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Mapping clinical classification systems, such as the International Classification of Diseases (ICD), is essential yet challenging. While the manual mapping method remains labor-intensive and lacks scalability, existing embedding-based automatic mapping methods, particularly those leveraging transformer-based pretrained encoders, encounter 2 persistent challenges: (1) linguistic variation and (2) varying granular details in clinical conditions.

Federated Ecosystems for Analytics and Standardized Technologies (FEAST) is a modular, cloud-based platform developed through the ARPA-H Biomedical Data Fabric initiative to enable secure, federated analysis of real-world biomedical data. To guide and iteratively refine its modular design, the FEAST team conducted a cross-institutional survey to systematically identify and prioritize research needs related to authorized-access data across diverse biomedical domains. This study presents a structured synthesis of submitted use cases [...]

Advancing health through informatics requires attending to justice. Recent policy changes in the United States have introduced significant barriers to promoting justice within informatics due to targeted funding cuts and hostility to science, especially science that prioritizes justice.

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

Population health management programs coordinate care for over 80 million Medicaid beneficiaries but lack systematic clinical decision support for determining when to intervene and which interventions to select for patients with complex conditions. Our objective was to develop and validate a clinical decision support system integrating acuity prediction and intervention selection models for population health management programs.

Ambient digital scribe (ADS) platforms, which combine ambient speech recognition and large language models to generate clinical documentation, are currently undergoing rapid clinical adoption. Early data suggest that ADS utilization may reduce documentation burden and improve provider efficiency; however, the ethical implications of this largely unregulated technology remain relatively unexamined.

To understand the heterogeneous treatment effects of electronic alerts for acute kidney injury (AKI).

We discuss implications of potential ascertainment biases for studies examining diabetes risk following SARS-CoV-2 infection using electronic health records (EHRs). We quantitatively explore sensitivity of results to misclassification of COVID-19 status using data from the U.S.-based Diabetes in Children, Adolescents and Young Adults (DiCAYA) Network on children (≤17 years) and young adults (18-44 years).

To develop AutoReporter, a large language model (LLM) system that automates evaluation of adherence to research reporting guidelines.