Reducing hospital readmissions is critical to the success and sustainability of both hospitals and the communities in which they reside. The purpose of this multiple case study was to explore organizational strategies hospital leaders use to reduce hospital readmissions. The study was limited to hospitals in Southwest Missouri with readmission rates below the state average. Complex adaptive systems was the conceptual framework for the study because of the complex nature and numerous stakeholders of the healthcare system. Data were collected from a purposive sample of 15 hospital leaders via semistructured interviews and an analysis of organizational artifacts. Member checking was used to increase reliability and validity of the results. Data analysis was conducted using Yin's 5 step process including qualitative analysis software to identify major and core themes. The major themes identified in the study included population health, hospital operations and patient interactions, leadership and mission, and barriers to reducing readmissions. The implications for positive social change include the potential to improve services hospital team members provide to patients, which may improve the overall health of the communities they serve. By promoting improved health outcomes for local communities, society benefits through reduction of costs to the federal government and an overall improvement in the health of communities.

Heart failure (HF) patients are among the populations with the highest rates of hospital readmission within 30 days of discharge. Because of the 2010 Health Care Reform legislation, healthcare organizations are subject to financial penalty when a patient population exhibits excess readmissions. A significant reason for readmission of HF patients is a gap in the transition of care from hospital to home. The purpose of this doctoral project was to develop a practice guideline of best practices for transitioning HF patients from hospital to home. The transitional care model and care transitions intervention provided the theoretical underpinnings for developing this project. The research question explored whether a transition-of-care guideline would reduce hospital readmission for the HF population. The methodology used to develop the clinical practice guideline was derived from a synthesis of scholarly literature and evidence-based transitional care quality initiatives. Seven interdisciplinary experts involved in HF transition of care used the Appraisal of Guidelines Research and Evaluation II instrument (AGREE II) to assess the development of the practice guideline. The scores of 6 AGREE II domains were summed and scaled to obtain a percentage of the maximum possible score for each domain. Scores showed that the clinical practice guideline was rigorous, high quality, effective in improving transition of care, and has the potential to reduce HF readmission. Positive social changes resulting from this practice guideline include an improvement in patient outcomes, a reduction in readmission rates, and a reduction in the associated financial burden to the hospital.

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.