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Identify barriers impacting the time consuming and error fraught process of medication reconciliation. Design and implement an electronic medication management system where patient and trusted healthcare proxies can participate in establishing and maintaining an inclusive and up-to-date list of medications.

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

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.

To develop a conceptual prediction model framework containing standardized steps and describe the corresponding open-source software developed to consistently implement the framework across computational environments and observational healthcare databases to enable model sharing and reproducibility.

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.

While depression and anxiety are common mental health issues, only a small segment of the population has access to standard one-on-one treatment. The use of smartphone apps can fill this gap. An app recommender system may help improve user engagement of these apps and eventually symptoms.

Microbiology laboratory results are complex and cumbersome to review. We sought to develop a new review tool to improve the ease and accuracy of microbiology results review.

As available data increases, so does the opportunity to develop risk scores on more refined patient populations. In this paper we assessed the ability to derive a risk score for a patient no-showing to a clinic visit.

Health informatics interventions are designed to help people avoid, recover from, or cope with disease and disability, or to improve the quality and safety of healthcare. Unfortunately, they pose a risk of producing intervention-generated inequalities (IGI) by disproportionately benefiting more advantaged people. In this perspective paper, we discuss characteristics of health-related interventions known to produce IGI, explain why health informatics interventions are particularly vulnerable to this phenomenon, and describe safeguards [...]