Search AMIA.org

Large language models are rapidly transforming medical education, yet their performance in Allergy/Immunology remains insufficiently characterized. Furthermore, concerns regarding accuracy, consistency, and sensitivity to input format persist.

Evaluation of ambient AI on patient experience, documentation efficiency, clinician workload, and clinical throughput across a large emergency department (ED) network.

Ambient clinical documentation tools are increasingly used to reduce administrative burden and improve provider experience. However, evidence describing their effects at scale across large, multi-site health systems remains limited.

BACKGROUND: Pharmacogenetics is increasingly recognized as essential for optimizing drug therapy and reducing preventable adverse drug events. However, applying pharmacogenetic data in real-world clinical decisions remains challenging. Traditional rule-based clinical decision support systems often fall short. Machine learning-based clinical decision support systems offer a more dynamic solution, combining pharmacogenetic variants, patient-specific clinical data, medication history, and guidelines to generate personalized treatment recommendations.

BACKGROUND: Excessive alerts, difficult-to-use interfaces, and inefficient workflows increase cognitive load and time in the electronic health record (EHR), contributing to clinician burnout. Ordering workflows are a high-friction component of the EHR experience. Order friction (OF) is a vendor-derived metric quantifying ordering burden as a count of required manipulations and interruptions.

BACKGROUND: Noona is a patient-facing app used by CancerCare Manitoba to connect patients to their electronic health record and communicate with their care team. Since the implementation of Noona, staff have been responsible for registering patients with Noona. Registration rates have been consistently low. With competing clinic priorities and workload demands on front-line staff, an alternative solution was needed.

BACKGROUND: Ambient artificial intelligence (AI) scribes may improve clinician-level outcomes (e.g., documentation burden) and patient experience. However, patients must agree to allow clinicians to use ambient AI scribes for these benefits to materialize. What motivates patients to agree to these tools' use and what factors improve their acceptability remain underexplored. It is also unclear whether patients' current trust in AI tools influences which factors affect ambient AI scribe acceptability.

BACKGROUND: The rapid development of information and communication technologies has rendered informatics competencies of health care professionals critical in the digital transformation process. Nurses, who constitute a substantial proportion of the health care workforce, are expected to develop nursing informatics competencies. These competencies are crucial for effective adaptation to digital health care environments and for ensuring the delivery of safe care. Therefore, it is important that informatics competencies are assessed [...]

MediLinker is a blockchain-based application for identity verification and personal health information management. To prepare for its clinical deployment, the perceptions of health consumers toward MediLinker should be examined.

Health-related social needs (HRSN) significantly influence health outcomes, yet healthcare organizations face persistent challenges tracking referrals to community-based organizations and interpreting referral outcomes across fragmented clinical and social care systems. Prior studies report low referral fulfillment rates, but much of this evidence derives from single organizations, manual data collection, or incentivized documentation workflows, limiting insight into how information infrastructure shapes what is observable at scale.