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The COVID-19 pandemic has once again highlighted the ubiquity and persistence of health inequities along with our inability to respond to them in a timely and effective manner. There is an opportunity to address the limitations of our current approaches through new models of informatics-enabled research and clinical practice that shift the norm from small- to large-scale patient engagement. We propose augmenting our approach to address health inequities through informatics-enabled [...]

This article reviews recent literature on the use of SNOMED CT as an extension of Lee et al's 2014 review on the same topic. The Lee et al's article covered literature published from 2001-2012, and the scope of this review was 2013-2020.

As master of science in health informatics (MSc HI) programs emerge in developing countries, quality assurance of these programs is essential. This article describes a comprehensive comparative analysis of competencies covered by accredited MSc HI programs in the East African common labor and educational zone.

Clinical notes present a wealth of information for applications in the clinical domain, but heterogeneity across clinical institutions and settings presents challenges for their processing. The clinical natural language processing field has made strides in overcoming domain heterogeneity, while pretrained deep learning models present opportunities to transfer knowledge from one task to another. Pretrained models have performed well when transferred to new tasks; however, it is not well understood if [...]

Multitask learning (MTL) using electronic health records allows concurrent prediction of multiple endpoints. MTL has shown promise in improving model performance and training efficiency; however, it often suffers from negative transfer - impaired learning if tasks are not appropriately selected. We introduce a sequential subnetwork routing (SeqSNR) architecture that uses soft parameter sharing to find related tasks and encourage cross-learning between them.

Cardiovascular disease (CVD) and its risk factors disproportionately affect people returning from incarceration. These individuals face multiple barriers to obtaining care, which can impact CVD and risk factor management and may be mitigated through use of a smartphone application (app). Therefore, we explored the CVD-related needs of people released from incarceration and which app features would support these needs.

The goals of this study are to describe the value and impact of Project HealthDesign (PHD), a program of the Robert Wood Johnson Foundation that applied design thinking to personal health records, and to explore the applicability of the PHD model to another challenging translational informatics problem: the integration of AI into the healthcare system.

In this perspective we want to highlight the rise of what we call "digital phenotyping" or inferring insights about peopleãs health and behavior from their digital devices and data, and the challenges this introduces. Indeed, the collection, processing, and storage of data comes with significant ethical, security and data governance considerations. The COVID-19 pandemic has laid bare the importance of scientific data and modeling, both to understand the nature and [...]

While the judicious use of antibiotics takes past microbiological culture results into consideration, this data's typical format in the electronic health record (EHR) may be unwieldy when incorporated into clinical decision-making. We hypothesize that a visual representation of sensitivities may aid in their comprehension.

We evaluated the usability of mobile COVID-19 contact tracing apps, especially for individuals with barriers to communication and limited digital literacy skills. We searched the Apple App Store, Google Play, peer-reviewed literature, and lay press to find contact tracing apps in the United States. We evaluated apps with a framework focused on user characteristics and user interface. Of the final 26 apps, 77% were on both iPhone and Android. 69% [...]