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

A Learning Health System for Radiation Oncology

The proposed research aims to address the challenges faced by clinical data science researchers in radiation oncology accessing, integrating, and analyzing heterogeneous data from various sources. The research presents a scalable intelligent infrastructure, called the Health Information Gateway and Exchange (HINGE), which captures and structures data from multiple sources into a knowledge base with semantically interlinked entities. This infrastructure enables researchers to mine novel associations and gather relevant knowledge for personalized clinical outcomes. The dissertation discusses the design framework and implementation of HINGE, which abstracts structured data from treatment planning systems, treatment management systems, and electronic health records. It utilizes disease-specific smart templates for capturing clinical information in a discrete manner. HINGE performs data extraction, aggregation, and quality and outcome assessment functions automatically, connecting seamlessly with local IT/medical infrastructure. Furthermore, the research presents a knowledge graph-based approach to map radiotherapy data to an ontology-based data repository using FAIR (Findable, Accessible, Interoperable, Reusable) concepts. This approach ensures that the data is easily discoverable and accessible for clinical decision support systems. The dissertation explores the ETL (Extract, Transform, Load) process, data model frameworks, ontologies, and provides a real-world clinical use case for this data mapping. To improve the efficiency of retrieving information from large clinical datasets, a search engine based on ontology-based keyword searching and synonym-based term matching tool was developed. The hierarchical nature of ontologies is leveraged to retrieve patient records based on parent and children classes. Additionally, patient similarity analysis is conducted using vector embedding models (Word2Vec, Doc2Vec, GloVe, and FastText) to identify similar patients based on text corpus creation methods. Results from the analysis using these models are presented. The implementation of a learning health system for predicting radiation pneumonitis following stereotactic body radiotherapy is also discussed. 3D convolutional neural networks (CNNs) are utilized with radiographic and dosimetric datasets to predict the likelihood of radiation pneumonitis. DenseNet-121 and ResNet-50 models are employed for this study, along with integrated gradient techniques to identify salient regions within the input 3D image dataset. The predictive performance of the 3D CNN models is evaluated based on clinical outcomes. Overall, the proposed Learning Health System provides a comprehensive solution for capturing, integrating, and analyzing heterogeneous data in a knowledge base. It offers researchers the ability to extract valuable insights and associations from diverse sources, ultimately leading to improved clinical outcomes. This work can serve as a model for implementing LHS in other medical specialties, advancing personalized and data-driven medicine

Electronic Health Record Optimization for Cardiac Care

Electronic health record (EHR) systems have been studied for over 30 years, and despite the benefits of information technology in other knowledge domains, progress has been slow in healthcare. A growing body of evidence suggests that dissatisfaction with EHR systems was not simply due to resistance to adoption of new technology but also due to real concerns about the adverse impact of EHRs on the delivery of patient care. Solutions for EHR improvement require an approach that combines an understanding of technology adoption with the complexity of the social and technical elements of the US healthcare system. Several studies are presented to clarify and propose a new framework to study EHR-provider interaction. Four focus areas were defined - workflow, communication, medical decision-making and patient care. Using Human Computer Interaction best practices, an EHR usability framework was designed to include a realistic clinical scenario, a cognitive walkthrough, a standardized simulated patient actor, and a portable usability lab. Cardiologists, fellows and nurse practitioners were invited to participate in a simulation to use their institution’s EHR system for a routine cardiac visit. Using a mixed methods approach, differences in satisfaction and effectiveness were identified. Cardiologists were dissatisfied with EHR functionality, and were critical of the potential impact of the communication of incorrect information, while displaying the highest level of success in completing the tasks. Fellows were slightly less dissatisfied with their EHR interaction, and demonstrated a preference for tools to improve workflow and support decision-making, and showed less success in completing the tasks in the scenario. Nurse practitioners were also dissatisfied with their EHR interaction, and cited poor organization of data, yet demonstrated more success than fellows in successful completion of tasks. Study results indicate that requirements for EHR functionality differ by type of provider. Cardiologists, cardiology fellows, and nurse practitioners required different levels of granularity of patient data for use in medical decision-making, defined different targets for communication, sought different solutions to workflow which included distribution of data input, and requested technical solutions to ensure valid and relevant patient data. These findings provide a foundation for future work to optimize EHR functionality

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers

The impact of hospital nurse staffing, work environment and patient-centeredness on the quality of care and patient safety

The healthcare industry is complex in nature. The quality of care and patient safety has become a national and international priority. Limited efforts have been made on improving nursing care in order to optimize the outcomes of care. This study empirically investigated the impact of hospital nurse staffing and work environment on the quality of care and patient safety in the medical and surgical wards in Malaysian private hospitals. The mediating effect of patient-centeredness on the effect of both hospital nurse staffing and work environment on the quality of care and patient safety was also investigated. A cross-sectional survey was conducted on 12 private hospitals. Data was collected, through questionnaires, from 652 nurses, with a 61.8 % response rate. The stratified simple random sampling was used to allow nurses from all shifts to participate in the study. Regression analyses and the Hayes PROCESS macro were conducted to test the hypotheses. The results showed that staffing had an insignificant negative impact on the outcomes of care, whereas work environment had a significant positive impact. Moreover, it was found that patient-centeredness significantly mediated the effect of both staffing and work environment upon the outcomes. A model for improving the quality of care and patient safety was proposed. The practical implications indicated that patientcenteredness suppresses the negative impact of nursing shortage and shift length, and complements the positive impact of work environment on the outcomes of care. Further mediators are recommended for future research on the impact of both hospital nurse staffing and work environment on the quality of care and patient safety

Transactions of the First International Conference on Health Information Technology Advancement vol. 1, no. 1

Full proceedings of The First International Conference on Health Information Technology Advancement held at Western Michigan University in Kalamazoo, Michigan on October 28, 2011. Conference Co-Chairs: Dr. Bernard Han, Director of the Center for HIT Advancement (CHITA) at Western Michigan University Dr. Sharie Falan, Associate Director of the Center for HIT Advancement (CHITA) at Western Michigan University Transactions Editor: Dr. Huei Lee, Professor in the Department of Computer Information Systems at Eastern Michigan Universit