Strategies for Applying Electronic Health Records to Achieve Cost Saving Benefits

The American Recovery and Reinvestment Act (ARRA) of 2009 authorized the distribution of about $30 billion incentive funds to accelerate electronic health record (EHR) applications to improve the quality of care, safety, privacy, care coordination, and patients\u27 involvement in healthcare. EHR use has the potential of saving$731 in costs for hospitals per patient admission; however, most hospitals are not applying EHR to reach the level at which cost savings are possible. The purpose of this single case study was to explore strategies that IT leaders in hospitals can use to apply EHR to achieve the cost saving benefits. The participants were IT leaders and EHR super users at a large hospital in Texas with successful experience in applying EHR. Information systems success model formed the conceptual framework for the study. I conducted face-to-face interviews and analyzed organizational documents. I used qualitative textual data analysis method to identify themes. Five themes emerged from this study, which are ensuring information quality, ensuring system quality, assuring service quality, promoting usability, and maximizing net benefits of the EHR system. The findings of this study included four strategies to apply EHR; these strategies include engaging training staff, documenting accurately and in a timely manner, protecting patient data, and enforcing organizational best practice policies to maximize reimbursement and cost savings. The findings of this study could contribute to positive social change for the communities because EHR successful application includes lower cost for hospitals that may lead to the provision of affordable care to more low-income patients

Using Big Data Analytics and Statistical Methods for Improving Drug Safety

This dissertation includes three studies, all focusing on utilizing Big Data and statistical methods for improving one of the most important aspects of health care, namely drug safety. In these studies we develop data analytics methodologies to inspect, clean, and model data with the aim of fulfilling the three main goals of drug safety; detection, understanding, and prediction of adverse drug effects.In the first study, we develop a methodology by combining both analytics and statistical methods with the aim of detecting associations between drugs and adverse events through historical patients' records. Particularly we show applicability of the developed methodology by focusing on investigating potential confounding role of common diabetes drugs on developing acute renal failure in diabetic patients. While traditional methods of signal detection mostly consider one drug and one adverse event at a time for investigation, our proposed methodology takes into account the effect of drug-drug interactions by identifying groups of drugs frequently prescribed together.In the second study, two independent methodologies are developed to investigate the role of prescription sequence factor on the likelihood of developing adverse events. In fact, this study focuses on using data analytics for understanding drug-event associations. Our analyses on the historical medication records of a group of diabetic patients using the proposed approaches revealed that the sequence in which the drugs are prescribed, and administered, significantly do matter in the development of adverse events associated with those drugs.The third study uses a chronological approach to develop a network of approved drugs and their known adverse events. It then utilizes a set of network metrics, both similarity- and centrality-based, to build and train machine learning predictive models and predict the likely adverse events for the newly discovered drugs before their approval and introduction to the market. For this purpose, data of known drug-event associations from a large biomedical publication database (i.e., PubMed) is employed to construct the network. The results indicate significant improvements in terms of accuracy of prediction of drug-evet associations compared with similar approaches