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To systematically synthesize how data quality (DQ) in disease and health outcomes registries has been conceptualized, assessed, and improved, emphasizing artificial intelligence (AI) and machine learning methods within registry-based DQ workflows.

While there are numerous mobile health (mHealth) apps on the market, previous research has highlighted their risks to privacy and security. Therefore, an instrument to assess the privacy and security of these apps may be helpful for ensuring consumers' data safety. We therefore conducted this rapid scoping review with the objective of surveying the landscape for instruments used to measure and quantify privacy and/or security of mHealth apps in the [...]

To examine posthumous health data donation as a pathway toward more representative datasets used to develop healthcare artificial intelligence (AI).

Patient portal messaging connects patients with their healthcare teams but is underused by Spanish-speaking patients.

To externally validate a pediatric surgical site infection (SSI) prediction model, assess institutional and temporal transportability, and evaluate recalibration strategies to support adaptation of registry-derived predictions for clinical decision support (CDS).

To develop an electronic health record (EHR)-based multimorbidity-weighted index (eMWI) using computable phenotypes mapped to the Observational Medical Outcomes Partnership (OMOP) common data model and previously validated physical functioning-based condition weights.

Cardiac surgeries are associated with a range of serious postoperative complications, and current predictive models often rely on static summaries of preoperative and intraoperative data compiled after the case. Such development limits their utility in real-time clinical settings. To address this challenge, we developed TE-Transformer, an end-to-end deep learning framework that continuously ingests heterogeneous electronic health record (EHR) data-including preoperative variables, high-frequency vital signs, medication administration records, and brief surgical [...]

Older adults face disproportionate risk of diagnostic errors in primary care, yet effective diagnostic decision support systems (DDSSs) remain lacking. Existing artificial intelligence (AI)-based DDSSs rely on gold-standard labels infeasible to obtain, have narrow diagnostic scope, and fail to reflect real-world uncertainty. We developed and validated a deep learning DDSS using imitation learning and collective clinician intelligence to generate diagnostic and order recommendations.

To demonstrate a replicable in-house framework for evaluating ambient digital scribe (ADS) systems and, applying it, to assess 3 commercial systems and examine alignment between clinician Likert ratings and a manual transcript-level error audit.