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.

Managing complex chronic illness is challenging due to its unpredictability. This paper explores the potential of voice for automated flare-up forecasts. We conducted a six-week speculative design study with individuals with endometriosis, tasking participants to submit daily voice recordings and symptom logs. Through focus groups, we elicited their experiences with voice capture and perceptions of its usefulness in forecasting flare-ups. Participants were enthusiastic and intrigued at the potential of flare-up forecasts through the analysis of their voice. They highlighted imagined benefits from the experience of recording in supporting emotional aspects of illness and validating both day-to-day and overall illness experiences. Participants reported that their recordings revolved around their endometriosis, suggesting that the recordings' content could further inform forecasting. We discuss potential opportunities and challenges in leveraging the voice as a data modality in human-centered AI tools that support individuals with complex chronic conditions.

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.

Intelligent systems for self-management can help patients and improve quality of life. However, designing AI-based systems is challenging because designers need to account not only for user needs, but also for capabilities and practical constraints of underlying algorithms. We propose and implement a human-centered AI framework to align human and technological requirements and constraints that can guide design of intelligent systems for personal health. We use concepts from a machine learning technique, reinforcement learning, to elicit user needs, through directed content analysis of user interviews, and uncover practical data constraints, through analysis of "in the wild" user engagement logs from a self-monitoring app. We gather and triangulate human-machine-data requirements for a self-management tool for individuals with endometriosis - a poorly understood, complex chronic condition with no reliable treatment. We present recommendations for developing a system that aligns with needs, capabilities, and constraints from human user, data, and machine learning perspectives.

With the numerous advances and broad applications of mobile health (mHealth), establishing concrete data sharing, privacy, and governance strategies at national (or regional) levels is essential to protect individual privacy and data usage. This article applies the recent Health Data Governance Principles to provide a guiding framework for low- and middle-income countries (LMICs) to create a comprehensive mHealth data governance strategy. We provide three objectives: (1) establish data rights and ownership to promote equitable benefits from health data, (2) protect people through building trust and addressing patients' concerns, and (3) promote health value by enhancing health systems and services. We also recommend actions for realizing each objective to guide LMICs based on their unique mHealth data ecosystems. These objectives require adopting a regulatory framework for data rights and protection, building trust for data sharing, and enhancing interoperability to use new datasets in advancing healthcare services and innovation.

OBJECTIVE: The aim of this study was to examine trends in the intended users and functionalities advertised by menstrual tracking apps to identify gaps in personas and intended needs fulfilled by these technologies. MATERIALS AND METHODS: Two types of materials were collected: a corpus of scientific articles related to the identities and needs of menstruators and a corpus of images and descriptions of menstrual tracking apps collected from the Google and Apple app stores. We conducted a scoping review of the literature to develop themes and then applied these as a framework to analyze the app corpus, looking for alignments and misalignments between the 2 corpora. RESULTS: A review of the literature showed a wide range of disciplines publishing work relevant to menstruators. We identified 2 broad themes: "who are menstruators?" and "what are the needs of menstruators?" Descriptions of menstrual trackers exhibited misalignments with these themes, with narrow characterizations of menstruators and design for limited needs. DISCUSSION: We synthesize gaps in the design of menstrual tracking apps and discuss implications for designing around: (1) an irregular menstrual cycle as the norm; (2) the embodied, leaky experience of menstruation; and (3) the varied biologies, identities, and goals of menstruators. An overarching gap suggests a need for a human-centered artificial intelligence approach for model and data provenance, transparency and explanations of uncertainties, and the prioritization of privacy in menstrual trackers. CONCLUSION: Comparing and contrasting literature about menstruators and descriptions of menstrual tracking apps provide a valuable guide to assess menstrual technology and their responsiveness to users and their needs.

Journal of the American Medical Informatics Association, ocab212, https://doi.org/10.1093/jamia/ocab212 In the originally published version of this manuscript, Figure 4 was mistakenly published without author approval and a funding statement was omitted. The publisher apologizes for the error. Figure 4 has been removed, and sub-section ‘Theme 1-1’ in section ‘Findings of the analysis of menstrual trackers’ amended to add in-text description to account for figure removal. The following statement has also been added to the Funding section: “During the time of this work KJ was supported by NINR T32NR007969 (PI: Bakken).”

In chronic conditions, patients and providers need support in understanding and managing illness over time. Focusing on endometriosis, an enigmatic chronic condition, we conducted interviews with specialists and focus groups with patients to elicit their work in care specifically pertaining to dealing with an enigmatic disease, both independently and in partnership, and how technology could support these efforts. We found that the work to care for the illness, including reflecting on the illness experience and planning for care, is significantly compounded by the complex nature of the disease: enigmatic condition means uncertainty and frustration in care and management; the multi-factorial and systemic features of endometriosis without any guidance to interpret them overwhelm patients and providers; the different temporal resolutions of this chronic condition confuse both patients and provides; and patients and providers negotiate medical knowledge and expertise in an attempt to align their perspectives. We note how this added complexity demands that patients and providers work together to find common ground and align perspectives, and propose three design opportunities (considerations to construct a holistic picture of the patient, design features to reflect and make sense of the illness, and opportunities and mechanisms to correct misalignments and plan for care) and implications to support patients and providers in their care work. Specifically, the enigmatic nature of endometriosis necessitates complementary approaches from human-centered computing and artificial intelligence, and thus opens a number of future research avenues.