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Large-scale propensity score (LSPS) models are increasingly used to control confounding in observational studies, but their reliability in small-sample settings is unclear. Small samples can limit a study's ability to estimate propensity scores accurately and achieve adequate covariate balance, raising concerns about insufficient confounding adjustment. Resulting in small datasets being excluded, despite potentially containing valid information.

Systematic clinical phenotyping using Human Phenotype Ontology (HPO) is central to rare disease diagnosis. However, current disease prioritization (ranking candidate diseases from HPO for a patient) methods face key challenges: they often fail to account for the hierarchical structure of HPO terms, ignore dependencies among correlated terms, and do not adjust for batch effects arising from systematic differences in phenotype documentation across cohorts, institutions, or clinicians. We aim to develop [...]

Machine-learning-based clinical risk prediction models are increasingly used to support decision-making in healthcare. While class-imbalance correction techniques are commonly applied to address rare outcomes, their impact on probabilistic calibration remains insufficiently understood. This study evaluated the effect of widely used resampling strategies on both discrimination and calibration across real-world clinical prediction tasks.

Data modernization (DM) seeks to transform data and information systems in public health (PH) to support core activities. This study characterizes incumbent PH workers who perform informatics and data-centric roles that are critical to DM activities.

To evaluate whether real-time electronic health record (EHR)-based secure messaging between emergency department (ED) clinicians and prior discharge teams influences ED disposition decisions for patients re-presenting within 30 days of hospital discharge.

Human Factors (HF) methods have potential to ensure the design of health information technology (HIT) is safe and usable, but limited research has examined what HF methods have been applied or could be applied to HIT. This study aimed to identify key HF and safety analysis methods used and/or recommended by HF experts working in healthcare or non-healthcare industries that may be suitable for supporting the design, redesign and configuration [...]

To propose an equity-by-design agenda for socially assistive robots (SARs) as embodied digital health informatics interventions.

Secondary headaches require urgent recognition due to potentially devastating consequences if untreated. Despite established clinical "red flag" criteria, identifying patients needing immediate evaluation remains challenging in primary care. This study developed and evaluated a large language model (LLM)-based multi-agent clinical decision support system for interpretable secondary headache diagnosis.

Clinical named entity recognition tools commonly map free text to Unified Medical Language System (UMLS) Concept Unique Identifiers (CUIs). For many downstream tasks, however, the clinically meaningful unit is not a single CUI but a concept set comprising related synonyms, subtypes, and associated concepts. Constructing these sets is labour-intensive, inconsistently performed, and poorly supported by existing tools.

Using AI algorithms can exacerbate health disparities if care or resources are allocated away from underserved populations. We evaluated an algorithm for its potential to worsen health disparities across different clinical use cases.