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User-generated content (UGC) in online environments provides opportunities to learn an individual's health status outside of clinical settings. However, the nature of UGC brings challenges in both data collecting and processing. The purpose of this study is to systematically review the effectiveness of applying machine learning (ML) methodologies to UGC for personal health investigations.

We describe the development of a nursing home information technology (IT) maturity model designed to capture stages of IT maturity.

The study sought to develop a clinical decision support system (CDSS) for the treatment of thyroid nodules, using a mind map and iterative decision tree (IDT) approach to the integration of clinical practice guidelines (CPGs).

We established a Patient Safety Learning Laboratory comprising 2 core and 3 individual project teams to introduce a suite of digital health tools integrated with our electronic health record to identify, assess, and mitigate threats to patient safety in real time. One of the core teams employed systems engineering (SE) and human factors (HF) methods to analyze problems, design and develop improvements to intervention components, support implementation, and evaluate the [...]

We seek to quantify the mortality risk associated with mentions of medical concepts in textual electronic health records (EHRs). Recognizing mentions of named entities of relevant types (eg, conditions, symptoms, laboratory tests or behaviors) in text is a well-researched task. However, determining the level of risk associated with them is partly dependent on the textual context in which they appear, which may describe severity, temporal aspects, quantity, etc.

The study sought to understand the impact of a phishing training program on phishing click rates for employees at a single, anonymous US healthcare institution.

We appreciate the detailed review provided by Magge et al1 of our article, "Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts." 2 In their letter, they present a subjective criticism that rests on concerns about our dataset composition and potential misinterpretation of comparisons to existing methods. Our article underwent two rounds of extensive peer review and has been cited 28 times1 in [...]

The study sought to identify collaborative electronic health record (EHR) usage patterns for pediatric trauma patients and determine how the usage patterns are related to patient outcomes.