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BACKGROUND: Electronic health record (EHR) clinical decision support (CDS) systems generate excessive, non-contextual alerts, causing clinician desensitization with override rates exceeding 90%. This alert fatigue disrupts workflows, contributes to burnout, and creates patient safety risks. Traditional survey-based methods fail to capture real-time end-user feedback at scale.

BACKGROUND: The COVID-19 pandemic accelerated the adoption of digital health technologies, including telehealth, electronic medical records, and patient-facing applications. These tools maintained access to care during periods of restriction but exposed gaps in workforce readiness, patient trust, digital literacy, and equity. Despite widespread implementation, limited evidence exists on how patients and health care providers (HCPs) experienced this transition and how these experiences influenced digital health adoption.

[This corrects the article DOI: 10.1093/jamiaopen/ooag100.].

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: Large language models (LLMs) 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.

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.

[This corrects the article DOI: 10.1093/jamiaopen/ooaf087.].

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.