A Life Cycle Approach to the Development and Validation of an Ontology of the U.S. Common Rule (45 C.F.R. § 46)

Requirements for the protection of human research subjects stem from directly from federal regulation by the Department of Health and Human Services in Title 45 of the Code of Federal Regulations (C.F.R.) part 46. 15 other federal agencies include subpart A of part 46 verbatim in their own body of regulation. Hence 45 C.F.R. part 46 subpart A has come to be called colloquially the ‘Common Rule.’ Overall motivation for this study began as a desire to facilitate the ethical sharing of biospecimen samples from large biospecimen collections by using ontologies. Previous work demonstrated that in general the informed consent process and subsequent decision making about data and specimen release still relies heavily on paper-based informed consent forms and processes. Consequently, well-validated computable models are needed to provide an enhanced foundation for data sharing. This dissertation describes the development and validation of a Common Rule Ontology (CRO), expressed in the OWL-2 Web Ontology Language, and is intended to provide a computable semantic knowledge model for assessing and representing components of the information artifacts of required as part of regulated research under 45 C.F.R. § 46. I examine if the alignment of this ontology with the Basic Formal Ontology and other ontologies from the Open Biomedical Ontology (OBO) Foundry provide a good fit for the regulatory aspects of the Common Rule Ontology. The dissertation also examines and proposes a new method for ongoing evaluation of ontology such as CRO across the ontology development lifecycle and suggest methods to achieve high quality, validated ontologies. While the CRO is not in itself intended to be a complete solution to the data and specimen sharing problems outlined above, it is intended to produce a well-validated computationally grounded framework upon which others can build. This model can be used in future work to build decision support systems to assist Institutional Review Boards (IRBs), regulatory personnel, honest brokers, tissue bank managers, and other individuals in the decision-making process involving biorepository specimen and data sharing

The use of foundational ontologies in biomedical research

Background: The FAIR principles recommend the use of controlled vocabularies, such as ontologies, to define data and metadata concepts. Ontologies are currently modelled following different approaches, sometimes describing conflicting definitions of the same concepts, which can affect interoperability. To cope with that, prior literature suggests organising ontologies in levels, where domain specific (low-level) ontologies are grounded in domain independent high-level ontologies (i.e., foundational ontologies). In this level-based organisation, foundational ontologies work as translators of intended meaning, thus improving interoperability. Despite their considerable acceptance in biomedical research, there are very few studies testing foundational ontologies. This paper describes a systematic literature mapping that was conducted to understand how foundational ontologies are used in biomedical research and to find empirical evidence supporting their claimed (dis)advantages. Results: From a set of 79 selected papers, we identified that foundational ontologies are used for several purposes: ontology construction, repair, mapping, and ontology-based data analysis. Foundational ontologies are claimed to improve interoperability, enhance reasoning, speed up ontology development and facilitate maintainability. The complexity of using foundational ontologies is the most commonly cited downside. Despite being used for several purposes, there were hardly any experiments (1 paper) testing the claims for or against the use of foundational ontologies. In the subset of 49 papers that describe the development of an ontology, it was observed a low adherence to ontology construction (16 papers) and ontology evaluation formal methods (4 papers). Conclusion: Our findings have two main implications. First, the lack of empirical evidence about the use of foundational ontologies indicates a need for evaluating the use of such artefacts in biomedical research. Second, the low adherence to formal methods illustrates how the field could benefit from a more systematic approach when dealing with the development and evaluation of ontologies. The understanding of how foundational ontologies are used in the biomedical field can drive future research towards the improvement of ontologies and, consequently, data FAIRness. The adoption of formal methods can impact the quality and sustainability of ontologies, and reusing these methods from other fields is encouraged.</p

Bottom-Up Modeling of Permissions to Reuse Residual Clinical Biospecimens and Health Data

