Symptom control may become challenging for terminally ill patients as they near the end of life. Patients often seek hospital admission to address symptoms, such as pain, nausea, vomiting, and restlessness. Alternatively, palliative medicine focuses on the control and mitigation of symptoms, while allowing patients to maintain their quality of life, whether in an outpatient or inpatient setting. Hospice care provides, in addition to inpatient care at a hospice facility or in a hospital, the option for patients to receive symptom management at home. This option for symptom control in the outpatient setting is essential to preventing repeated and expensive hospital readmissions. This article discusses the impact of hospice care on hospital readmission rates.

Background: Globally, studies have established that 40-50% of psychiatric patients with SMDs are readmitted within one year of discharge from the acute hospital admission. Lowand middle-income countries (LMICs) such as Malawi have also reported high rates of psychiatric readmissions. This poses challenges when providing psychiatric care to patients. Most of Malawi`s health institutions, including Saint John of God Psychiatric Hospital (SJOG), rely primarily on donor funding. In order to maximise the available donor funding, there is a need to reduce readmissions resulting from modifiable or controlled factors. There are no studies in Malawi which have investigated these risk factors. The study aimed to establish the frequency of readmissions and the associated factors among patients at SJOG Psychiatric Hospital in Mzuzu, Malawi. The specific areas examined were sociodemographic and clinical-related factors associated with readmission. Methods: This was a retrospective cohort case record review study. Two hundred and seventy five clinical files of patients admitted for the first time at SJOG Psychiatric Hospital Mzuzu, Malawi between 1 January, 2014 and 31 December, 2015 were extracted. Data on socio-demographics and clinical information were collected using an extraction sheet at 3, 6 and 12 months post-discharge from the acute (first) hospital admission. Logistic regression models were developed to investigate the associations between socio-demographics, clinicalrelated factors and readmissions. Ethical approval for this study was granted by the Faculty of Health Sciences Human Research Ethics Committee at the University of Cape Town. Approval to conduct this research in Malawi was obtained from the National Health Sciences Research Ethics Committee. Results: Readmission rates of 1.5%, 4.4%, and 11.3% were found within the 3, 6 and 12 months of discharge from the acute hospital admission respectively. None of the independent variables predicted readmission within the 3 month of discharge from the acute hospital admission. In the unadjusted logistic regression model, having children (OR=0.26, 95% C.I 0.07-0.96) protected against readmissions within the 6 month of follow-up period. In the unadjusted logistic regression model, having children (OR= 0.40, 95% C.I 0.18-0.88), staying outside the hospital catchment area (OR=0.44, 95% C.I 0.20-0.96), and having insight (OR=0.22, 95% C.I 0.10-0.49) into their illness were protective factors to readmission, while taking SGAs (OR=4.67, 95% C.I 1.33-16.39) predicted readmission within the 12 month follow-up period. After adjusting for age and gender in the multivariable analysis, staying outside catchment area (OR=0.33, 95% C.I 0.14-0.79) and having insight (OR=0.19, 95% C.I 0.08-0.46) to their illness were protective factors, while taking SGAs (OR=5.29, 95% C.I 1.43-19.51) remained a predictor of readmission within 12 months of discharge from the acute admission. Conclusion: The findings of this study demonstrated that readmissions are associated with socio-demographic and clinical factors such as catchment area, patient insight into their condition and type of antipsychotics. The study identifies the need to develop interventions targeting the groups at risk of being readmitted.

INTRODUCTION: Unplanned hospital visits have emerged as a quality metric encompassing many aspects of postoperative morbidity and deficiencies in the transition from inpatient to outpatient care. This study aims to identify patient, encounter, and organizational factors that may influence Emergency Department (ED) visits and readmissions following bariatric surgery. METHODS: A modified version of a framework initially proposed by Vest et al. in their systematic review of the determinants of preventable readmissions was used as a conceptual framework for this study. The Michigan Bariatric Surgery Collaborative (MBSC) database was used to identify patients undergoing all primary bariatric procedures at 40 centers with >100 patients in the database from 2006–2015. Multivariate logistic regression modeling was used to identify factors associated with unplanned hospital visits. Using an indirect standardization process, each sites’ observed to expected ratio for 30-day readmission was calculated. The association between each site’s adjusted readmission rate with their rate of ED visits, Emergency Department-Sourced readmissions (EDSR), major complications, and compliance with best practices were calculated with Pearson’s correlation coefficients. RESULTS: Younger age, greater comorbidities, increased length of stay, procedure type, and Medicaid/Medicare insurance were significantly associated with readmissions in a multivariate logistic regression model. There was significant variation among sites’ adjusted rates of readmission, EDSR, best practice compliance, and major complications. There was a moderately strong association between each sites’ adjusted readmission rate with their rate of EDSR (r=0.53), major complications (r=0.53), and ED visits (r=0.55). However, the association between bariatric centers’ compliance with best practices to reduce unplanned hospital visits and their readmission rates was fairly weak (r= -0.14). CONCLUSION: Several individual, encounter, and organization-level characteristics are associated with an increased risk of unplanned visits after bariatric surgery. Bariatric centers are more likely to have higher readmission rates if their site has higher rates of major complications and if their ED is less likely to treat and then discharge bariatric patients. Further examination of organizational characteristics of bariatric programs that affect postoperative readmissions, including ED practices, is needed to better guide future initiatives aimed at improving this quality metric.

