In an effort to reduce hospital 30-day readmissions a Transitional Care Coach Program (TCCP) was developed in 2014 at a Southwest Urban Medical Center. The CDC Framework for Program Evaluation (2012) applies insight and experience gained from past program experience to effect change in practice and improve patient outcomes. The evaluation seeks to determine TCCP utilization, to assess its impact on 30-day readmission rates for high-risk patients, to inform stakeholders of a viable follow-up program, and to determine evidence-based interventions for program improvement. This TCCP program evaluation describes characteristics of patients who participated in the program, assesses whether interventions were delivered as intended, and determines if interventions reduced hospital 30-day readmission rates compared to readmission rates prior to program implementation. Descriptive statistics are used to describe the patient population, health status, and program utilization. For the diagnoses of acute myocardial infarction (AMI), chronic obstructive pulmonary disease (COPD), heart failure (HF), and pneumonia (PNA), Chi-square test analyses were performed to compare 30-day readmission rates of the TCCP participants and readmission rates for this medical center for the time period prior to program implementation. The primary finding of this program evaluation is an overall numerical decrease in hospital readmission rate by 3% compared to the baseline data. Although the change (a decrease) was in the desired direction, the degree of change was not statistically significant based on pooled data. A statistically significant decrease was observed only for the AMI diagnosis. However, as any decrease in readmissions decreases the financial burden to both the organization and the patient, the TCCP appears to have had a positive impact. It is recommended that a renewed TCCP be conducted to allow for (1) an increased timespan for data collection, (2) an increased number of medical categories assessed to allow for more non-parametric statistical analysis (e.g. adding categories of Total Joint Replacement and Sepsis diagnoses), (3) tracking of number of days to readmittance to allow for improvement to be measured and analyzed beyond a single dichotomous category. Evidence-based recommendations have been made to continue and improve interventions that further reduce hospital readmissions.

The transition from hospital to home or alternate care setting is a time of vulnerability for all patients and particularly for our elders. If not handled appropriately there is a risk to our elders for readmission to the hospital environment that may decrease their overall quality of life and further compromise their health status. in addition to the individual risks associated with patient readmissions, there are societal impacts that reach far beyond our current generation of elders 65 and older. This impact may have dire implications for the future fiscal health of the next generation. a review of the current and past literature shows that there are a limited number of resources available for hospitals to use in order to comply with the new Value Based Purchasing initiatives that are being implemented by CMS regarding the reduction in readmission rates. the problem of hospital readmissions is confounded by the many processes that are available for study, from pre-hospitalization conditions and care through hospitalization, discharge, and finally to post discharge processes. While most research and literature reviews have focused on individual disease causes, there is a need to provide hospitals with a resource that outlines the available options and interventions that have been shown to be effective in reducing hospital readmissions. the purpose of this study is to review relevant literature related to the problem of hospital readmissions for our elder population. This study is designed to look at interventions, both disease based and non-disease based, that have been previously implemented and have shown effective reductions in readmission rates. This analysis and synthesis can provide an important contribution to our understanding of the factors and variables that influence the readmission rates of our elder population. This review has the potential to assist and direct hospital administrators and to discharge planners, social workers, and other health professions to implement intervention strategies that promote the continuing health status of our elder population while reducing their overall rates of readmissions.

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.

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.

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

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.

An early hospital readmission means that a newly discharged patient is readmitted within a small time frame (&lt; 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.

Unplanned hospital readmission after a recent hospitalization is an indication of poor healthcare quality and a waste of healthcare resources. The Centers for Medicare and Medicaid Services (CMS) initiated the Hospital Readmission Reduction Program (HRRP) to improve healthcare quality and reduce costs; however, studies found the risk adjustment method used in calculating the standardized readmission rate was less accurate without hospital region or community factors. Accordingly, this cross-sectional quantitative study was designed to examine spatial patterns in hospital readmission rates following Andersen's behavioral model of health service utilization. This study was the first geospatial analysis on risk standardized hospital readmissions (RSRR) based on hospital geographic locations. Secondary data from the CMS was used in assessing the global and local geospatial cluster patterns using Global Moran's Index, Anselin local Moran's Index, and graphical analysis tool to identify cluster groups. The study found hospital-wide RSRR was significantly clustered across the country or at the local level. A total of 15 optimal cluster groups were identified with wide variability in cluster size. The hospital-wide and other seven CMS published RSRRs were significantly different among all clusters. The geographically bounded hospital RSRRs provided evidence in support of adding community or regional layer to risk adjustment of RSRR. The specific cluster groups with extremely high or low readmission rates can assist national and local policymakers and hospital administrators to identify specific targets to take actions. This research has social change implications for reducing hospital readmission rates and saving healthcare costs.

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