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The American Medical Informatics Association (AMIA) Task Force on Diversity, Equity, and Inclusion (DEI) was established to address systemic racism and health disparities in biomedical and health informatics, aligning with AMIA's mission to transform healthcare. AMIA's DEI initiatives were spurred by member voices responding to police brutality and COVID-19's impact on Black/African American communities.

 This study aimed to describe the current landscape of electronic health record (EHR) training and optimization programs (ETOPs) and their impact on health care workers' (HCWs) experience with the EHR.

 Health professions trainees (trainees) are unique as they learn a chosen field while working within electronic health records (EHRs). Efforts to mitigate EHR burden have been described for the experienced health professional (HP), but less is understood for trainees. EHR or documentation burden (EHR burden) affects trainees, although not all trainees use EHRs, and use may differ for experienced HPs.

 There is a need to integrate informatics education into medical training programs given the rise in demand for health informaticians and the call on the Accreditation Council for Graduate Medical Education and the body of undergraduate medical education for implementation of informatics curricula.

 Ensuring the security of nursing information holds substantial importance to nursing outcomes and healthcare system management. The awareness of information security among nurses in China is generally inadequate, and there is a lack of standardized evaluation tools for nurse information security in nursing practice. The nursing sector necessitates the establishment of a robust culture surrounding information security.

Mobile health (mHealth) regimens can improve health through the continuous monitoring of biometric parameters paired with appropriate interventions. However, adherence to monitoring tends to decay over time. Our randomized controlled trial sought to determine: (1) if a mobile app with gamification and financial incentives significantly increases adherence to mHealth monitoring in a population of heart failure patients; and (2) if activity data correlate with disease-specific symptoms.

We describe the development and implementation of a system for monitoring patient-reported adverse events and quality of life using electronic Patient Reported Outcome (ePRO) instruments in the I-SPY2 Trial, a phase II clinical trial for locally advanced breast cancer. We describe the administration of technological, workflow, and behavior change interventions and their associated impact on questionnaire completion.

Acute hepatic porphyria (AHP) is a group of rare but treatable conditions associated with diagnostic delays of 15 years on average. The advent of electronic health records (EHR) data and machine learning (ML) may improve the timely recognition of rare diseases like AHP. However, prediction models can be difficult to train given the limited case numbers, unstructured EHR data, and selection biases intrinsic to healthcare delivery. We sought to train [...]

 Due to fear of stigma, patients want more control over the sharing of sensitive medical records. The Substance Abuse and Mental Health Administration (SAMHSA) and the Office of the National Coordinator (ONC) supported the development of standards-compliant, consent-respecting medical record exchange technology using metadata labeling (e.g., substance use information). Existing technologies must be updated with newer standards and support more than binary-sensitive categorizations to better align with how physicians categorize [...]

 Data exploration in modern electronic health records (EHRs) is often aided by user-friendly graphical interfaces providing "self-service" tools for end users to extract data for quality improvement, patient safety, and research without prerequisite training in database querying. Other resources within the same institution, such as Honest Brokers, may extract data sourced from the same EHR but obtain different results leading to questions of data completeness and correctness.