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Interpretive clinical artificial intelligence (AI), in which a clinician interprets an image or signal and an AI system offers a competing or confirming read, is increasingly routine in radiology, pathology, dermatology, and endoscopy. Oversight of these systems assumes that independent human judgment occurred before AI influence entered the decision. The informatics infrastructure, meaning the electronic health record, its audit logs, and the reporting systems built on them, does not verify [...]

Clinician trust in artificial intelligence (AI) is a frequently cited barrier to adoption, yet there is limited empirical evidence on how transparency features influence trust. This study aimed to address this evidence gap.

Electronic health record (EHR) audit logs capture clinician-EHR interaction patterns, but most audit log research relies on aggregated measures (eg, total time). We investigated how audit logs could be modeled using large language model (LLM) architectures to learn underlying workflow sequences during clinician-EHR interactions.

The objective of this study was to construct a harmonized SNOMED Clinical Terms (SNOMED CT) components quality assurance framework for identifying, categorizing, and assessing data quality issues in SNOMED CT components.

Design, implement, and evaluate a standards-based architecture for secure federated analytics across heterogeneous Trusted Research Environments (TREs) that aligns with the Five Safes Framework, without requiring TREs to modify their internal governance, infrastructure, or operational practices.

In this Perspective, we provide the rationale for the retention of safety-critical certification criteria for health information technology by the Office of the National Coordinator for Health Information Technology (ONC). To not do so puts all future American patients at risk. The proposed deregulatory actions are based on faulty premises, will lead to serious adverse patient outcomes, and sets a dangerous precedent. Public safety regulations should be held to a [...]

To evaluate how cross-terminology mapping of International Classification of Diseases (ICD)-coded diagnoses is associated with preservation of diagnostic distinctions and traceability in electronic health record (EHR)-derived phenotypes for genomic research.

To determine whether large language models can compute the Charlson Comorbidity Index (CCI) from unstructured, German-language diagnosis lists with reliability approaching that of expert human consensus.

To determine whether statewide health information exchange (HIE) data can support practice-level prediction of short-term psychiatric emergency presentation, and to compare a locally trained model with general-purpose clinical risk scores.

The Code Map Services/Common Data Model Harmonization Phase III (CDMH-III) project aims to create services allowing researchers to streamline and establish standardized model-to-model/data standards mappings and transformation rules leveraging common data models (CDMs) and common data elements (CDEs) to address the challenge of repeated mapping efforts.