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To investigate subjective experiences and patterns of engagement with a novel electronic tool for facilitating reflection and problem solving for individuals with type 2 diabetes, Mobile Diabetes Detective (MoDD).

Prior studies of computing applications that support patients' medication knowledge and self-management offer valuable insights into effective application design, but do not address inpatient settings. This study is the first to explore the design and usefulness of patient-facing tools supporting inpatient medication management and tracking.

Electronic health records (EHRs) have great potential to improve quality of care. However, their use may diminish "patient-centeredness" in exam rooms by distracting the healthcare provider from focusing on direct patient interaction. The authors conducted a qualitative interview study to understand the magnitude of this issue, and the strategies that primary care providers devised to mitigate the unintended adverse effect associated with EHR use.

Electronic health records (EHRs) are increasingly used for clinical and translational research through the creation of phenotype algorithms. Currently, phenotype algorithms are most commonly represented as noncomputable descriptive documents and knowledge artifacts that detail the protocols for querying diagnoses, symptoms, procedures, medications, and/or text-driven medical concepts, and are primarily meant for human comprehension. We present desiderata for developing a computable phenotype representation model (PheRM).

Medication-indication information is a key part of the information needed for providing decision support for and promoting appropriate use of medications. However, this information is not readily available to end users, and a lot of the resources only contain this information in unstructured form (free text). A number of public knowledge bases (KBs) containing structured medication-indication information have been developed over the years, but a direct comparison of these resources [...]

The objective of this research is to empirically explore the drivers of patients' consent to sharing of their medical records on health information exchange (HIE) platforms.

Regular physical activity helps prevent heart disease, stroke, diabetes, and other chronic diseases, yet a broad range of conditions impair mobility at great personal and societal cost. Vast amounts of data characterizing human movement are available from research labs, clinics, and millions of smartphones and wearable sensors, but integration and analysis of this large quantity of mobility data are extremely challenging. The authors have established the Mobilize Center (http://mobilize.stanford.edu) to [...]