health literacy assessment

Psychiatrists often struggle to efficiently interpret longitudinal patient trajectories due to the heterogeneous integration of structured and unstructured clinical data—particularly free-text clinical notes. To address this, we propose VIEWER: an open-source, scalable visual analytics tool introducing the first modular, participatory clinical visualization framework tailored to mental health contexts. VIEWER integrates FHIR-compliant data ingestion, natural language processing (NLP) for semantic parsing of clinical notes, and interactive visualizations (built with D3.js and Plotly), enabling joint dynamic querying and point-of-care display of both structured metrics and unstructured text. Deployed across the UK’s largest NHS mental health trust, it processes data from 409,000 patients. Evaluation involving over 1,000 clinicians demonstrated significantly reduced task completion time, high user satisfaction, and successful operational adoption with continuous iterative refinement.

Health sciences—hypothesis-driven and emphasizing reproducibility—clash with visual analytics—iterative, exploratory, and interaction-dependent—leading to cross-disciplinary challenges: terminological misalignment, divergent expectations for data preparation, conflicting validation criteria, and contradictory interpretability requirements. To address this, we propose an integrative framework structured along three dimensions: cultural adaptation, standard harmonization, and process coordination. It specifies seven concrete, actionable steps—the first systematic effort to bridge confirmatory and exploratory research paradigms. Grounded in interdisciplinary co-design, the framework incorporates integrated workflow modeling, a terminology alignment tool, and a multi-stage quality validation benchmark. It enables clinically relevant, reliable, and reproducible collaborative analysis. By fostering deep methodological integration, the framework advances a unified research agenda that enhances scientific rigor, practical feasibility, and clinical translatability of hybrid analytical approaches.

This study addresses the lack of clear mechanisms for effective communication between healthcare providers and culturally diverse immigrant patients in high-income countries—a gap that hinders the design of culturally appropriate health technologies. Through semi-structured interviews with 15 healthcare practitioners serving immigrant communities, the research systematically identifies four key cultural competence strategies: recognition, community engagement, incremental care, and adaptive communication. Building on these insights, the work proposes a contextualized and actionable design framework for health technologies tailored to immigrant populations. This framework offers human-computer interaction (HCI) researchers and practitioners principled guidance and practical implications for developing culturally sensitive digital health tools that meaningfully support cross-cultural care delivery.

This study addresses the lack of systematic research on data visualization design practices in real-world public health contexts, which hinders the evaluation of their effectiveness and suitability for public communication. For the first time, it collects and analyzes over 4,000 visualizations from the websites of more than 20 domestic and international public health agencies. Combining manual annotation with qualitative content analysis, the work systematically codes key features—including chart types, accessibility, decorative elements, and design flaws—and establishes the first reusable annotated dataset and classification framework for public health visualizations. The findings reveal prevalent design patterns and common shortcomings, offering empirical evidence to improve the communication of official health data. The study also publicly releases a visualization corpus to support future research in this domain.

Manual review of unstructured electronic health record (EHR) text to construct reference standards for large-scale database studies is time-consuming and labor-intensive. Method: We propose an NLP-driven, multi-wave adaptive sampling validation framework that integrates NLP-assisted annotation, quantitative bias analysis, and a predefined termination rule based on error convergence—dynamically optimizing both sample selection and stopping timing while preserving measurement accuracy. Results: Empirical evaluation shows that NLP reduces per-record review time by 40%; multi-wave sampling with termination criteria skips 77% of records requiring no manual review, with negligible impact (<0.5 percentage points) on final algorithm performance estimation bias. The framework significantly improves validation efficiency, feasibility, and scalability, offering a reproducible, resource-efficient, and standardized validation pathway for coded-algorithm-based health outcome measurement.

This study addresses the challenge of effectively utilizing large-scale, heterogeneous patient-generated health data in clinical practice, where time constraints and limited data literacy among healthcare professionals hinder meaningful engagement. To bridge this gap, the authors propose an interactive system that integrates large language model (LLM)-generated summaries with a natural language conversational interface, enabling clinicians to rapidly comprehend and flexibly explore multimodal health data within cardiovascular disease risk reduction scenarios. Embedded into clinical workflows, the system combines automated summarization, dialog-driven interaction, and multimodal visualization. A mixed-methods evaluation involving 16 healthcare professionals demonstrated that AI-generated summaries significantly enhanced data interpretability, while conversational capabilities facilitated adaptive exploration, effectively mitigating disparities in data literacy. The study also identifies critical challenges concerning transparency, privacy preservation, and risks of overreliance on AI assistance.

Medical visualization lacks a systematic design process that simultaneously addresses stakeholder differentiation, logical coherence across design stages, and task-type adaptability. Method: This study proposes a cognition-driven, systematic design research model grounded in literature review and cross-disciplinary practice. It innovatively introduces a binary task subclassification—hypothesis-driven versus non-hypothesis-driven—first applied in medical visualization, and refines each design phase according to the complexity of underlying medical problems. The model explicitly emphasizes stakeholder identification, cognitive progression between stages, and fine-grained classification of inferential versus descriptive tasks. Contribution/Results: The model was applied to guide the development of a medical visual analytics method and retrospectively analyzed three canonical works. Evaluation confirms its theoretical rigor, practical feasibility, and cross-case generalizability—demonstrating effectiveness in enhancing systematicity, operationality, and transferability in medical visualization design.

This study addresses systemic methodological deficiencies in contemporary clinical statistical research, including overreliance on hypothesis testing, predictive models detached from patient realities, flawed meta-analytic practices, and unwarranted confidence in conclusions. For the first time, these previously isolated issues are unified under a “systemic dysfunction” framework. Through critical methodological analysis, institutional critique, and interdisciplinary perspectives, the work reveals that the root causes lie in the complicity among educational systems, expert role definitions, and research governance structures. It demonstrates how current practices adversely impact clinical decision-making and calls for structural reforms in education, peer review, and policy to enhance the reliability and clinical relevance of research findings.

This study addresses the limited understanding of how adolescents conceptualize the role of artificial intelligence (AI) in health-related learning and management. Focusing on youth aged 14 to 17 within the context of familial celiac disease diagnosis, the research employed design fiction and co-design methods across seven qualitative workshops. Findings reveal a shift in adolescents’ perception of AI—from a mere efficiency tool toward a medium for meaning-making and collaborative sensemaking. The study proposes a novel perspective wherein AI should support adolescent autonomy, reflective capacity, and family-based health collaboration. Four distinct AI roles emerged: facilitating health comprehension and help-seeking intentions, alleviating cognitive load, aiding family health management, and offering guidance that respects user autonomy. Notably, participants expressed ambivalent attitudes toward AI-provided emotional support.