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Use heuristic, deep learning (DL), and hybrid AI methods to predict semantic group (SG) assignments for new UMLS Metathesaurus atoms, with target accuracy ≥95%.

A scoping review identified interventions for optimizing hospital medication alerts post-implementation, and characterized the methods used, the populations studied, and any effects of optimization.

This work investigates if deep learning (DL) models can classify originating site locations directly from magnetic resonance imaging (MRI) scans with and without correction for intensity differences.

This study aims to summarize the research literature evaluating machine learning (ML)-based clinical decision support (CDS) systems in healthcare settings.

Generation of automated clinical notes has been posited as a strategy to mitigate physician burnout. In particular, an automated narrative summary of a patient's hospital stay could supplement the hospital course section of the discharge summary that inpatient physicians document in electronic health record (EHR) systems. In the current study, we developed and evaluated an automated method for summarizing the hospital course section using encoder-decoder sequence-to-sequence transformer models.

Patients who receive most care within a single healthcare system (colloquially called a "loyalty cohort" since they typically return to the same providers) have mostly complete data within that organization's electronic health record (EHR). Loyalty cohorts have low data missingness, which can unintentionally bias research results. Using proxies of routine care and healthcare utilization metrics, we compute a per-patient score that identifies a loyalty cohort.

Patient portals are increasingly used to recruit patients in research studies, but communication response rates remain low without tactics such as financial incentives or manual outreach. We evaluated a new method of study enrollment by embedding a study information sheet and HIPAA authorization form (HAF) into the patient portal preCheck-in (where patients report basic information like allergies).

The ubiquity of electronic health records (EHRs) has made incorporating EHRs into medical practice an essential component of resident's training. Patient encounters, an important element of practice, are impacted by EHRs through factors that include increasing documentation requirements. This research sheds light on the role of EHRs on resident clinical skills development with emphasis on their role in patient encounters.