A novel and validated agile Ontology Engineering methodology for the development of ontology-based applications

The goal of this Thesis is to investigate the status of Ontology Engineering, underlining the main key issues still characterizing this discipline. Among these issues, the problem of reconciling macro-level methodologies with authoring techniques is pivotal in supporting novel ontology engineers. The latest approach characterizing ontology engineering methodologies leverages the agile paradigm to support collaborative ontology development and deliver efficient ontologies. However, so far, the investigations in the current support provided by these methodologies and the delivery of efficient ontologies have not been investigated. Thus, this work proposes a novel framework for the investigation of agile methodologies, with the objective of identifying the strong point of each agile methodology and their limitations. Leveraging on the findings of this analysis, the Thesis introduces a novel agile methodology – AgiSCOnt – aimed at tackling some of the key issues characterizing Ontology Engineering and weaknesses identified in existing agile approaches. The novel methodology is then put to the test as it is adopted for the development of two new domain ontologies in the field of health: the first is dedicated to patients struggling with dysphagia, while the second addresses patients affected by Chronic obstructive pulmonary disease.The goal of this Thesis is to investigate the status of Ontology Engineering, underlining the main key issues still characterizing this discipline. Among these issues, the problem of reconciling macro-level methodologies with authoring techniques is pivotal in supporting novel ontology engineers. The latest approach characterizing ontology engineering methodologies leverages the agile paradigm to support collaborative ontology development and deliver efficient ontologies. However, so far, the investigations in the current support provided by these methodologies and the delivery of efficient ontologies have not been investigated. Thus, this work proposes a novel framework for the investigation of agile methodologies, with the objective of identifying the strong point of each agile methodology and their limitations. Leveraging on the findings of this analysis, the Thesis introduces a novel agile methodology – AgiSCOnt – aimed at tackling some of the key issues characterizing Ontology Engineering and weaknesses identified in existing agile approaches. The novel methodology is then put to the test as it is adopted for the development of two new domain ontologies in the field of health: the first is dedicated to patients struggling with dysphagia, while the second addresses patients affected by Chronic obstructive pulmonary disease

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

The MedRed ontology for representing clinical data acquisition metadata

Electronic Data Capture (EDC) software solutions are progressively being adopted for conducting clinical trials and studies, carried out by biomedi-cal, pharmaceutical and health-care research teams. In this paper we present the MedRed Ontology, whose goal is to represent the metadata of these studies, using well-established standards, and reusing related vocabularies to describe essential aspects, such as validation rules, composability, or provenance. The paper de-scribes the design principles behind the ontology and how it relates to existing models and formats used in the industry. We also reuse well-known vocabularies and W3C recommendations. Furthermore, we have validated the ontology with ex-isting clinical studies in the context of the MedRed project, as well as a collection of metadata of well-known studies. Finally, we have made the ontology available publicly following best practices and vocabulary sharing guidelines