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We report on findings from a meeting convened by the American College of Medical Informatics (ACMI) to characterize aspects of the patient experience that could be improved using informatics.

Home healthcare (HHC) clinical notes contain critical infection indicators that clinicians need in structured "indicator + context" pairs. Data sparsity and limited computing resources hinder automated extraction in decentralized HHC settings. This study developed and evaluated a resource-efficient pipeline using instruction-tuned, moderate-sized large language models (LLMs) to address these barriers. To address the data sparsity challenge, we also assessed the impact of a targeted LLM-based data augmentation strategy.

Leveraging routine electronic health records (EHR) for dementia detection is a growing field, but quality and clinical utility of existing models are unclear. This systematic review aimed to evaluate performance, methodological quality, and risk of bias of EHR-based dementia prediction models.

The increasing use of machine learning (ML) in clinical care makes fairness a central issue. Fairness, defined as the absence of disparities across individuals or subgroups, shares several parallels with concepts in clinical epidemiology. The objective was to apply clinical epidemiology frameworks to the evaluation of ML fairness, both to enhance understanding of these concepts and to strengthen fairness assessments.

To evaluate the effectiveness of generative query expansion for biomedical literature retrieval.

Mixed methods are used to holistically understand the "what", "how", and "why" questions within a single study by integrating quantitative and qualitative methods. Although this approach has demonstrated value in other disciplines, their use in applied clinical informatics research remains largely unexplored. This methodological review characterized the types of informatics intervention studies that used mixed methods designs and the specific methods of evaluation and reporting.

Artificial Intelligence models are increasingly used in health care, yet global performance metrics can mask variations in reliability across individual patients or subgroups with shared attributes, called patient profiles. This study introduces predictive performance precision analysis in medicine (MED3pa), a method that identifies when models are less reliable, allowing clinicians to better assess model limitations.

Despite widespread implementation of predicted patient wait time information systems in hospital emergency departments (EDs), the relationship between quality of announced wait time information and ED overcrowding mitigation remains unclear. This study investigates how prediction accuracy, update frequency, and patient adoption rates affect ED overcrowding level.

To quantify run-to-run reproducibility of Gemini 3 Flash Preview and GPT-5.2 for trial-success classification across temperature and reasoning/thinking settings and determine whether single-run reporting suffices.

Intelligent agent-driven research co-pilots, leveraging advances in generative AI, are transforming how scientists access biomedical knowledge. This paper presents Med.ai ASK, an agentic question-answering system designed to address biomedical inquiries through dynamic retrieval augmentation and tool-driven reasoning. We aim to develop a system capable of parsing the nuance in biomedical scientists' research questions to provide reliable, grounded responses that are more accurate than other generative AI solutions.