Integration of HeartSmart Kids into Clinical Practice: A Quality Improvement Project

Presented to the Faculty of the University of Alaska, Anchorage in partial fulfillment of requirements for the degree of MASTER OF SCIENCE, FAMILY PRACTICE NURSEIn 2009, the Centers for Medicare & Medicaid (CMS), established “Meaningful Use” regulations through an incentive program, as part of the American Recovery and Reinvestment Act of 2009 (Gance-Cleveland, Gilbert, Gilbert, Dandreaux, & Russell, 2014). Meaningful Use (MU) is tied to reimbursement and focuses on how the Electronic Health Record (EHR) is being used (Center for Disease Control and Prevention, 2012). The goal of MU is to transform the use of the EHR from a documentation tool, to a data reservoir which allows for meaningful reviews and interpretations of the quality of care (Gance-Cleveland et al, 2014).Project / Background / Significance / Review of Literature / Problem Overview / Problem Statement / Purpose / Design / Method / Plan Do Study Act (PDSA) / Ethical Considerations / Significance to Nursing / Dissemination / Conclusion

Design and Analysis for Precision Medicine Subgroup Identification

In 2015 President Barack Obama announced the launch of the Precision Medicine Initiative, spurring an out pour of interest into research regarding patient-specific health. Precision medicine is the reproducible research from which health care professionals can provide targeted treatments to their patients. Two objectives in precision medicine include (i) identifying treatment-response subgroups and (ii) identifying disease subgroups. In this manuscript, we will consider a place for traditional study designs in the new age of precision medicine by presenting the machine learning tools and statistical theory necessary to do so. We begin with a newly proposed method for estimating the individualized treatment regime from crossover studies. This method expands generalized outcome weighted learning into the 2x2 crossover study framework by considering the difference in treatment response as the observed reward and correcting for carryover effects, estimated through regression methods. After, we propose a new technique for identifying disease subgroups by applying hierarchical clustering techniques to what can be interpreted as a set of denoised outcomes. These values are weighted averages of the observed and fitted outcomes, estimated by regressing on a set of features. Finally, we return to identifying treatment-response subgroups, but, in the realm of case-control studies. We again expand on generalized outcome weighted learning in addition to accounting for the difference in the covariate distribution between the selected study sample and the total population. Between this method and electronic health data, advancements for rare and expensive to study diseases may be closer than we think.Doctor of Philosoph

EHRs Connect Research and Practice: Where Predictive Modeling, Artificial Intelligence, and Clinical Decision Support Intersect

Objectives: Electronic health records (EHRs) are only a first step in capturing and utilizing health-related data - the challenge is turning that data into useful information. Furthermore, EHRs are increasingly likely to include data relating to patient outcomes, functionality such as clinical decision support, and genetic information as well, and, as such, can be seen as repositories of increasingly valuable information about patients' health conditions and responses to treatment over time. Methods: We describe a case study of 423 patients treated by Centerstone within Tennessee and Indiana in which we utilized electronic health record data to generate predictive algorithms of individual patient treatment response. Multiple models were constructed using predictor variables derived from clinical, financial and geographic data. Results: For the 423 patients, 101 deteriorated, 223 improved and in 99 there was no change in clinical condition. Based on modeling of various clinical indicators at baseline, the highest accuracy in predicting individual patient response ranged from 70-72% within the models tested. In terms of individual predictors, the Centerstone Assessment of Recovery Level - Adult (CARLA) baseline score was most significant in predicting outcome over time (odds ratio 4.1 + 2.27). Other variables with consistently significant impact on outcome included payer, diagnostic category, location and provision of case management services. Conclusions: This approach represents a promising avenue toward reducing the current gap between research and practice across healthcare, developing data-driven clinical decision support based on real-world populations, and serving as a component of embedded clinical artificial intelligences that "learn" over time.Comment: Keywords: Data Mining; Decision Support Systems, Clinical; Electronic Health Records; Implementation; Evidence-Based Medicine; Data Warehouse; (2012). EHRs Connect Research and Practice: Where Predictive Modeling, Artificial Intelligence, and Clinical Decision Support Intersect. Health Policy and Technology. arXiv admin note: substantial text overlap with arXiv:1112.166

A Differentially Private Weighted Empirical Risk Minimization Procedure and its Application to Outcome Weighted Learning

It is commonplace to use data containing personal information to build predictive models in the framework of empirical risk minimization (ERM). While these models can be highly accurate in prediction, results obtained from these models with the use of sensitive data may be susceptible to privacy attacks. Differential privacy (DP) is an appealing framework for addressing such data privacy issues by providing mathematically provable bounds on the privacy loss incurred when releasing information from sensitive data. Previous work has primarily concentrated on applying DP to unweighted ERM. We consider an important generalization to weighted ERM (wERM). In wERM, each individual's contribution to the objective function can be assigned varying weights. In this context, we propose the first differentially private wERM algorithm, backed by a rigorous theoretical proof of its DP guarantees under mild regularity conditions. Extending the existing DP-ERM procedures to wERM paves a path to deriving privacy-preserving learning methods for individualized treatment rules, including the popular outcome weighted learning (OWL). We evaluate the performance of the DP-wERM application to OWL in a simulation study and in a real clinical trial of melatonin for sleep health. All empirical results demonstrate the viability of training OWL models via wERM with DP guarantees while maintaining sufficiently useful model performance. Therefore, we recommend practitioners consider implementing the proposed privacy-preserving OWL procedure in real-world scenarios involving sensitive data.Comment: 24 pages and 2 figures for the main manuscript, 5 pages and 2 figures for the supplementary material

ConfidentCare: A Clinical Decision Support System for Personalized Breast Cancer Screening

Breast cancer screening policies attempt to achieve timely diagnosis by the regular screening of apparently healthy women. Various clinical decisions are needed to manage the screening process; those include: selecting the screening tests for a woman to take, interpreting the test outcomes, and deciding whether or not a woman should be referred to a diagnostic test. Such decisions are currently guided by clinical practice guidelines (CPGs), which represent a one-size-fits-all approach that are designed to work well on average for a population, without guaranteeing that it will work well uniformly over that population. Since the risks and benefits of screening are functions of each patients features, personalized screening policies that are tailored to the features of individuals are needed in order to ensure that the right tests are recommended to the right woman. In order to address this issue, we present ConfidentCare: a computer-aided clinical decision support system that learns a personalized screening policy from the electronic health record (EHR) data. ConfidentCare operates by recognizing clusters of similar patients, and learning the best screening policy to adopt for each cluster. A cluster of patients is a set of patients with similar features (e.g. age, breast density, family history, etc.), and the screening policy is a set of guidelines on what actions to recommend for a woman given her features and screening test scores. ConfidentCare algorithm ensures that the policy adopted for every cluster of patients satisfies a predefined accuracy requirement with a high level of confidence. We show that our algorithm outperforms the current CPGs in terms of cost-efficiency and false positive rates