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To verify that federated genomic study sites applied identical preprocessing pipelines without disclosing raw genotypes.

Echocardiography and cardiac catheterization reports capture important clinical assessment information of cardiac function and disease severity. This study explores using open-source transformer-based language models (LMs) that are run locally within an institutional environment as a privacy-preserving alternative to external API-based large LM to systematically extract clinical data from unstructured echocardiography and cardiac catheterization reports, aiming to improve data accessibility for research and patient care.

To test the impact of visual summaries of blood pressure (BP) data (eg, stoplight and gradient displays), within the context of a patient-facing digital application connected to the EHR, on patient judgments about hypertension control.

We evaluated the data requirement for modern AI tools to outperform simpler models in predicting short-term mortality in over 500 000 patients with hemodialysis-dependent kidney failure.

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 [...]

To assess patient awareness, trust, perceived benefits, and risks of artificial intelligence (AI) in clinical care within an urban safety-net health system.

To address the gap between artificial intelligence (AI) model development and clinical implementation by developing a machine-learning model to predict cochlear implant (CI) eligibility based on routine hearing tests, while illustrating 3 practical lessons: reformulation for clinical utility, selection of problem-specific metrics, and handling input data variability.

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.

To develop and validate a prespecified logistic model for detecting mild cognitive impairment (MCI) using MyCog Mobile, a self-administered smartphone-based screening application, and to evaluate a structured simplification that reduces patient burden while maintaining diagnostic accuracy.

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) [...]