We conducted formal analyses of a scoring system for a contraception decision aid to support transgender and gender-nonconforming (TGNC) assigned female at birth (AFAB) individuals. For this purpose, we developed a methodology framework to assess the weights for each decision factor, to conduct univariate and multivariate sensitivity analyses, and to provide data visualization, which led to successful identification of critical values in weight assignment that can impact the recommendations generated by the system. These analyses made critical contributions to development and validation of the system's knowledge base, providing explainable recommendations, and conducting additional research related to system users and functions. Future research is required to explore high-dimensional sensitivity analyses, to address technical issues identified, and to examine the generalizability of the methodology to other applications.

Large language models (LLMs) excel in natural language processing (NLP) but struggle with domain-specific complexities in electronic health records (EHRs). We demonstrate that retrieval-augmented generation (RAG) enhances LLMs for dietary supplement (DS) information extraction. By testing models like Llama-3 with diverse retrievers on tasks including entity recognition and usage classification, task-aligned retrieval outperforms reliance on model size or specialization. Smaller general models paired with optimized retrievers match or exceed specialized counterparts-structured retrieval aids complex tasks (e.g., triple extraction), while semantic retrieval improves classification. Results challenge assumptions that larger or domain-specific models are superior, emphasizing dynamic knowledge integration over brute-force scaling. This approach offers practical strategies for clinical NLP, enabling efficient EHR analysis without massive resources. Prioritizing retrieval strategies over model size advances tools for evidence-based healthcare, highlighting adaptability and cost-effectiveness in real-world medical applications.

This study is part of the OsteoPorotic fracTure preventION System (OPTIONS) project which aims to develop an evidence-based mobile application for older adults transitioning from skilled nursing facilities (SNFs) back to the community after lower limb fractures. The app promotes exercise, nutrition, and bone health medications to prevent future fractures. Using a Design science framework, app requirements were identified by synthesizing scientific knowledge, clinical expertise, and end-user needs. An initial mockup was developed based on these specifications and iteratively refined through design sessions incorporating end-user feedback. The final OPTIONS app features four core functions: (1) task-based self-management support, (2) personalized exercises and education, (3) motivational messaging, and (4) progress tracking allowing users to monitor their progress through visualizations. By addressing usability challenges for older adults, the app provides a personalized, engaging experience for continuous health management.

Medical diagnosis prediction plays a critical role in disease detection and personalized healthcare. While machine learning (ML) models have been widely adopted for this task, their reliance on supervised training limits their ability to generalize to unseen cases, particularly given the high cost of acquiring large, labeled datasets. Large language models (LLMs) have shown promise in leveraging language abilities and biomedical knowledge for diagnosis prediction. However, they often suffer from hallucinations, lack structured medical reasoning, and produce useless outputs. To address these challenges, we propose KERAP, a knowledge graph (KG)-enhanced reasoning approach that improves LLM-based diagnosis prediction through a multi-agent architecture. Our framework consists of a linkage agent for attribute mapping, a retrieval agent for structured knowledge extraction, and a prediction agent that iteratively refines diagnosis predictions. Experimental results demonstrate that KERAP enhances diagnostic reliability efficiently, offering a scalable and interpretable solution for zero-shot medical diagnosis prediction.

Cardiovascular event adjudication is essential in clinical trials but relies on manual chart review that is slow, variable, and expensive. We present a two-stage framework that automates adjudication of cardiovascular deaths using large language models (LLMs). First, a few-shot LLM extracts structured evidence (event, span, negation, date) from unstructured clinical documents. Second, a Tree-of-Thoughts adjudicator aligns its reasoning with clinical endpoint committee (CEC) guidelines to classify deaths as cardiovascular or non-cardiovascular and produce an auditable rationale. On Lilly clinical-trial data, extraction achieved precision 0.96, recall 0.71 (F1 0.82), and adjudication attained 0.68 accuracy (GPT-4 ToT), outperforming a summarizer-plus-adjudicator baseline. We introduce CLEART, a rubric-based automated score that quantifies rationale quality across clarity, consistency, detail, guideline adherence, relevance, and timeline accuracy (overall 0.67), highlighting temporal reasoning and relevance as key areas for improvement. This approach can reduce adjudication time and variability while increasing transparency.

