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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.

To develop and evaluate RobotReviewer, a machine learning (ML) system that automatically assesses bias in clinical trials. From a (PDF-formatted) trial report, the system should determine risks of bias for the domains defined by the Cochrane Risk of Bias (RoB) tool, and extract supporting text for these judgments.

Immunization registries or information systems are critical to improving the quality and evaluating the ongoing success of immunization programs. However, the completeness of these systems is challenged by a myriad of factors including the fragmentation of vaccine administration, increasing mobility of individuals, new vaccine development, use of multiple products, and increasingly frequent changes in recommendations. Mobile technologies could offer a solution, which mitigates some of these challenges. Engaging individuals to [...]

To assess the effect of tablet computers with a mobile patient portal application on hospitalized patients' knowledge and activation.

The aim of this systematic review was to synthesize current knowledge of the factors influencing healthcare professional adoption of mobile health (m-health) applications.

To share approaches and innovations adopted to deliver a relatively inexpensive clinical data management (CDM) framework within a low-income setting that aims to deliver quality pediatric data useful for supporting research, strengthening the information culture and informing improvement efforts in local clinical practice.

Morbidity and mortality due to preeclampsia in settings with limited resources often results from delayed diagnosis. The Congo Red Dot (CRD) test, a simple modality to assess the presence of misfolded proteins in urine, shows promise as a diagnostic and prognostic tool for preeclampsia. We propose an innovative mobile health (mHealth) solution that enables the quantification of the CRD test as a batch laboratory test, with minimal cost and equipment.