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 Effective data management is critical in medical informatics research, ensuring the success and integrity of clinical studies. However, students often lack comprehensive training in this area, highlighting the need for tailored educational programs.

The degree to which deployed artificial intelligence-based deterioration prediction algorithms (AI-DPA) differ in their development, the reasons for these differences, and how this may impact their performance remains unclear. Our primary objective was to identify design factors and associated decisions related to the development of AI-DPA and highlight deficits that require further research.

 The "cancer risk calculator" mobile application aims to inform patients about their personal risks of cancer and the risk factors influencing said risks. The present analysis examines the responses to a questionnaire submitted by oncology patients treated with radiotherapy or their family members.

To identify demographic, social, and clinical factors associated with HIV self-management and evaluate whether the CHAMPS intervention is associated with changes in an individual's HIV self-management.

 Engagement of clinicians who understand clinical workflows and technology constraints can accelerate the development and implementation of better electronic health record (EHR) designs that improve quality and reduce burnout. Provider builder programs can accelerate clinical informatics education for a broader coalition of clinical specialties.

 Reliable, precise, timely, and clear documentation of diagnoses is difficult. Poor specificity or the absence of diagnostic documentation can lead to decreased revenue and increased payor denials, audits, and queries to providers. Nuance's Dragon Medical Advisor (DMA) is a computer-assisted physician documentation (CAPD) product. Natural language processing is used to present real-time advice on diagnostic specificity during documentation.

Timely access to data is needed to improve care for substance-exposed birthing persons and their infants, a significant public health problem in the United States. We examined the current state of birthing person and infant/child (dyad) data-sharing capabilities supported by health information exchange (HIE) standards and HIE network capabilities for data exchange to inform point-of-care needs assessment for the substance-exposed dyad.

Early detection of surgical complications allows for timely therapy and proactive risk mitigation. Machine learning (ML) can be leveraged to identify and predict patient risks for postoperative complications. We developed and validated the effectiveness of predicting postoperative complications using a novel surgical Variational Autoencoder (surgVAE) that uncovers intrinsic patterns via cross-task and cross-cohort presentation learning.

Machine learning and deep learning are powerful tools for analyzing electronic health records (EHRs) in healthcare research. Although family health history has been recognized as a major predictor for a wide spectrum of diseases, research has so far adopted a limited view of family relations, essentially treating patients as independent samples in the analysis.

This article describes the implementation of preemptive clinical pharmacogenomics (PGx) testing linked to an automated clinical decision support (CDS) system delivering actionable PGx information to clinicians at the point of care at UCSF Health, a large Academic Medical Center.