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Long COVID (LC) remains poorly understood, and there is a critical need for advanced computational tools to better identify and characterize patients. In this study, we use summarized symptom reports by RECOVER-Adult cohort participants linked to EHR data to characterize patients and train a computable phenotype algorithm of LC.

The analysis of care trajectories derived from electronic health records and claims data has become increasingly common in biomedical informatics. This has enabled large-scale studies of care processes, yet widely used binary code representations result in high-dimensional, sparse data that fail to capture semantic relationships between medical concepts. Learning dense vector representations (embeddings) has emerged as a promising approach to address these limitations. We aimed to construct and share joint [...]

The successful integration of Machine Learning (ML) models into clinical practice remains limited, as they often lack the standardized, quantifiable risk measures essential for clinical workflows. This study, therefore, aims to demonstrate the Unified Auto Clinical Scores (Uni-ACS) method as a means to translate ML predictions into interpretable biostatistical formats, a translation critical for clinical adoption and alignment with evidence-based practice guidelines.

To describe considerations for integration of human and artificial intelligence for creating a postmarketing surveillance system capable of timely and reliably identifying causal effects of medications on safety endpoints.

Distributed Research Networks (DRNs) offer significant opportunities for collaborative multi-site research and have significantly advanced healthcare research based on clinical observational data. However, generating high-quality real-world evidence using fit-for-use data from multi-site studies faces important challenges, including biases associated with various types of heterogeneity within and across sites and data sharing difficulties. Over the last 10 years, Privacy-Preserving Distributed Algorithms (PDA) have been developed and utilized in numerous national and [...]

To argue that diagnostic and predictive AI should be evaluated by both classification performance and the downstream work their outputs create.

The use of ambient AI documentation tools is rapidly growing in US hospitals and clinics. Such tools generate the first draft of clinical notes from scribed patient-provider conversations, which clinicians can then review and edit before signing into electronic health records (EHR). Understanding how and why clinicians make modifications to AI-generated drafts is critical to improving AI design and clinical efficiency, yet it has been under-studied. This study aims to [...]

Biobanks are essential for intelligent medicine but face fragmentation and heterogeneity. No standardized framework exists for assessing biobank data value using public information; this study addresses this gap.

Using 15 years of hospital survey data (2008-2023), we examined rural-urban trends in electronic health records (EHR) adoption and advanced capabilities for data sharing, public health reporting, and care improvements.