OBJECTIVES: Advancing health through informatics requires attending to justice. Recent policy changes in the United States have introduced significant barriers to promoting justice within informatics due to targeted funding cuts and hostility to science, especially science that prioritizes justice. MATERIALS AND METHODS: We present five key principles for advancing a justice-oriented informatics agenda, synthesized from our workshop held at the American Medical Informatics Association 2022 Annual Symposium. RESULTS: These principles are: (1) Recognize knowledge and methodologies across communities; (2) Acknowledge historical and cultural contexts of interactions; (3) Facilitate transparency and accountability through clear measures and metrics; (4) Foster trust and sustainability; and (5) Equitably allocate compensation and resources. DISCUSSION AND CONCLUSION: We discuss barriers to implementing these principles that have arisen since the 2022 workshop and provide recommendations for moving towards justice-oriented informatics. We offer examples of how these principles may be used to frame challenges and adapt to new barriers within BMI.

OBJECTIVE: The American Medical Informatics Association (AMIA) Task Force on Diversity, Equity, and Inclusion (DEI) was established to address systemic racism and health disparities in biomedical and health informatics, aligning with AMIA's mission to transform healthcare. AMIA's DEI initiatives were spurred by member voices responding to police brutality and COVID-19's impact on Black/African American communities. MATERIALS AND METHODS: The Task Force, consisting of 20 members across 3 groups aligned with AMIA's 2020-2025 Strategic Plan, met biweekly to develop DEI recommendations with the help of 16 additional volunteers. These recommendations were reviewed, prioritized, and presented to the AMIA Board of Directors for approval. RESULTS: In 9 months, the Task Force (1) created a logic model to support workforce diversity and raise AMIA's DEI awareness, (2) conducted an environmental scan of other associations' DEI activities, (3) developed a DEI framework for AMIA meetings, (4) gathered member feedback, (5) cultivated DEI educational resources, (6) created a Board nominations and diversity session, (7) reviewed the Board's Strategic Planning for DEI alignment, (8) led a program to increase diversity at the 2020 AMIA Virtual Annual Symposium, and (9) standardized socially-assigned race and ethnicity data collection. DISCUSSION: The Task Force proposed actionable recommendations that focused on AMIA's role in addressing systemic racism and health equity, helping the organization understand its member diversity. CONCLUSION: This work supported marginalized groups, broadened the research agenda, and positioned AMIA as a DEI leader while reinforcing the need for ongoing transformation within informatics.

OBJECTIVE: Developing equitable, sustainable informatics solutions is key to scalability and long-term success for projects in the global health informatics (GHI) domain. This paper presents key strategies for incorporating principles of health equity in the GHI project lifecycle. MATERIALS AND METHODS: The American Medical Informatics Association (AMIA) GHI Working Group organized a collaborative workshop at the 2023 AMIA Annual Symposium that included the presentation of five case studies of how principles of health equity have been incorporated into projects situated in low-and-middle-income countries and with Indigenous communities in the U.S. and best practices for operationalizing these principles into other informatics projects. RESULTS: We present five principles: (1) Inclusion and Participation in Ethical, Sustainable Collaborations; (2) Engaging Community-Based Participatory Research Approaches; (3) Stakeholder Engagement; (4) Scalability and Sustainability; (5) Representation in Knowledge Creation, along with strategies that informatics researchers may use to incorporate these principles into their work. DISCUSSION: Presented case studies and subsequent focus groups yielded key concepts and strategies to promote health equity that may be operationalized across GHI projects. CONCLUSION: Equitable, sustainable, and scalable GHI projects require intentional integration of community and stakeholder perspectives in project development, implementation, and knowledge creation processes.

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.

Many approaches in biomedical informatics (BMI) rely on the ability to define, gather, and manipulate biomedical data to support health through a cyclical research-practice lifecycle. Researchers within this field are often fortunate to work closely with healthcare and public health systems to influence data generation and capture and have access to a vast amount of biomedical data. Many informaticists also have the expertise to engage with stakeholders, develop new methods and applications, and influence policy. However, research and policy that explicitly seeks to address the systemic drivers of health would more effectively support health. Intersectionality is a theoretical framework that can facilitate such research. It holds that individual human experiences reflect larger socio-structural level systems of privilege and oppression, and cannot be truly understood if these systems are examined in isolation. Intersectionality explicitly accounts for the interrelated nature of systems of privilege and oppression, providing a lens through which to examine and challenge inequities. In this paper, we propose intersectionality as an intervention into how we conduct BMI research. We begin by discussing intersectionality's history and core principles as they apply to BMI. We then elaborate on the potential for intersectionality to stimulate BMI research. Specifically, we posit that our efforts in BMI to improve health should address intersectionality's five key considerations: (1) systems of privilege and oppression that shape health; (2) the interrelated nature of upstream health drivers; (3) the nuances of health outcomes within groups; (4) the problematic and power-laden nature of categories that we assign to people in research and in society; and (5) research to inform and support social change.

Social determinants of health (SDoH) are the conditions in which people are born, live, work, and age that affect personal well-being, health outcomes, and life expectancy.1 SDoH include a range of nonmedical factors, including substance use, quality of domestic life, marital status, employment status, education, race, geography, and other factors that impact health. Understanding patient SDoH can inform patient health care and has the potential to improve health outcomes and reduce health disparities.2,3 Patient SDoH information is documented in the electronic health record (EHR) and other health-related databases through structured data and free-text (natural language) documents, including patient notes. For many SDoH, the free-text descriptions capture social and behavioral factors with higher prevalence and more detail than is available through structured data. Utilizing free-text SDoH information in large-scale studies, clinical decision-support systems, and other secondary use applications, requires the automatic extraction of key aspects of the SDoH using natural language processing (NLP). NLP-based information extraction maps the unstructured, free-text descriptions of SDoH to structured semantic representations that can be combined with available structured data to create more complete patient profiles.

Clinical notes present a wealth of information for applications in the clinical domain, but heterogeneity across clinical institutions and settings presents challenges for their processing. The clinical natural language processing field has made strides in overcoming domain heterogeneity, while pretrained deep learning models present opportunities to transfer knowledge from one task to another. Pretrained models have performed well when transferred to new tasks; however, it is not well understood if these models generalize across differences in institutions and settings within the clinical domain. We explore if institution or setting specific pretraining is necessary for pretrained models to perform well when transferred to new tasks. We find no significant performance difference between models pretrained across institutions and settings, indicating that clinically pretrained models transfer well across such boundaries. Given a clinically pretrained model, clinical natural language processing researchers may forgo the time-consuming pretraining step without a significant performance drop.