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Developing equitable, sustainable informatics solutions is key to scalability and long-term success for projects in the global health informatics (GHI) domain. This paper presents key strategies for incorporating principles of health equity in the GHI project lifecycle.

Digital health equity, the opportunity for all to engage with digital health tools to support good health outcomes, is an emerging priority across the world. The field of digital health equity would benefit from a comprehensive and systematic understanding of digital health, digital equity, and health equity, with a focus on real-world applications. We conducted a scoping review to identify and describe published frameworks and concepts relevant to digital health [...]

Modernizing and strengthening the US public health data and information infrastructure requires a strong public health informatics (PHI) workforce. The study objectives were to characterize existing PHI specialists and assess informatics-related training needs.

Objective Experiences sharing complex workflow-integrated clinical decision support (CDS) across health systems are sparse and not well reported. This case study presents the sharing of a hybrid electronic health record (EHR)-native and SMART-compatible CDS tool for automating provision of smoking cessation treatment for caregivers during pediatric visits. Materials & Methods We conducted a comprehensive needs assessment using socio-technical frameworks to identify workflow gaps and technical requirements. A multidisciplinary team of [...]

To assess the prevalence of recommended design elements in implemented electronic health record (EHR) interruptive alerts across pediatric care settings.

Digital health research involves collecting vast amounts of personal health data, making data management practices complex and challenging to convey during informed consent.

We aimed to demonstrate the importance of establishing best practices in large language model research, using repeat prompting as an illustrative example.

To detect and classify features of stigmatizing and biased language in intensive care electronic health records (EHRs) using natural language processing techniques.

We evaluate the effectiveness of large language models (LLMs), specifically GPT-based (GPT-3.5 and GPT-4) and Llama-2 models (13B and 7B architectures), in autonomously assessing clinical records (CRs) to enhance medical education and diagnostic skills.

To quantify utilization and impact on documentation time of a large language model-powered ambient artificial intelligence (AI) scribe.