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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.

To conduct a nationwide survey among professionals working in oncology departments in China to investigate their attitudes, perceptions, and experiences regarding medical artificial intelligence (AI), and to explore and compare the factors influencing AI behavioral intention (BI; willingness to adopt AI) between physicians and nurses using the Unified Theory of Acceptance and Use of Technology (UTAUT).

We adapted the individualized polysocial risk score (iPsRS), a machine learning model originally developed for patients with type 2 diabetes, to evaluate its generalizability in predicting 1-year hospitalization risk in a disease-agnostic adult cohort, with attention to fairness and explainability.

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 [...]

Patients with substance misuse are at high risk for clinical deterioration, and pre-hospital encounters constitute important risk factors. We sought to incorporate these risk factors into a novel prediction model using linked electronic health record, emergency medical services (EMSs), and claims data.

To evaluate natural language processing (NLP) and machine learning (ML) approaches for identifying social needs in electronic health record (EHR) notes, using balanced versus real-world imbalanced datasets.

To examine how algorithmic fairness is measured, operationalized, and reported in machine learning (ML) models designed to predict or support secondary prevention of cardiovascular disease (CVD) outcomes including progression, recurrence, readmission, and post-index mortality in racialized populations.