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 This study aimed to (1) empirically investigate current practices and analyze ethical dimensions of clinical data sharing by health care organizations for uses other than treatment, payment, and operations; and (2) make recommendations to inform research and policy for health care organizations to protect patients' privacy and autonomy when sharing data with unrelated third parties.

 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.

 As teleconsultations continue to rise in popularity due to their convenience and accessibility, it is crucial to identify and address the challenges they present in order to improve the patient experience, enhance outcomes, and ensure the quality of care. To identify communication challenges that clinicians and patients experience during teleconsultation, a scoping review was conducted.

 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.

 Interoperability between electronic health records (EHR) and immunization information systems (IIS) may positively influence data quality, affecting timeliness, completeness, and accuracy of these data. However, the extent to which EHR/IIS interoperability may influence the day-to-day vaccination workflow and related recordkeeping tasks performed at medical practices is unclear.

There is rapidly growing interest in learning health systems (LHSs) nationally and globally. While the critical role of informatics is recognized, the informatics community has been relatively slow to formalize LHS as a priority area.

This study aims to improve the ethical use of machine learning (ML)-based clinical prediction models (CPMs) in shared decision-making for patients with kidney failure on dialysis. We explore factors that inform acceptability, interpretability, and implementation of ML-based CPMs among multiple constituent groups.

Cancer diagnosis comes as a shock to many patients, and many of them feel unprepared to handle the complexity of the life-changing event, understand technicalities of the diagnostic reports, and fully engage with the clinical team regarding the personalized clinical decision-making.

This study aims to (1) review machine learning (ML)-based models for early infection diagnostic and prognosis prediction in post-acute care (PAC) settings, (2) identify key risk predictors influencing infection-related outcomes, and (3) examine the quality and limitations of these models.