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

A survey of computable biomedical knowledge repositories

IntroductionWhile data repositories are well-established in clinical and research enterprises, knowledge repositories with shareable computable biomedical knowledge (CBK) are relatively new entities to the digital health ecosystem. Trustworthy knowledge repositories are necessary for learning health systems, but the policies, standards, and practices to promote trustworthy CBK artifacts and methods to share, and safely and effectively use them are not well studiedMethodsWe conducted an online survey of 24 organizations in the United States known to be involved in the development or deployment of CBK. The aim of the survey was to assess the current policies and practices governing these repositories and to identify best practices. Descriptive statistics methods were applied to data from 13 responding organizations, to identify common practices and policies instantiating the TRUST principles of Transparency, Responsibility, User Focus, Sustainability, and TechnologyResultsAll 13 respondents indicated to different degrees adherence to policies that convey TRUST. Transparency is conveyed by having policies pertaining to provenance, credentialed contributors, and provision of metadata. Repositories provide knowledge in machine-readable formats, include implementation guidelines, and adhere to standards to convey Responsibility. Repositories report having Technology functions that enable end-users to verify, search, and filter for knowledge products. Less common TRUST practices are User Focused procedures that enable consumers to know about user licensing requirements or query the use of knowledge artifacts. Related to Sustainability, less than a majority post describe their sustainability plans. Few organizations publicly describe whether patients play any role in their decision-making.ConclusionIt is essential that knowledge repositories identify and apply a baseline set of criteria to lay a robust foundation for their trustworthiness leading to optimum uptake, and safe, reliable, and effective use to promote sharing of CBK. Identifying current practices suggests a set of desiderata for the CBK ecosystem in its continued evolutionPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/175738/1/lrh210314_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/175738/2/lrh210314.pd

Defining AMIA\u27s artificial intelligence principles

Recent advances in the science and technology of artificial intelligence (AI) and growing numbers of deployed AI systems in healthcare and other services have called attention to the need for ethical principles and governance. We define and provide a rationale for principles that should guide the commission, creation, implementation, maintenance, and retirement of AI systems as a foundation for governance throughout the lifecycle. Some principles are derived from the familiar requirements of practice and research in medicine and healthcare: beneficence, nonmaleficence, autonomy, and justice come first. A set of principles follow from the creation and engineering of AI systems: explainability of the technology in plain terms; interpretability, that is, plausible reasoning for decisions; fairness and absence of bias; dependability, including safe failure ; provision of an audit trail for decisions; and active management of the knowledge base to remain up to date and sensitive to any changes in the environment. In organizational terms, the principles require benevolence-aiming to do good through the use of AI; transparency, ensuring that all assumptions and potential conflicts of interest are declared; and accountability, including active oversight of AI systems and management of any risks that may arise. Particular attention is drawn to the case of vulnerable populations, where extreme care must be exercised. Finally, the principles emphasize the need for user education at all levels of engagement with AI and for continuing research into AI and its biomedical and healthcare applications

Pharmacotherapy and pulmonary fibrosis risk after SARS-CoV-2 infection: a prospective nationwide cohort study in the United StatesResearch in context

Summary: Background: Pulmonary fibrosis is characterized by lung parenchymal destruction and can increase morbidity and mortality. Pulmonary fibrosis commonly occurs following hospitalization for SARS-CoV-2 infection. As there are medications that modify pulmonary fibrosis risk, we investigated whether distinct pharmacotherapies (amiodarone, cancer chemotherapy, corticosteroids, and rituximab) are associated with differences in post-COVID-19 pulmonary fibrosis incidence. Methods: We used the National COVID-19 Cohort Collaboration (N3C) Data Enclave, which aggregates and harmonizes COVID-19 data across the United States, to assess pulmonary fibrosis incidence documented at least 60 days after COVID-19 diagnosis among adults hospitalized between January 1st, 2020 and July 6th, 2022 without pre-existing pulmonary fibrosis. We used propensity scores to match pre-COVID-19 drug-exposed and unexposed cohorts (1:1) based on covariates with known influence on pulmonary fibrosis incidence, and estimated the association of drug exposure with risk for post-COVID-19 pulmonary fibrosis. Sensitivity analyses considered pulmonary fibrosis incidence documented at least 30- or 90-days post-hospitalization and pulmonary fibrosis incidence in the COVID-19-negative N3C population. Findings: Among 5,923,394 patients with COVID-19, we analyzed 452,951 hospitalized adults, among whom pulmonary fibrosis incidence was 1.1 per 100-person-years. 277,984 hospitalized adults with COVID-19 were included in our primary analysis, among whom all drug exposed cohorts were well-matched to unexposed cohorts (standardized mean differences <0.1). The post-COVID-19 pulmonary fibrosis incidence rate ratio (IRR) was 2.5 (95% CI 1.2–5.1, P = 0.01) for rituximab, 1.6 (95% CI 1.3–2.0, P < 0.0001) for chemotherapy, and 1.2 (95% CI 1.0–1.3, P = 0.02) for corticosteroids. Amiodarone exposure had no significant association with post-COVID-19 pulmonary fibrosis (IRR = 0.8, 95% CI 0.6–1.1, P = 0.24). In sensitivity analyses, pre-COVID-19 corticosteroid use was not consistently associated with post-COVID-19 pulmonary fibrosis. In the COVID-19 negative hospitalized population (n = 1,240,461), pulmonary fibrosis incidence was lower overall (0.6 per 100-person-years) and for patients exposed to all four drugs. Interpretation: Recent rituximab or cancer chemotherapy before COVID-19 infection in hospitalized patients is associated with increased risk for post-COVID-19 pulmonary fibrosis. Funding: The analyses described in this publication were conducted with data or tools accessed through the NCATS N3C Data Enclave https://covid.cd2h.org and N3C Attribution &amp; Publication Policy v1.2-2020-08-25b supported by NIH K23HL146942, NIH K08HL150291, NIH K23HL148387, NIH UL1TR002389, NCATS U24 TR002306, and a SECURED grant from the Walder Foundation/Center for Healthcare Delivery Science and Innovation, University of Chicago. WFP received a grant from the Greenwall Foundation. This research was possible because of the patients whose information is included within the data and the organizations (https://ncats.nih.gov/n3c/resources/data-contribution/data-transfer-agreement-signatories) and scientists who have contributed to the on-going development of this community resource (https://doi.org/10.1093/jamia/ocaa196)

Citizen Science to Further Precision Medicine: From Vision to Implementation

The active involvement of citizen scientists in setting research agendas, partnering with academic investigators to conduct research, analyzing and disseminating results, and implementing learnings from research can im- prove both processes and outcomes. Adopting a citizen science approach to the practice of precision medicine in clinical care and research will require healthcare providers, researchers, and institutions to address a number of technical, organizational, and citizen scientist collaboration issues. Some changes can be made with relative ease, while others will necessitate cultural shifts, redistribution of power, recommitment to shared goals, and improved communication. This perspective, based on a workshop held at the 2018 AMIA Annual Symposium, identifies current barriers and needed changes to facilitate broad adoption of a citizen science-based approach in healthcare