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Widespread health information exchange (HIE) is a national objective motivated by the promise of improved care and a reduction in costs. Previous reviews have found little rigorous evidence that HIE positively affects these anticipated benefits. However, early studies of HIE were methodologically limited. The purpose of the current study is to review the recent literature on the impact of HIE.

To examine the effects of graphical formats and age on consumers' comprehension and perceptions of the use of self-monitoring test results.

Although family and friends (FF) often play a significant support role in the health of older adults (OA), we know little about their role in personal health information management (PHIM). To address this gap and inform the design of PHIM tools, we describe the work, needs, and barriers of FF in the context of PHIM for OAs.

We studied the impact of online social support on patient self-care behavior in an online health community for human immunodeficiency virus (HIV) patients. We conceptualized emotional and informational support provided by community members into nuanced sub-dimensions. We explored how the direct and interaction effects of these sub-dimensions impact the self-care behavior of a support seeker.

To understand whether user reviews of Instant Blood Pressure (IBP), an inaccurate, unregulated BP-measuring app reflected IBP's inaccuracy, to understand drivers for high and low ratings, and to understand if disclaimers prevented medical use.

This study reports the development and psychometric evaluation of the Smartphone for Clinical Work Scale (SCWS) to measure nurses' use of smartphones for work purposes.

Review the existing studies including an assessment tool/method to assess the quality of mHealth apps; extract their criteria; and provide a classification of the collected criteria.

Existing screening tools for early detection of autism are expensive, cumbersome, time- intensive, and sometimes fall short in predictive value. In this work, we sought to apply Machine Learning (ML) to gold standard clinical data obtained across thousands of children at-risk for autism spectrum disorder to create a low-cost, quick, and easy to apply autism screening tool.