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Understanding how to identify the social determinants of health from electronic health records (EHRs) could provide important insights to understand health or disease outcomes. We developed a methodology to capture 2 rare and severe social determinants of health, homelessness and adverse childhood experiences (ACEs), from a large EHR repository.

The International Classification of Functioning, Disability and Health (ICF) is the World Health Organization's standard for describing health and health-related states. Examples of how the ICF has been used in Electronic Health Records (EHRs) have not been systematically summarized and described yet.

To introduce blockchain technologies, including their benefits, pitfalls, and the latest applications, to the biomedical and health care domains.

Clinical decision support tools for risk prediction are readily available, but typically require workflow interruptions and manual data entry so are rarely used. Due to new data interoperability standards for electronic health records (EHRs), other options are available. As a clinical case study, we sought to build a scalable, web-based system that would automate calculation of kidney failure risk and display clinical decision support to users in primary care practices.

Identifying all published reports of randomized controlled trials (RCTs) is an important aim, but it requires extensive manual effort to separate RCTs from non-RCTs, even using current machine learning (ML) approaches. We aimed to make this process more efficient via a hybrid approach using both crowdsourcing and ML.

To systematically review the literature pertaining to asynchronous automated electronic notifications of laboratory results to clinicians.

Given the strong push to empower patients and make them partners in their health care, we evaluated the current capability of hospitals to offer health information technology that facilitates patient engagement (PE).

To introduce a disease prognosis framework enabled by a robust classification scheme derived from patient-specific transcriptomic response to stimulation.