Consent forms serve as evidence of permissions granted by patients for clinical procedures. As the recognized value of biospecimens and health data increases, many clinical consent forms also seek permission from patients or their legally authorized representative to reuse residual clinical biospecimens and health data for secondary purposes, such as research. Such permissions are also granted by the government, which regulates how residual clinical biospecimens may be reused with or without consent. There is a need for increasingly capable information systems to facilitate discovery, access, and responsible reuse of residual clinical biospecimens and health data in accordance with these permissions. Semantic web technologies, especially ontologies, hold great promise as infrastructure for scalable, semantically interoperable approaches in healthcare and research. While there are many published ontologies for the biomedical domain, there is not yet ontological representation of the permissions relevant for reuse of residual clinical biospecimens and health data. The Informed Consent Ontology (ICO), originally designed for representing consent in research procedures, may already contain core classes necessary for representing clinical consent processes. However, formal evaluation is needed to make this determination and to extend the ontology to cover the new domain. This dissertation focuses on identifying the necessary information required for facilitating responsible reuse of residual clinical biospecimens and health data, and evaluating its representation within ICO. The questions guiding these studies include: 1. What is the necessary information regarding permissions for facilitating responsible reuse of residual clinical biospecimens and health data? 2. How well does the Informed Consent Ontology represent the identified information regarding permissions and obligations for reuse of residual clinical biospecimens and health data? We performed three sequential studies to answer these questions. First, we conducted a scoping review to identify regulations and norms that bear authority or give guidance over reuse of residual clinical biospecimens and health data in the US, the permissions by which reuse of residual clinical biospecimens and health data may occur, and key issues that must be considered when interpreting these regulations and norms. Second, we developed and tested an annotation scheme to identify permissions within clinical consent forms. Lastly, we used these findings as source data for bottom-up modelling and evaluation of ICO for representation of this new domain. We found considerable overlap in classes already in ICO and those necessary for representing permissions to reuse residual clinical biospecimens and health data. However, we also identified more than fifty classes that should be added to or imported into ICO. These efforts provide a foundation for comprehensively representing permissions to reuse residual clinical biospecimens and health data. Such representation fills a critical gap for developing applications which safeguard biospecimen resources and enable querying based on their permissions for use. By modeling information about permissions in an ontology, the heterogeneity of these permissions at a range of levels (e.g., federal regulations, consent forms) can be richly represented using entity-relationship links and embedded rules of inference and inheritance. Furthermore, by developing this content in ICO, missing content will be added to the Open Biological and Biomedical Ontology (OBO) Foundry, enabling use alongside other widely adopted ontologies and providing a valuable resource for biospecimen and information management. These methods may also serve as a model for domain experts to interact with ontology development communities to improve ontologies and address gaps which hinder successful uptake.PHDNursingUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162937/1/eliewolf_1.pd

Design and Architecture of an Ontology-driven Dialogue System for HPV Vaccine Counseling

Speech and conversational technologies are increasingly being used by consumers, with the inevitability that one day they will be integrated in health care. Where this technology could be of service is in patient-provider communication, specifically for communicating the risks and benefits of vaccines. Human papillomavirus (HPV) vaccine, in particular, is a vaccine that inoculates individuals from certain HPV viruses responsible for adulthood cancers - cervical, head and neck cancers, etc. My research focuses on the architecture and development of speech-enabled conversational agent that relies on series of consumer-centric health ontologies and the technology that utilizes these ontologies. Ontologies are computable artifacts that encode and structure domain knowledge that can be utilized by machines to provide high level capabilities, such as reasoning and sharing information. I will focus the agent’s impact on the HPV vaccine domain to observe if users would respond favorably towards conversational agents and the possible impact of the agent on their beliefs of the HPV vaccine. The approach of this study involves a multi-tier structure. The first tier is the domain knowledge base, the second is the application interaction design tier, and the third is the feasibility assessment of the participants. The research in this study proposes the following questions: Can ontologies support the system architecture for a spoken conversational agent for HPV vaccine counseling? How would prospective users’ perception towards an agent and towards the HPV vaccine be impacted after using conversational agent for HPV vaccine education? The outcome of this study is a comprehensive assessment of a system architecture of a conversational agent for patient-centric HPV vaccine counseling. Each layer of the agent architecture is regulated through domain and application ontologies, and supported by the various ontology-driven software components that I developed to compose the agent architecture. Also discussed in this work, I present preliminary evidence of high usability of the agent and improvement of the users’ health beliefs toward the HPV vaccine. All in all, I introduce a comprehensive and feasible model for the design and development of an open-sourced, ontology-driven conversational agent for any health consumer domain, and corroborate the viability of a conversational agent as a health intervention tool

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe

Too big to fail? A case study of the rise and fall of a medical research infrastructure

