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To highlight the importance of reporting negative results in large language model (LLM) research, particularly as these systems are increasingly integrated into healthcare.

Patient portals (PP) can enhance healthcare access for pediatric patients and families but disparities persist among patients with diverse preferred languages. Our objective was to optimize the PP user interface and associated activation workflows to improve activation rates for pediatric families with Spanish as preferred language (Spanish-speaking patients and/or parents/guardians).

Value-based care (VBC) represents a fundamental shift from volume-driven reimbursement to models focused on improving patient outcomes and reducing costs. Informatics plays an essential, but often underappreciated, role in enabling VBC. Traditional discussions of informatics emphasize data and technology; however, a broader sociotechnical view highlights how people, organizations, workflows, and policies interact with technology to influence the success of VBC initiatives. In this article, we apply the Informatics Stack as [...]

Patients often receive health care from multiple organizations. Privacy Preserving Record Linkage (PPRL) is a technology for linking patient records without releasing personally identifiable information. We compared a commercial PPRL tool that uses the XGBoost machine learning algorithm with Care Everywhere (CE), a widely used rule-based patient linkage module.

Predictive models of suicide risk have focused on features extracted from structured data found in electronic health records, with limited consideration of predisposing life events (LE) expressed in unstructured clinical text such as housing instability and marital troubles. This study aims to expand upon previous research, demonstrating how high-performance computing (HPC) and machine learning methodologies can be used to extract and annotate 8 LE across all Veterans Health Administration (VHA) [...]

This study examined the use of machine learning (ML) and domain-specific enrichment in patient-generated health data, in the form of free-text meal logs, to classify meals on alignment with different nutritional goals.

Clinical practice guidelines (CPGs) provide evidence-based recommendations for patient care; however, integrating them into artificial intelligence (AI) remains challenging. Previous approaches, such as rule-based systems or black-box AI models, face significant limitations, including poor interpretability, inconsistent adherence to guidelines, and narrow domain applicability. To address this, we develop and validate CPGPrompt, an auto-prompting system that converts narrative clinical guidelines into large language models (LLMs).

Temporal drift, defined as changes over time in underlying data distributions, can degrade the performance of clinical prediction models. In head and neck cancer (HNC) radiotherapy, evolving proton and carbon ion therapies may shift the risk of oral mucositis over time. This study aimed to compare machine learning (ML) strategies for mitigating temporal drift in predicting grade ≥2 oral mucositis among patients treated with particle therapy.