From Data to Decision: An Implementation Model for the Use of Evidence-based Medicine, Data Analytics, and Education in Transfusion Medicine Practice

Healthcare in the United States is underperforming despite record increases in spending. The causes are as myriad and complex as the suggested solutions. It is increasingly important to carefully assess the appropriateness and cost-effectiveness of treatments especially the most resource-consuming clinical interventions. Healthcare reimbursement models are evolving from fee-for-service to outcome-based payment. The Patient Protection and Affordable Care Act has added new incentives to address some of the cost, quality, and access issues related to healthcare, making the use of healthcare data and evidence-based decision-making essential strategies. However, despite the great promise of these strategies, the transition to data-driven, evidence-based medical practice is complex and faces many challenges. This study aims to bridge the gaps that exist between data, knowledge, and practice in a healthcare setting through the use of a comprehensive framework to address the administrative, cultural, clinical, and technical issues that make the implementation and sustainability of an evidence-based program and utilization of healthcare data so challenging. The study focuses on promoting evidence-based medical practice by leveraging a performance management system, targeted education, and data analytics to improve outcomes and control costs. The framework was implemented and validated in transfusion medicine practice. Transfusion is one of the top ten coded hospital procedures in the United States. Unfortunately, the costs of transfusion are underestimated and the benefits to patients are overestimated. The particular aim of this study was to reduce practice inconsistencies in red blood cell transfusion among hospitalists in a large urban hospital using evidence-based guidelines, a performance management system, recurrent reporting of practice-specific information, focused education, and data analytics in a continuous feedback mechanism to drive appropriate decision-making prior to the decision to transfuse and prior to issuing the blood component. The research in this dissertation provides the foundation for implementation of an integrated framework that proved to be effective in encouraging evidence-based best practices among hospitalists to improve quality and lower costs of care. What follows is a discussion of the essential components of the framework, the results that were achieved and observations relative to next steps a learning healthcare organization would consider

Research and Education in Computational Science and Engineering

Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers of all persuasions with algorithmic inventions and software systems that transcend disciplines and scales. Carried on a wave of digital technology, CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society; and the CSE community is at the core of this transformation. However, a combination of disruptive developments---including the architectural complexity of extreme-scale computing, the data revolution that engulfs the planet, and the specialization required to follow the applications to new frontiers---is redefining the scope and reach of the CSE endeavor. This report describes the rapid expansion of CSE and the challenges to sustaining its bold advances. The report also presents strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie

The State-of-the-Art of Set Visualization

Sets comprise a generic data model that has been used in a variety of data analysis problems. Such problems involve analysing and visualizing set relations between multiple sets defined over the same collection of elements. However, visualizing sets is a non-trivial problem due to the large number of possible relations between them. We provide a systematic overview of state-of-the-art techniques for visualizing different kinds of set relations. We classify these techniques into six main categories according to the visual representations they use and the tasks they support. We compare the categories to provide guidance for choosing an appropriate technique for a given problem. Finally, we identify challenges in this area that need further research and propose possible directions to address these challenges. Further resources on set visualization are available at http://www.setviz.net

Evidence and Extrapolation: Mechanisms for Regulating Off-Label Uses of Drugs and Devices

A recurring, foundational issue for evidence-based regulation is deciding whether to extend governmental approval from an existing use with sufficient current evidence of safety and efficacy to a novel use for which such evidence is currently lacking. This extrapolation issue arises in the medicines context when an approved drug or device that is already being marketed is being considered (1) for new conditions (such as off-label diagnostic categories), (2) for new patients (such as new subpopulations), (3) for new dosages or durations, or (4) as the basis for approving a related drug or device (such as a generic or biosimilar drug). Although the logic of preapproval testing and the precautionary principle—first, do no harm—would counsel in favor of prohibiting extrapolation approvals until after traditional safety and efficacy evidence exists, such delays would unreasonably sacrifice beneficial uses. The harm of accessing unsafe products must be balanced against the harm of restricting access to effective products. In fact, the Food and Drug Administration\u27s (FDA\u27s) current regulations in many ways reject the precautionary principle because they largely permit individual physicians to prescribe medications for off-label uses before any testing tailored to those uses has been done. The FDA\u27s approach empowers physicians, but overshoots the mark by allowing enduring use of drugs and devices with insubstantial support of safety and efficacy. This Article instead proposes a more dynamic and evolving evidence-based regime that charts a course between the Scylla and Charybdis of the overly conservative precautionary principle on one hand, and the overly liberal FDA regime on the other. Our approach calls for improvements in reporting, testing, and enforcement regulations to provide a more layered and nuanced system of regulatory incentives. First, we propose a more thoroughgoing reporting of off-label use (via the disclosure of diagnostic codes and detailing data) in manufacturers\u27 annual reports to the FDA, in the adverse event reports to the FDA, in Medicare/Medicaid reimbursement requests, and, for a subset of FDA-designated drugs, in prescriptions themselves. Second, we would substantially expand the agency\u27s utilization of postmarket testing, and we provide a novel framework for evaluating the need for postmarket testing. Finally, our approach calls for a tiered labeling system that would allow regulators and courts to draw finer reimbursement and liability distinctions among various drug uses, and would provide the agency both the regulatory teeth and the flexibility it presently lacks. Together, these reforms would improve the role of the FDA in the informational marketplace underlying physicians\u27 prescribing decisions. This evolutionary extrapolation framework could also be applied to other contexts