In the last decade, varied state-level policies on contraception access have highlighted the importance of large-scale public health datasets in assessing the impact of these policies on reproductive healthcare access. This study uses PRAMS Phase 8 (2016-2022) data to examine predictive factors of postpartum birth control use, hypothesizing that state policies impact contraception uptake and barriers, particularly regarding the expansion of immediate postpartum long-acting reversible contraception (LARC) reimbursement policies. Two distinct logistic regression models were constructed, and the inclusion of state as a covariate significantly reduced residual deviance (ΔDeviance = 13.696, p=0.0002). This finding indicates that state of residence is a statistically significant predictor of postpartum birth control usage. This study underscores the significant impact of state-level and institutional policies on birth control usage and LARC uptake, emphasizing the need for informed policy changes and patient-centered strategies to address disparities and improve postpartum reproductive health outcomes.

As teenagers increasingly turn to social media for health-related information, understanding the values of teen-targeted content has become important. Although videos on healthy lifestyles and self-improvement are gaining popularity on social media platforms like YouTube, little is known about how these videos benefit and engage with teenage viewers. To address this, we conducted a thematic analysis of 44 YouTube videos and 66,901 comments. We found that these videos provide various advice on teenagers' common challenges, use engaging narratives for authenticity, and foster teen-centered communities through comments. However, a few videos also gave misleading advice to adolescents that can be potentially harmful. Based on our findings, we discuss design implications for creating relatable and intriguing social media content for adolescents. Additionally, we suggest ways for social media platforms to promote healthier and safer experiences for teenagers.

Patient Generated Health Data empower patients and supports their longitudinal care, with several studies and initiatives attempting to integrate these data into electronic health records of patients. However, studies have indicated clinicians' desire and interest in contextual information about these data. There are limited studies that consider contextual and provenance information about these data at the point of collecting the data. This has reliability, trust and usability implications for PGHD shared with electronic health records (EHR). This paperseeks to describe how an ontology-driven model was employed to capture PGHD with provenance and context, sufficient for clinical decision making. We also evaluatedthe modelusingtwo PGHDsources, and shared theprocess of sharing this information with an EHR in standardized RDF/FHIR formats. Future studies will seek to evaluate the modelin a practical context with clinicians, to ensure alignment with their clinical practice and workflow.

Accurately identifying disease diagnoses from electronic health records (EHRs) is crucial for clinical/biomedical research; however, this is challenging when diagnoses are complex and require data from several sources, e.g., multiple myeloma (MM) and its precursor condition, MGUS. Leveraging the national Veterans Health Administration EHRs, we developed and validated a large language model (LLM)-based pipeline that utilizes only clinical notes from randomly selected patients identified via ICD codes for MGUS/MM. Among the evaluated LLMs and alternative approaches, Llama-3-8B-based pipeline with prompt engineering achieved the best performance. This pipeline not only saved the preprocessing steps and shortened the overall processing time but also outperformed rule-based or machine learning-based methods for identifying MGUS and achieved comparable performance for MM, solely relying on clinical notes. Our work demonstrates that the developed LLM-based pipeline can efficiently and effectively identify MGUS/MM diagnoses to replace manual chart abstraction and rule- or machine learning-based natural language processing methods.

Of the millions of Americans with chronic health conditions (CHCs), a growing number are coming to identify as disabled due to their CHCs. Their definition of disability differs substantially from how health informatics has traditionally thought about disability and CHCs. Rather than seeing disability as worsening CHCs that ought to be prevented, disability community definitions see disability as a form of social difference, akin to race and gender. To understand the impact of this perspective on disability on people with CHCs, we interviewed 15 participants who identify as disabled due to their CHCs. We found that it was often difficult to develop a disability identity, but doing so had significant benefits: greater self-acceptance, accessibility, and community. We conclude by identifying opportunities for health informatics to enable more people with CHCs to develop and benefit from a disability identity.

Health literacy significantly impacts patient outcomes, yet many struggle to understand complex medication information. Medication non-adherence often results from poor comprehension of drug instructions and contributes to preventable hospitalizations and poor treatment outcomes. With the increasing use of digital health interventions, AI-powered chatbots present an opportunity to improve patient access to understandable and personalized medication information. This study evaluates the usability, accessibility, and effectiveness of EMPATHICA, an AI-powered chatbot designed to provide patient-centric medication information. The study assesses whether chatbot-generated responses improve patient comprehension and engagement. The research observed the interaction between participants and the web-based application, where participants asked the chatbot to measure the chatbot's usability and accuracy using various qualitative and quantitative measures and expert physician evaluation of the responses. AI-driven chatbots have the potential to bridge health literacy gaps by providing clear and accessible medication information. By evaluating EMPATHICA, this study contributes to the growing field of AI applications supporting patient-informed medication use.

Recent advances in vision-language models (VLMs) have enabled automatic radiology report generation, yet current evaluation methods remain limited to general-purpose NLP metrics or coarse classification-based clinical scores. In this study, we propose a clinically informed evaluation framework for VLM-generated radiology reports that goes beyond traditional performance measures. We define a taxonomy of 12 radiology-specific error types, each annotated with clinical risk levels (low, medium, high) in collaboration with physicians. Using this framework, we conduct a comprehensive error analysis of three representative VLMs, i.e., DeepSeek VL2, CXR-LLaVA, and CheXagent, on 685 gold-standard, expert-annotated MIMIC-CXR cases. We further introduce a risk-aware evaluation metric, the Clinical Risk-weighted Error Score for Text-generation (CREST), to quantify safety impact. Our findings reveal critical model vulnerabilities, common error patterns, and condition-specific risk profiles, offering actionable insights for model development and deployment. This work establishes a safety-centric foundation for evaluating and improving medical report generation models. The source code of our evaluation framework, including CREST computation and error taxonomy analysis, is available at https://github.com/guanharry/VLM-CREST.

Clinical Named Entity Recognition (NER) is vital for extracting structured data from clinical text, but ensuring model generalizability across institutions remains challenging. This study compares two approaches: (1) Federated Learning (FL), a privacy-preserving decentralized method, and (2) Large Language Models (LLMs) trained on diverse corpora. We evaluate Visual Acuity (VA) extraction from ophthalmology notes at Stanford (USA) and Moorfields Eye Hospital (UK), using BERT-based models, FL strategies (FedAvg, STWT), and LLMs (Llama-3-70B, Mixtral-8x7B). Results show that FL significantly improves generalization, with STWT outperforming FedAvg in stability and accuracy. LLMs demonstrate strong performance on MEH data but struggle with structured Stanford notes. These findings highlight FL's effectiveness for cross-institutional learning while revealing domain-specific limitations of LLMs, underscoring the need for tailored approaches to clinical NER.

Personalized food recommendation systems must balance various constraints, including medical guidelines, nutritional needs, and individual preferences. However, existing methods often struggle with overly restrictive queries, frequently failing to generate recommendations when no exact match exists. To address this challenge, we propose an adaptive knowledge graph (KG) retrieval framework that integrates Large Language Models (LLMs) for intelligent constraint relaxation. Our approach dynamically prioritizes constraints, ensuring that critical dietary requirements remain intact while selectively relaxing less essential ones. By leveraging LLM-driven constraint analysis and structured relaxation strategies, our system significantly enhances recommendation coverage without compromising key dietary needs, while maintaining optimal recommendation performance. Experimental results on both the original and the extended-constraint dataset demonstrate that our method successfully retrieves recommendations in cases where previous approaches fail, achieving higher retrieval accuracy and a balanced tradeoff between flexibility and adherence to dietary constraints. The code is public available at https://github.com/zpf0117b2/adaptiveRetrieval.

Documenting clients, screenings and vaccinations administered is of particular importance during mass vaccination, since information regarding uptake is critical for monitoring adverse effects and vaccine efficacy. This is especially essential when a newly-developed vaccine is being dispensed or when multiple doses of vaccine are needed per person. Despite these needs, there is no uniform or integrated system for effective vaccine data collection. This work focuses on modernizing public health infrastructure through informatics. In this paper, we describe and analyze five types of electronic technologies for registration and screening in vaccination clinics. We contrast their functionalities, usability and operations performance based on time-motion studies and service data collected during actual influenza vaccination campaigns. We evaluate their dispensing performance under an optimal dispensing clinic design. Our analysis shows that each of these electronic technologies can improve overall throughput by 16% to 45%. Based on our findings, we design a prototypical registration and screening system with integrated information flow that can be used for dispensing, monitoring and assessing mass vaccination. The system connects to the local Immunization Information System and electronic medical record systems. The design is flexible and adaptable for different types of medical countermeasures, and is suitable for regional public health departments. Our approach bridges research and public health informatics in a practical way, demonstrating how data can guide both system design and public health response planning.