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We propose Heterogeneity-aware Collaborative One-shot Lossless Algorithm for Generalized Linear Model (COLA-GLM-H), a novel one-shot lossless distributed algorithm that enables the integration of heterogeneous multi-institutional data while preserving patient privacy by avoiding patient-level data sharing.

This study evaluates the impact of self-management and support of m-health applications on medication adherence (MA) and the corresponding long-term medical expenditures among patients with Type 2 Diabetes (T2D), using an analytic framework generalizable to other chronic conditions.

Patients value access to their medical reports on patient portals, but the terminology in those reports can cause confusion and anxiety. Can the artificial intelligence (AI) simplification of radiology reports into plain language improve patient comprehension?

To develop the first comprehensive, standardized annotated corpus of Chinese online health information (OHI) using the full 16-item DISCERN instrument and to establish a reliable annotation process that supports automated quality assessment.

Computational phenotyping from electronic health records (EHRs) is essential for clinical research, decision support, and quality/population health assessment, but the proliferation of algorithms for the same conditions makes it difficult to identify which algorithm is most appropriate for reuse.

To introduce a novel, standardised approach to evaluating AI prediction models in balancing effectiveness, efficiency and utility, using a sepsis prediction model case study.

To guide research data services (RDS) teams in managing researcher variability (eg, differing deadlines, funding, expertise) when honest-brokering data, we present a framework based on operations management principles and a food service analogy.

To build natural language processing (NLP) strategies to characterize measures of genetic counseling (GC) efficiency and classify measures according to phase of GC (pre- or post-genetic testing).

Stigmatizing language (SL) in Electronic Health Records (EHRs) can perpetuate biases and negatively impact patient care. This study introduces a novel method for automatically detecting such language to improve healthcare documentation practices.

Electronic health records (EHRs) lack a widely adopted standard for recording transgender and gender diverse (TGD) status, complicating research on TGD health. Computational models have been developed to identify TGD individuals in EHRs; however, gaps remain in understanding which components contribute to stronger phenotyping approaches. This scoping review evaluates EHR-based models for identifying TGD individuals, focusing on identifier types, performance, external validation, and ethical reporting to guide best practices.