Readmission risk prediction for patients after total hip or knee arthroplasty

Cybersecurity intelligence sharing (CIS) has the potential to help organisations improving their situational awareness. Although CIS has received more attention from organisations, participation in CIS operation is not satisfactory, and there is not too much information about the factors that are antecedent to CIS among organisations . Thus, this study aims to investigate technical and non-technical factors including organisational and environmental factors influence organisational participation in CIS practices

Potentially avoidable hospitalisations in Australia: causes for hospitalisations and primary health care interventions

The Australian Institute of Health and Welfare (AIHW) described potentially avoidable hospitalisations (PAHs) as “admissions to hospital that could have potentially been prevented through the provision of appropriate non-hospital health services”. The AIHW classify PAHs into three main types: vaccine-preventable, chronic, and acute conditions. In 2009-10, PAHs related to chronic conditions were the most common, due mainly to the high rates of hospitalisations for diabetes complications (24% of all PAHs). Moderately high rates of PAHs were also reported for chronic obstructive pulmonary disease (COPD), dehydration and gastroenteritis, and dental conditions (9-10% of all PAHs)

A design science framework for research in health analytics

Data analytics provide the ability to systematically identify patterns and insights from a variety of data as organizations pursue improvements in their processes, products, and services. Analytics can be classified based on their ability to: explore, explain, predict, and prescribe. When applied to the field of healthcare, analytics presents a new frontier for business intelligence. In 2013 alone, the Centers for Medicare and Medicaid Services (CMS) reported that the national health expenditure was $2.9 trillion, representing 17.4% of the total United States GDP. The Patient Protection and Affordable Care Act of 2010 (ACA) requires all hospitals to implement electronic medical record (EMR) technologies by year 2014 (Patient Protection and Affordable Care Act, 2010). Moreover, the ACA makes healthcare process and outcomes more transparent by making related data readily available for research. Enterprising organizations are employing analytics and analytical techniques to find patterns in healthcare data (I. R. Bardhan & Thouin, 2013; Hansen, Miron-Shatz, Lau, & Paton, 2014). The goal is to assess the cost and quality of care and identify opportunities for improvement for organizations as well as the healthcare system as a whole. Yet, there remains a need for research to systematically understand, explain, and predict the sources and impacts of the widely observed variance in the cost and quality of care available. This is a driving motivation for research in healthcare. This dissertation conducts a design theoretic examination of the application of advanced data analytics in healthcare. Heart Failure is the number one cause of death and the biggest contributor healthcare costs in the United States. An exploratory examination of the application of predictive analytics is conducted in order to understand the cost and quality of care provided to heart failure patients. The specific research question is addressed: How can we improve and expand upon our understanding of the variances in the cost of care and the quality of care for heart failure? Using state level data from the State Health Plan of North Carolina, a standard readmission model was assessed as a baseline measure for prediction, and advanced analytics were compared to this baseline. This dissertation demonstrates that advanced analytics can improve readmission predictions as well as expand understanding of the profile of a patient readmitted for heart failure. Implications are assessed for academics and practitioners

The Development of A Systematic Discharge Planning Process For the Care of Copd Patients In A Small Urban Community Hospital

Background: Several attempts have been made to examine factors that influence 30-day readmissions in a hospital setting to ensure that inpatient care is accompanied by an effective post-discharge plan that can decrease 30-day readmissions to guide hospitals to use practices that increase hospitals ‘quality implications (Shah et al., 2015; Kripalani et al., 2007; Rinne et al., 2017, Jenks, Williams and Coleman, 2009, Shah, Press, Husingh-Scheetz & White, 2016; Sickler et al., 2015; Pruitt, 2018; Hansen et al., 2013; Simmering et al., 2016; Alper, O’Malley, & Greenwald, 2019). Purpose: To determine the role of post-discharge care in 30-day readmissions along with the typical clinical outcomes identified, we examined a small urban hospital population and the patient characteristics in each post-discharge care setting (HSC, HHC, LTAC, and SNF). Patients and Methods: A retrospective study was conducted in patients with COPD hospitalizations using the data from a small urban community hospital from 2014 to 2019, n = 1,008. Results: Home health care was identified as having the highest readmission rate in this small urban community hospital using a test of proportions. The weighted variables from a researcher-developed covariate scoring table were analyzed using a Chi-square analysis. The findings provided a reference framework for a systemized discharged planning process according to how the variables/groups were score

Exploring Education Needs for Heart Failure Patients\u27 Transition of Care to Home

Transitions of care is a model designed to ensure that patients have resources needed to assist them to care for themselves at home after hospital discharge, which helps to decrease preventable adverse events. For people with heart failure (HF) to transition home from the hospital successfully, specific education is needed that is individualized to the disease process, but most patients\u27 educational needs after discharge are unmet. The purpose of this qualitative study, guided by the Meleis middle range theory of transition, was to explore the perspectives of people with HF about their educational needs in order to gather data that could inform better care practices for them once they are discharged from the hospital. Twelve participants with HF were interviewed post hospital discharge about their education experience at discharge and what they felt was needed for them to be successful in caring for themselves after discharge. Data were analyzed, and three themes emerged: discharge preparation, lifestyle changes, and transitions of care. Participants indicated that they had a positive experience with the education provided, that they had to make changes to their daily routines, and that the transition of care program was beneficial in helping them successfully care for themselves after discharge. Further studies should interview people of different ethnicities with HF, should include multiple sites in the study, and should extend the research to people with other illnesses to gain their perception of discharge education. Results contribute to positive social change because individuals with HF who know how to care for themselves at home will be able to improve their quality of life as they can effectively transition to home from the hospital setting

Social and Behavioral Domains in Acute Care Electronic Health Records: Barriers, Facilitators, Relevance, and Value.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2018

A novel framework for predicting patients at risk of readmission

Uncertainty in decision-making for patients’ risk of re-admission arises due to non-uniform data and lack of knowledge in health system variables. The knowledge of the impact of risk factors will provide clinicians better decision-making and in reducing the number of patients admitted to the hospital. Traditional approaches are not capable to account for the uncertain nature of risk of hospital re-admissions. More problems arise due to large amount of uncertain information. Patients can be at high, medium or low risk of re-admission, and these strata have ill-defined boundaries. We believe that our model that adapts fuzzy regression method will start a novel approach to handle uncertain data, uncertain relationships between health system variables and the risk of re-admission. Because of nature of ill-defined boundaries of risk bands, this approach does allow the clinicians to target individuals at boundaries. Targeting individuals at boundaries and providing them proper care may provide some ability to move patients from high risk to low risk band. In developing this algorithm, we aimed to help potential users to assess the patients for various risk score thresholds and avoid readmission of high risk patients with proper interventions. A model for predicting patients at high risk of re-admission will enable interventions to be targeted before costs have been incurred and health status have deteriorated. A risk score cut off level would flag patients and result in net savings where intervention costs are much higher per patient. Preventing hospital re-admissions is important for patients, and our algorithm may also impact hospital income