An Autoethnographic Account of Innovation at the US Department of Veterans Affairs

The history of the U.S. Department of Veterans Affairs (VA) health information technology (HIT) has been characterized by both enormous successes and catastrophic failures. While the VA was once hailed as the way to the future of twenty-first-century health care, many programs have been mismanaged, delayed, or flawed, resulting in the waste of hundreds of millions of taxpayer dollars. Since 2015 the U.S. Government Accountability Office (GAO) has designated HIT at the VA as being susceptible to waste, fraud, and mismanagement. The timely central research question I ask in this study is, can healthcare IT at the VA be healed? To address this question, I investigate a HIT case study at the VA Center of Innovation (VACI), originally designed to be the flagship initiative of the open government transformation at the VA. The Open Source Electronic Health Record Alliance (OSEHRA) was designed to promote the open innovation ecosystem public-private-academic partnership. Based on my fifteen years of experience at the VA, I use an autoethnographic methodology to make a significant value-added contribution to understanding and modeling the VA’s approach to innovation. I use several theoretical information system framework models including People, Process, and Technology (PPT), Technology, Organization and Environment (TOE), and Technology Adaptive Model (TAM) and propose a new adaptive theory to understand the inability of VA HIT to innovate. From the perspective of people and culture, I study retaliation against whistleblowers, organization behavioral integrity, and lack of transparency in communications. I examine the VA processes, including the different software development methodologies used, the development and operations process (DevOps) of an open-source application developed at VACI, the Radiology Protocol Tool Recorder (RAPTOR), a Veterans Health Information Systems and Technology Architecture (VistA) radiology workflow module. I find that the VA has chosen to migrate away from inhouse application software and buy commercial software. The impact of these People, Process, and Technology findings are representative of larger systemic failings and are appropriate examples to illustrate systemic issues associated with IT innovation at the VA. This autoethnographic account builds on first-hand project experience and literature-based insights

Technology 2004, Vol. 2

Proceedings from symposia of the Technology 2004 Conference, November 8-10, 1994, Washington, DC. Volume 2 features papers on computers and software, virtual reality simulation, environmental technology, video and imaging, medical technology and life sciences, robotics and artificial intelligence, and electronics

Bioinspired metaheuristic algorithms for global optimization

This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions

Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter

In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF