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To evaluate whether an electronic health record (EHR) cohort has sufficient data quality to support target trial emulation design elements, using Medicare claims as a reference for billing-derived domains and linked EHR-claims data to illustrate the incremental value of linkage.

Early recognition of unplanned ICU transfer is critical for improving outcomes, yet existing pediatric warning systems lack accuracy and timeliness. We developed machine learning models predicting unplanned ICU transfer up to 24 hours in advance.

High-quality routine health information systems (RHIS) are critical for improving maternal, newborn, and child health (MNCH), to facilitate clinical decision-making and track service delivery. Despite widespread use of health facility data forms for capturing indicators, there are no evidence-based standardized formats or content guidelines for MNCH. The aim of this scoping review was to identify, synthesize, and explore literature related to health facility data forms regarding data-element content, design, and [...]

Machine learning (ML)-based cluster analysis is a common method for subtyping medical conditions and presentations of disease states. Recent advancements in algorithms and increasing access to vast healthcare data have further increased the use of such ML models. However, practical implementation is lacking, largely due to methodological limitations and insufficient reporting and clinical contextualization in the extant literature, with no existing guidelines for this purpose.

We present a method and open-source prototype to augment Clinical Quality Language (CQL) using a large language model (LLM) in the execution loop for querying unstructured text.

This study evaluated the optimization of a medication-related clinical decision support system (MRCDSS) in a 1000-bed general hospital in China by analyzing clinician responses.

To evaluate the utility of large language models (LLMs) without and with retrieval-augmented generation (RAG) resources for automating Research Electronic Data Capture (REDCap) support at a large academic medical center, focusing on ticket complexity, categorization, and response drafting.

This study examines disparities in the use of interoperability features within the EHR among family medicine physicians in the United States, particularly in areas with higher social deprivation index and rural settings.

Artificial intelligence is used in healthcare to identify and manage health conditions across diverse patient populations and clinical settings, but biases in algorithms can perpetuate and exacerbate inequalities in health and healthcare delivery. While some research has focused on addressing this critical issue, there is a lack of comprehensive information on the types of strategies employed for bias mitigation in the use of artificial intelligence in healthcare and the effectiveness [...]

Electronic health record (EHR) data are prone to missingness and errors. Previously, we devised an enriched chart review protocol where a "roadmap" of auxiliary diagnoses was used to recover missing values. Still, chart reviews are expensive and time-intensive, limiting the number of patients whose data can be reviewed. Now, we investigate the accuracy and scalability of a roadmap-driven algorithm, based on International Classification of Diseases, 10th revision (ICD-10) codes, to [...]