Department of Biomedical Informatics and Data Science, School of Medicine, Yale University, New Haven, CT 06510, United States.
Citations 2
Developing and sustaining inclusive language in biomedical informatics communications: an AMIA Board of Directors endorsed paper on the Inclusive Language and Context Style Guidelines
Abstract Objectives In 2023, AMIA’s Inclusive Language and Context Style Guidelines (the “Guidelines”) were approved by the Board of Directors and made a publicly available resource. This work began in 2021 through AMIA’s DEI Task Force and subsequent DEI Committee; many members provided input, feedback, and time to create the Guidelines. In this paper, the authors provide a transparent account of the origin, development, contents, and dissemination of the Guidelines and share plans for their future development and use. Materials and Methods Our approach to drafting, refining, and distributing the Guidelines included consulting existing language guides, AMIA member reviews, external expert reviews, webinars, and workshops. Through an iterative approach to drafting and refining the Guidelines, the authors consulted relevant language guidelines and many experts throughout and beyond the AMIA community. Results The Inclusive Language Context Guidelines were formally approved by the AMIA Board of Directors on February 15, 2023. The Guidelines included four principles to be considered in scientific communications: Plurality, Precision, Transparency, and Destigmatization. Discussion A moment of vulnerability where an AMIA member raised concerns about the use of harmful language during a presentation resulted in the creation of a principled approach to support inclusive language within biomedical and health informatics communications. We envision that the Guidelines will support health equity by challenging dominant public narratives around health, fostering stronger interdisciplinary collaboration and critical thinking about the impact of language, and creating a more welcoming environment for the broader AMIA community. This work could not have been completed without the support of many AMIA members and other researchers in biomedical and health informatics. The Guidelines are a living document that will continue to be updated with input and feedback from the AMIA community into the future.
Systematic design and data-driven evaluation of social determinants of health ontology (SDoHO)
Abstract Objective Social determinants of health (SDoH) play critical roles in health outcomes and well-being. Understanding the interplay of SDoH and health outcomes is critical to reducing healthcare inequalities and transforming a “sick care” system into a “health-promoting” system. To address the SDOH terminology gap and better embed relevant elements in advanced biomedical informatics, we propose an SDoH ontology (SDoHO), which represents fundamental SDoH factors and their relationships in a standardized and measurable way. Material and Methods Drawing on the content of existing ontologies relevant to certain aspects of SDoH, we used a top-down approach to formally model classes, relationships, and constraints based on multiple SDoH-related resources. Expert review and coverage evaluation, using a bottom-up approach employing clinical notes data and a national survey, were performed. Results We constructed the SDoHO with 708 classes, 106 object properties, and 20 data properties, with 1,561 logical axioms and 976 declaration axioms in the current version. Three experts achieved 0.967 agreement in the semantic evaluation of the ontology. A comparison between the coverage of the ontology and SDOH concepts in 2 sets of clinical notes and a national survey instrument also showed satisfactory results. Discussion SDoHO could potentially play an essential role in providing a foundation for a comprehensive understanding of the associations between SDoH and health outcomes and paving the way for health equity across populations. Conclusion SDoHO has well-designed hierarchies, practical objective properties, and versatile functionalities, and the comprehensive semantic and coverage evaluation achieved promising performance compared to the existing ontologies relevant to SDoH.