Chronic obstructive pulmonary disease (COPD) signifies a significant public health challenge that is both avoidable and treatable. There was no standardized education offered to the COPD population at the practice location. The scope of the project encompassed standardizing education by developing a self-management packet for the COPD patients. The goal of this project was to examine how the development of a standardized COPD self-management packet enhances the quality of care and strategizes reducing 30-day readmissions compared to nonstandardized delivery of education. Orem's self-care theory and Bandura's self-efficacy concept were used to explain the principle of self-management, while Rosswurm and Larrabee's evidence-based practice model was used to guide practice change. The U.S. Prevention Service Task Force's level of evidence hierarchy was chosen to categorize the strengths and weaknesses of the evidence referenced for this project. Postdevelopment surveys using the Likert scale were distributed to the facility's COPD committee, and a 70% response rate of strongly agreed to all questions was achieved. There were no adverse responses, and the packet was approved unanimously. Based on the positive responses, the packet will be easily adapted and beneficial in practice. The recommendation is to pilot the packet on the medical-surgical unit and follow-up postdischarge with phone calls to ascertain patients' perspective of the packet. Utilization of the education packet will lead to positive social change by affording the stakeholders self-management awareness and positive outcome measures including reducing the COPD 30-day readmission rate, curtailing economic strains, and promoting positive patient-centered relationships.

Heart failure (HF) readmissions create a financial burden for healthcare nationwide and speak to the lack of effective discharge preparation for patients to be successful with self-care at home. The 183-bed hospital where this DNP quality initiative will take place currently reports an observed-over-expected (O/E) readmission rate for HF patients (Centers for Medicare and Medicaid [CMS]). Core measures on HF developed by the Joint Commission and the Centers for Medicare and Medicaid Services do not appear to be enough to ensure successful transitions of care from hospital to home. Guided by the LOGIC model, the purpose of this quality improvement initiative was to develop a HF educational module to improve patients' readiness to learn in order to promote self-care and prevent readmission to the hospital within 30 days. The design of the educational program was supported by the evidence-based literature and incorporated best practices promoted by the Joint Commission, the Institute for Healthcare Improvement, and the Agency for Healthcare Research and Quality. Content evaluation of the newly developed HF educational program was conducted by 10 experts using a quantitative Likert-type scale and qualitative narrative feedback. Descriptive findings from the Likert scale showed a range of 3.9 to 4.0 in the content, process, and design of the program. Recommendations for improvement included more detail around pathophysiology, as well as how to initiate the process in the outpatient setting. Positive social change can result from the program which offers a relevant strategy to reduce readmissions for HF and has wide-application options for many chronic illnesses that can be better managed through effective discharge teaching.

With the increasing incidence and prevalence of heart failure in the United States, the burden imposed on both the patients and healthcare system is becoming increasingly difficult to manage. In 2009, the American Heart Association estimated that there were 15 million office visits and 6.5 million days spent in the hospital due to heart failure. Complex regimens requiring lifestyle alterations make it difficult for patients to adhere to provider recommendations. In an attempt to decrease this burden, the effects of home telemonitoring have been under investigation. An integrated review of the literature was conducted to summarize findings from studies investigating whether home telemonitoring increases patient adherence to prescribed therapy while reducing hospital admissions and readmissions. The search included relevant studies from 2005 to 201 0. A total of eleven studies were reviewed in this thesis. Statistically significant improvements were found in four of the five articles regarding patient adherence. Two of the articles reporting on hospital admissions indicated statistically significant reductions. while the others showed a trend towards a reduction in hospitalizations. Statistically significant decreases were found in hospital readmissions in three of five studies. While the evidence was not strong enough to support broad-scale implementation, telemonitoring can be recommended as a viable adjunct to usual care for some patients. Nurses should be at the forefront for providing patient education and assessing data provided by telemonitoring equipment. Large-scale. replicable studies are recommended to further determine the effectiveness of telemonitoring related to outcomes of heart failure patients.

An early hospital readmission means that a newly discharged patient is readmitted within a small time frame (< 30 days) due to reasons directly related to the original admission. This generally runs the risk of negatively impacting both the wellbeing of the patient in question as well as the hospice care unit admitting the patient economically. Being able to use modern computational tools to predict which patients run a large risk of soon becoming admitted once more either prior to or during their discharge can help in the task of preventing these incidents altogether. During this study, 65 different machine learning models were trained on a dataset assembled using metrics from 130 American hospitals over a 10-year period. While the dataset is specialised on patients affected by diabetes, the study also presents generalized models trained on a version of the dataset free from attributes unique to patients affected by diabetes. Several of these models are trained using methods specifically designed to counter an inherent class imbalance issue present within the chosen problem domain. The study results in the presentation of several performance related metrics of the trained models, including AUC scores and an approximation of the early readmission cost per patient predicted using the different models. Lastly, the study concludes with some examples of potential alternative methods that may further evolve the performance of the models designed for this task as well as a discussion regarding the ethics of deploying such a solution in the real world.

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