thesis sought to determine the relationship between maternal circumstances and infant mortality, morbidity and congenital anomaly rates in Winnipeg, Manitoba, Canada. Using logistic regression models the study explored provincial program screening data and administrative data held. The study found higher rates of congenital anomalies within two parent families and male infants. There was a relationship between hospital readmission rates and social and economic factors. Newborn hospital readmissions were associated with social support factors, while post-neonatal hospital readmissions were associated with contextual factors. Understanding the odds of infant mortality, morbidity and congenital anomaly in relation to different maternal socioeconomic factors may contribute to future health planning and the development of interventions that can improve health equity. / October 2015

Thesis (Ph. D.)--University of Wisconsin-Madison, 2002. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (p. 240-252).

Context. Re-admission is the return hospitalization within 30 days from the date of original admission or discharge from hospital. Thecosts of the unplanned re-admissions were estimated to $25 billion per year alone in the U.S. Re-admission rate also has a huge impact onquality of care provided to the patients, cost of health care, and utilization of hospital resources and the image of the care provider. Studies indicate huge potential of savings that can be achieved with incremental performance improvements in detecting cases of preventable re-admissions. Objectives. In this study we find the different features that helpin predicting readmissions, compare different machine learning techniques and build a model to predict readmissions using one technique.We also propose a framework for implementation of this model in the real world situations. Methods. To reach the objective, the data of the patients over a period of time were studied to determine the factors that help in identifying re-admissions. Experiments are performed for identifying the features that are more relevant to predict re-admissions and for investigating the most suitable machine learning techniques for this purpose.This model was tested to predict re-admission cases for Acute Myocardial Infarction and Pneumonia. Results. The features that help in predicting re-admissions are determined,and a model was developed using these features and the selected machine learning algorithm. The model showed good results in predicting re-admissions. The model predicted risk of Acute Myocardial Infarction(c-statistic=0.811), and Pneumonia(c=0.76). Conclusions. We conclude that our model showed good results in predicting re-admissions. The developed model is discriminative for specific diseases like Acute Myocardial Infarction and Pneumonia. Itis also generalized as it incorporates the features that can be easily available from all of the patient population over the globe.

Class of 2013 Abstract / Specific Aims: To examine the relationship between hospital readmission rates and responses to a medication adherence questionnaire (Morisky) in patients with congestive heart failure (CHF). Methods: The Morisky questionnaire, assessing medication adherence, was administered to all CHF patients admitted from September 15, 2012 to March 7, 2013. Information collected from the electronic medical record (EMR) for all patients with complete Morisky questionnaires included: age, sex, ethnicity, insurance, height, weight, marital status, tobacco use, alcohol use, number of home medications, all-cause and CHF admission in the previous 365 days from when the questionnaire was administered as well as the following events/disease states: myocardial infarction, hypertension, atrial fibrillation, stroke, diabetes mellitus, peripheral vascular disease, chronic obstructive pulmonary disease, congestive heart disease and chronic kidney disease. Main Results: Of the 120 patients enrolled, 52% scored 1-5 on the Morisky questionnaire indicating some problem with medication adherence while 48% scored 0 (no problems). There was no correlation between the Morisky score and age (95% CI: -3.3-5.7), number of medications (95% CI: -0.26, 2.85), or number of comorbidities (95% CI: -1.02,0.03). The Morisky questionnaire was not predictive of all cause readmissions (95% CI: 0.35, 2.01) p = 0.691). For CHF readmissions the Morisky score was not significant (95% CI: 0.6, 4.11, p=0.358) but the confidence interval suggests a trend. Conclusion: There is no correlation between Morisky scores, age, comorbidities, and medication number. Readmission rates were not predicted by Morisky scores; with more participants a trend may be detected for CHF readmissions.

Background: Diastolic function (DF) guidelines have been simplified but lack extensive outcome data. Using a rural university heart failure (HF) database, we assessed whether DF grading could predict HF, HF readmission, and all-cause mortality (ACM). Methods: In this single-center retrospective study that included 613 patients in sinus rhythm hospitalized for HF (HF with preserved—254 patients, with mid-range—216 patients, and reduced ejection fraction—143 patients), we recorded demographics, Doppler-echo, Framingham HF score, laboratories, HF readmission, and ACM with follow-up to 2167 days. Results: Diastolic dysfunction (Ddys) parameters (left atrial volume index [LAVI] > 34 ml/m2, tricuspid regurgitation [TR] velocity > 2.8 m/sec, and E/e’ > 14) had moderate sensitivity (46.2%–65.0%) for predicting HF among all phenotypes combined with DF grading having moderate predictability and additive to a clinical composite for HF prediction (AUC =.677, P < 0.0001; difference =.043, P < 0.001) for combined phenotypes. Ddys parameters and Ddys severity (2016 ASE criteria: grade II and III) were significantly associated with HF readmission for decompensated HF within 60–2167 days of follow-up (LAVI > 34 ml/m2: HR 1.56 [1.26–2.19]; E/e’ > 14: HR 1.44 [1.21–1.99]; TR > 2.8 m/sec: H1.43 [1.19–1.88]; LV Dys grade II: HR 2.12 [1.42–2.96]; LV Ddys grade III: HR 2.39 [1.57–4.82]). Conclusion: The findings of this study highlight the clinical and prognostic relevance of determining the severity of LV Ddys in patients with HF with regard to HF verification and HF readmission.