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Our scoping review aims to assess what legal, ethical, and socio-technical factors contribute to or inhibit the success of national eHealth system implementations. In addition, our review seeks to describe the characteristics and benefits of eHealth systems.

To summarize how artificial intelligence (AI) is being applied in COVID-19 research and determine whether these AI applications integrated heterogenous data from different sources for modeling.

In electronic health record data, the exact time stamp of major health events, defined by significant physiologic or treatment changes, is often missing. We developed and externally validated a method that can accurately estimate these time stamps based on accurate time stamps of related data elements.

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.