Data, metadata, and workflow in healthcare informatics

This dissertation considers a number of interlinked concepts, propositions and relations, and puts forward a set of design theses, to support the role of informatics in the overall goal of knowledge-based, information-driven, integrated, patient-centred, collaborative healthcare and research. This rather ambitious scope may be delimited by exclusion: the work is not concerned explicitly with genomics or bioinformatics, but it does encompass certain aspects of trans- lational medicine and personalized healthcare, which I take to be subsumed in some sense under “knowledge-based” and “information-driven”. Although I do not exclude public health informatics, my exposure extends only to surveillance of infectious diseases, patient engagement, and the effectiveness of screening programmes. I do take ethical, legal, social and economic issues (ELSE) to be included, at least to the extent that I aim at an infrastructure that encompasses these issues and aims to incorporate them in technical designs in an effort to meet ethicists’, lawyers’, policy makers’, and economists’ concerns halfway. To a first approx- imation, the aim has been to integrate two strands of work over the last decade or more: the informatics of medical records on one hand and the distributed computational infrastructures for healthcare and biomedical research on the other.The papers assembled in this dissertation span a period of rapid growth in biomedical inform- atics (BMIi). Their unifying theme was not declared programmatically at the beginning of this period, but rather developed, along with individual pieces of work, as my engagement – and that of my students – with BMI became more focused and penetrated deeper into the issues. Nevertheless, I believe I have learned something from each project I have been involved in and have brought this cumulative experience to bear on the central theme of my present work. My thematic vision is of a scientifically literate and engaged community whose members – citizens, patients, caregivers, advocates – are sufficiently interested in medical progress and in their own health to take ownership of their medical records, to subscribe to a research service that informs them about progress and about current studies that may interest them, and so take responsibility for their own and the health of those close to them. This entails many things: agreements on what constitutes legitimate data sharing and when such sharing may be permitted or required by the patient as owner of the data. It calls for a means of recognizing the intellectual contribution, and in some healthcare economies, the economic interest of a physician who generates that record. Ethically, it requires a consenting policy that allows patients to control who may approach them for participation in a study, whether as a subject, as a co-investigator, as a patient advocate, or as a lay advisor. Educationally, it requires willingness on the part of physician- researchers and scientists to disseminate what they have discovered and what they have learned in terms that are comprehensible to the interested lay participant—but do not speak down to her

Toward informatics-enabled preparedness for natural hazards to minimize health impacts of climate change

Natural hazards (NHs) associated with climate change have been increasing in frequency and intensity. These acute events impact humans both directly and through their effects on social and environmental determinants of health. Rather than relying on a fully reactive incident response disposition, it is crucial to ramp up preparedness initiatives for worsening case scenarios. In this perspective, we review the landscape of NH effects for human health and explore the potential of health informatics to address associated challenges, specifically from a preparedness angle. We outline important components in a health informatics agenda for hazard preparedness involving hazard-disease associations, social determinants of health, and hazard forecasting models, and call for novel methods to integrate them toward projecting healthcare needs in the wake of a hazard. We describe potential gaps and barriers in implementing these components and propose some high-level ideas to address them

Characterizing Long COVID: Deep Phenotype of a Complex Condition.

BACKGROUND: Numerous publications describe the clinical manifestations of post-acute sequelae of SARS-CoV-2 (PASC or long COVID ), but they are difficult to integrate because of heterogeneous methods and the lack of a standard for denoting the many phenotypic manifestations. Patient-led studies are of particular importance for understanding the natural history of COVID-19, but integration is hampered because they often use different terms to describe the same symptom or condition. This significant disparity in patient versus clinical characterization motivated the proposed ontological approach to specifying manifestations, which will improve capture and integration of future long COVID studies. METHODS: The Human Phenotype Ontology (HPO) is a widely used standard for exchange and analysis of phenotypic abnormalities in human disease but has not yet been applied to the analysis of COVID-19. FINDINGS: We identified 303 articles published before April 29, 2021, curated 59 relevant manuscripts that described clinical manifestations in 81 cohorts three weeks or more following acute COVID-19, and mapped 287 unique clinical findings to HPO terms. We present layperson synonyms and definitions that can be used to link patient self-report questionnaires to standard medical terminology. Long COVID clinical manifestations are not assessed consistently across studies, and most manifestations have been reported with a wide range of synonyms by different authors. Across at least 10 cohorts, authors reported 31 unique clinical features corresponding to HPO terms; the most commonly reported feature was Fatigue (median 45.1%) and the least commonly reported was Nausea (median 3.9%), but the reported percentages varied widely between studies. INTERPRETATION: Translating long COVID manifestations into computable HPO terms will improve analysis, data capture, and classification of long COVID patients. If researchers, clinicians, and patients share a common language, then studies can be compared/pooled more effectively. Furthermore, mapping lay terminology to HPO will help patients assist clinicians and researchers in creating phenotypic characterizations that are computationally accessible, thereby improving the stratification, diagnosis, and treatment of long COVID. FUNDING: U24TR002306; UL1TR001439; P30AG024832; GBMF4552; R01HG010067; UL1TR002535; K23HL128909; UL1TR002389; K99GM145411

User-driven modelling: Visualisation and systematic interaction for end-user programmin

This research enables computer literate engineers to model problems in software by minimising code they need to write. Software development is difficult for many engineers as they may have no time, experience, or access to software development tools necessary to model their problems. Using a combination of modelling via use of formulae (equations) and visualisation of the way these formulae interact, it is possible to construct modelling software without requiring code. This technique of user-driven modelling/programming (UDM/P) could be applied to any problem that requires linked equations to be represented and tracked, and results from these calculated. End-user programming could be tackled by many researchers co-operating to create specific solutions to different kinds of end-user programming problems. A stepped ontology based translation process assists with progress towards a generic solution, this is first applied to engineering modelling. © 2012 Elsevier Ltd

Ethical and Policy Issues

The evolution of Artificial Intelligence in health and medicine has been paralleled by an extraordinary effort to identify and address the ethical issues raised by this technology. It has become clear that the success of AI in patient care and research is dependent on the success of this effort to address appropriate software quality and standards, explainability and interpretability, transparency and accountability, human control, and bias and fairness. The tools of applied ethics are identified as essential if the growing use of AI is to be trusted and trustworthy and therefore to achieve the benefits for which its promoters, developers, and users hope

Protecting Patient Privacy in Cyber Environments

Confidentiality in the medical encounter is crucial to providing adequate patient care. Health data is therefore privileged and protected by legal mechanisms. Health systems use electronic records and large-scale databases. Increasingly consumers use also IT to collect, store and share data about daily life and health behaviors. Sharing data via network-based systems or storing it ‘in the cloud’ produces multiple ‘digital selves,’ health ‘data doubles’ and ‘virtual patients.’ With so many stakeholders involved much data is produced without clear governance structures, blurring the view of what is done with the data. These problems are exacerbated through the networked, distributed nature of health data collection and convergence of protected hospital systems, commercial collection and aggregation of data and consumer health technologies. This brings patient privacy into the realm of cybersecurity. This panel explores how cybersecurity impacts the governance of critical IT infrastructures and mitigation of threats, what sociotechnical challenges are related to protection of large-scale HIT systems, how surveillance and bioethics studies seek to understand threats to personal privacy in the context of networked technologies and finally what changes to laws and regulations would be required