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 The proliferation of electronic health record (EHR) alerts has led to widespread alert fatigue and clinician burnout, undermining the effectiveness of clinical decision support and compromising patient safety.

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.

Advances in health informatics rapidly expanded use of big-data analytics and electronic health records (EHR) by clinical researchers seeking to optimize interprofessional ICU team care. This study developed and validated a program for extracting interprofessional teams assigned to each patient each shift from EHR event logs.

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.

 The NIH Pragmatic Trials Collaboratory supports the design and conduct of 31 embedded pragmatic clinical trials, and many of these trials use patient-reported outcome measures (PROMs) to provide valuable information about the patients' health and wellness. Often these trials enroll medically underserved patients, including people with incomes below the federal poverty threshold, racial or ethnic minority groups, or rural or frontier communities.

 Healthcare provider burnout is a critical issue with significant implications for individual well-being, patient care, and healthcare system efficiency. Addressing burnout is essential for improving both provider well-being and the quality of patient care. Ambient artificial intelligence (AI) offers a novel approach to mitigating burnout by reducing the documentation burden through advanced speech recognition and natural language processing technologies that summarize the patient encounter into a clinical note to be [...]

 With an aging population preferring to age in place, there's a need for efficient health monitoring systems in eldercare. This study assesses the effectiveness of a linguistic summary alert system compared with a standard health alert system requiring data visualization interpretation by clinicians.

 This study aims to explore how health informaticists collaborate with multiple stakeholder groups, each possessing varying levels of comfort and competence with health technology and data. stakeholder engagement is highlighted as a crucial skill for health informaticists, necessitated by the differing competency levels among stakeholders.

 Patients with cirrhosis are at high risk for developing hepatocellular carcinoma (HCC), warranting receipt of semiannual surveillance imaging with the potential to identify abnormal liver lesions. Since the implementation of the 21st Century Cures Act's Information Blocking provision, test results are immediately released to patients through the patient portal. There is an increasing trend of patients reviewing their results before their providers.

 This study aimed to demonstrate real-world use of the Making Electronic Data More Available for Research and Public Health (MedMorph) Reference Architecture (RA) for automated exchange of hepatitis C-related data for public health surveillance and research using Fast Healthcare Interoperability Resources (FHIR).