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

Bringing the pieces together:Integrating cardiac and geriatric care in older patients with heart disease

Due to the increasing aging population, the number of older cardiac patients is also expected to rise in the next decades. The treatment of older cardiac patients is complex due to the simultaneously presence of comorbidities and polypharmacy, and geriatric conditions such as functional impairment, fall risk and malnutrition. However, the assessment of geriatric conditions is not part of the medical routine in cardiology and therefore these conditions are frequently unrecognized although they have a significant impact on treatment and on outcomes. In addition, treatments are mostly based on single-disease oriented guidelines and inadequately take other conditions into account. This may lead to conflicting recommendations and treatments that do not address important outcomes for older patients such as daily functioning, symptom relief and quality of life. Thus, the care of older cardiac patients is currently suboptimal which increases the risk of functional loss, readmission and mortality. The overall aim of the work described in this thesis is to explore the integration of cardiac and geriatric care for older patients with heart disease. First, by examining how hospitalized older cardiac patients at high risk for adverse events could be identified. Second, by investigating lifestyle-related secondary prevention of cardiovascular complications in older cardiac patients. And third, by developing a transitional care intervention for older cardiac patients and evaluating the effect on unplanned hospital readmission and mortality

The Quality Application of Deep Learning in Clinical Outcome Predictions Using Electronic Health Record Data: A Systematic Review

Introduction: Electronic Health Record (EHR) is a significant source of medical data that can be used to develop predictive modelling with therapeutically useful outcomes. Predictive modelling using EHR data has been increasingly utilized in healthcare, achieving outstanding performance and improving healthcare outcomes. Objectives: The main goal of this review study is to examine different deep learning approaches and techniques used to EHR data processing. Methods: To find possibly pertinent articles that have used deep learning on EHR data, the PubMed database was searched. Using EHR data, we assessed and summarized deep learning performance in a number of clinical applications that focus on making specific predictions about clinical outcomes, and we compared the outcomes with those of conventional machine learning models. Results: For this study, a total of 57 papers were chosen. There have been five identified clinical outcome predictions: illness (n=33), intervention (n=6), mortality (n=5), Hospital readmission (n=7), and duration of stay (n=1). The majority of research (39 out of 57) used structured EHR data. RNNs were used as deep learning models the most frequently (LSTM: 17 studies, GRU: 6 research). The analysis shows that deep learning models have excelled when applied to a variety of clinical outcome predictions. While deep learning's application to EHR data has advanced rapidly, it's crucial that these models remain reliable, offering critical insights to assist clinicians in making informed decision. Conclusions: The findings demonstrate that deep learning can outperform classic machine learning techniques since it has the advantage of utilizing extensive and sophisticated datasets, such as longitudinal data seen in EHR. We think that deep learning will keep expanding because it has been quite successful in enhancing healthcare outcomes utilizing EHR data

Using Healthcare Data to Inform Health Policy: Quantifying Cardiovascular Disease Risk and Assessing 30-Day Readmission Measures

Health policy makers are struggling to manage health care and spending. To identify strategies for improving health quality and reducing health spending, policy makers need to first understand health risks and outcomes. Despite lacking some desirable clinical detail, existing health care databases, such as national health surveys and claims and enrollment data for insured populations, are often rich in information relating patient characteristics to heath risks and outcomes. They typically encompass more inclusive populations than can feasibly be achieved with new data collection and are valuable resources for informing health policy. This dissertation illustrates how the Medicare Current Beneficiary Survey (MCBS) and MassHealth data can be used to develop models that provide useful estimates of risks and health quality measures. It provides insights into: 1) the benefits of a proxy for the Framingham cardiovascular disease (CVD) risk score, that relies only on variables available in the MCBS, to target health interventions to policy-relevant subgroups, such as elderly Medicare beneficiaries, based on their risk of developing CVD, 2) the importance of setting appropriate risk-adjusted quality of care standards for accountable care organizations (ACOs) based on the characteristics of their enrolled members, and 3) the outsized effect of high- frequency hospital users on re-admission measures and possibly other quality measures. This work develops tools that can be used to identify and support care of vulnerable patients to both improve their health outcomes and reduce spending – an important step on the road to health equity