3 research outputs found
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
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Simultaneous modelling and clustering of visual field data
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonIn the health-informatics and bio-medical domains, clinicians produce an enormous amount of data which can be complex and high in dimensionality. This scenario includes visual field data, which are used for managing the second leading cause of blindness in the world: glaucoma. Visual field data are the most common type of data collected to diagnose glaucoma in patients, and usually the data consist of 54 or 76 variables (which are referred to as visual field locations). Due to the large number of variables, the six nerve fiber bundles (6NFB), which is a collection of visual field locations in groups, are the standard clusters used in visual field data to represent the physiological traits of the retina. However, with regard to classification accuracy of the data, this research proposes a technique to find other significant spatial clusters of visual field with higher classification accuracy than the 6NFB.
This thesis presents a novel clustering technique, namely, Simultaneous Modelling and Clustering (SMC). SMC performs clustering on data based on classification accuracy using heuristic search techniques. The method searches a collection of significant clusters of visual field locations that indicate visual field loss progression. The aim of this research is two-fold. Firstly, SMC algorithms are developed and tested on data to investigate the effectiveness and efficiency of the method using optimisation and classification methods. Secondly, a significant clustering arrangement of visual field, which highly interrelated visual field locations to represent progression of visual field loss with high classification accuracy, is searched to complement the 6NFB in diagnosis of glaucoma. A new clustering arrangement of visual field locations can be used by medical practitioners together with the 6NFB to complement each other in diagnosis of glaucoma in patients.
This research conducts extensive experiment work on both visual field and simulated data to evaluate the proposed method. The results obtained suggest the proposed method appears to be an effective and efficient method in clustering visual field data and
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improving classification accuracy. The key contributions of this work are the novel model-based clustering of visual field data, effective and efficient algorithms for SMC, practical knowledge of visual field data in the diagnosis of glaucoma and the presentation a generic framework for modelling and clustering which is highly applicable to many other dataset/model combinations