Introduction: Distributed Research Infrastructures are becoming increasingly more salient as science expands and universities continue to look for new means to cooperate and share expertise and expenses on large-scale projects. One area which has seen much development in recent years is biobanking, as there have been numerous attempts to harmonise the different biobanking standards over the years, none of which have been entirely successful. BBMRI.se was an EU-initiative that sought to harmonise the biobanks nationwide. BBMRI.se, was thus selected as a case for studying how a distributed Research Infrastructure was set up. At the time of its creation, the organisation constituted the largest investment ever made by the Swedish Research Council in a medical Research Infrastructure. The organisation involved all Swedish universities with a medical faculty, in addition to two other universities. However, the organisation was marred by a number of controversies and would eventually fold in 2018. Aim: This dissertation is to elucidate the mechanisms involved in the construction of a medical large-scale distributed Research Infrastructure, and to understand the motivations and rationale of the experts who activate themselves in constructing it. Thus, the overall aim of this doctoral thesis is to identify the benefits and constraints of forming a large-scale medical, distributed Research Infrastructure. Specifically, this dissertation looks at a real-life case while comparing it to the available literature covering the development of Research Infrastructures as well as some of the theories covering mindsharing and collective entrepreneurship. The ambition is to contribute knowledge on the determining factors in bringing a large-scale infrastructure together as well as the risks associated with it. Hence, this dissertation asks the following research question: What are the principal lessons for researchers, entrepreneurs and funders that can be inferred from the formation of a large-scale distributed Research Infrastructure towards securing more sustainable prospects for similar, future endeavours? More precisely, this dissertation seeks to determine what the most debated topics are within the academic discourse on Research Infrastructures (study I), after which it looks at the factors involved in constructing shaping a distributed Research Infrastructure (study II). The study then endeavours to looks at some of the pitfalls and how managerial self-governance affects organisational failure (study III). The study then seeks to investigate the mind-set of the managers/pioneers involved in setting up BBMRI.se and if they perceive the organisation in a similar fashion for the other managers (study IV) and how they have reasoned behind their motivations for joining the initiative in the first place (study V). The overall results have endeavoured to elucidate what components are at work when forming such an infrastructure at an organisational level, but also to understand the reasoning and motivation that the individuals responsible in setting up the infrastructure might have had, and how their visions and/or actions may have impacted on the organisation. Method: Some various designs and data collection methods were used in this dissertation. Study I was a literature study carried out as a narrative review using the PRISMA statements as a guideline. Both the Web of Science (WOS) and PubMed databases were scoured for articles. Study II-V used qualitative, semi-structured interviews with BBMRI.se managers. All of these studies took on the form of iterative, directed content analyses, with the exception of study III, which was an inductive, directed content analysis. Results: Study I found that the most commonly discussed topics concerned the need for developing and expanding the use of “infrastructures”. The findings indicated that the future of scientific research calls for a deeper and more widespread multidisciplinary forms of collaboration. Study II found that it is crucial to identify the potential collaborative and deliberative organisational elements of organisational team building already at the outset of establishing a distributed Research Infrastructure. The study also found that, contrary to suggestions of extant literature, the establishment of a distributed Research Infrastructure does not necessarily counteract organisational fragmentation. Study III identified that an organisation with high levels of task uncertainty and low levels of organisational integration will suffer from organisational fragmentation. The type of fragmentation manifested in BBMRI.se is best identified as a “fragmented adhocracy”. This means that the organisation’s mission statement is subject to diverse views, leading to goals that are separate, unstable and sometimes even conflicting, while also lacking in co-ordination. The study also found that the organisation lacked a “liaison device” and instead depended on a more traditional model of planning and control systems through its reliance on strategy documents and interim evaluation reports. This was in spite of the fact that this model is better suited for a more vertical organisational structure. Study IV investigated how managers/associates of BBMRI.se perceived the organisation’s brand and the role of “mindsharing”. The results showed that mindsharing occurred throughout the initial two stages (“Brand Strategic Analysis” and “Brand Identity”), but would disspate throughout the remaining two final stages (“Brand Operationalising”, and “Post Implementation Reflections”). This resulted in a fragmented brand perception, which resulted in the failure of generating a “pull-effect” for the BBMRI.se brand. Study V looked at how collective entrepreneurial team cognition of the instigators behind BBMRI.se changes throughout the process of establishing the organisation. The study devised a new “action phase model”, known as the “4 I’s” of entrepreneurship, where each “I” elaborated on the entrepreneurial rationale behind the various stages of the creation process. These were “Intention”, “Initiation”, “Implementation” and “Introspection”. The results illustrated that the respondents agreed that there was a need for BBMRI.se, while disagreeing on what the organisation should be doing and what its challenges consisted of. The homogenous mind-set would begin to dissipate once the “Initiation” stage was reached, declining further throughout the Implementation stage. Conclusion: The overall conclusions from study I-V have shown that distributed Research Infrastructures carries potential to form a platform to pool scientific research in the face of the ever-expanding sciences, where the demands of co-financing and scientific co-operation are becoming ever so pressing. In addition, distributed Research Infrastructures have the benefit of utilising initial synergy effects and using multidisciplinary teams. In line with the contention of Gibbons et al. (1994), this carries the potential of opening up new possibilities of scientific knowledge production. Provided there is a political incentive in place to allocate the necessary funding, the process of establishing a distributed Research Infrastructure can be done in a considerably swift timespan. However, there are several inherent risks. Most notably, there was a lack of “infrastructuring”, as defined by Star and Bowker (2002). This means that scientists as well as the policy- makers should gradually learn together through a learning process about how to creating an effective large-scale infrastructure. This may have prevented mindsharing from becoming consolidated throughout the formation process (Aaker, 1996; Acuña, 2012; Azevedo, 2005; J. Griffin, 2009; Holt, 2016; Krishnan, Sullivan, Groza, & Aurand, 2013; Stevens, 2003). This, in turn, would also put an end to the collective entrepreneurship that had up till that point characterised BBMRI.se, in which the motivations and drivers of the initiators/managers, as well as their respective recollections of the same, were instrumental features (Cardon, Post, & Forster, 2017; Czarniawska-Joerges & Wolff, 1991; Sakhdari, 2016). Moreover, the integration of autonomous “National Champions” (leading scientists within their field) carries a risk of the “principal-agent” problem, which in turn can lead to “moral hazard” as the “National Champion(s)” may elect to undertake added risks, since someone else bears the cost of those risks (Holmstrom, 1982; Laffont & Martimort, 2002; Steets, 2010). There is also an over- whelming risk of organisational fragmentation, which, coupled with managerial neglect, may cause the eventual failure of the organisation