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Exploring the contribution of health informatics is an emerging topic in relation to addressing climate change, but less examined is a body of literature reporting on the potential and effectiveness of women participating in climate action supported by digital health. This perspective explores how empowering women through digital health literacy (DHL) can support them to be active agents in addressing climate change risk and its impacts on health and well-being [...]

To explore the use of a shared communication and coordination platform-the CareVirtue journal feature-for care networks of people living with Alzheimer's disease and related dementias to inform the design of care network support technologies.

Establish a baseline of informatics professionals' perspectives on climate change and health.

The coronavirus disease 2019 (COVID-19) pandemic has caused millions of deaths around the world and revealed the need for data-driven models of pandemic spread. Accurate pandemic caseload forecasting allows informed policy decisions on the adoption of non-pharmaceutical interventions (NPIs) to reduce disease transmission. Using COVID-19 as an example, we present Pandemic conditional Ordinary Differential Equation (PAN-cODE), a deep learning method to forecast daily increases in pandemic infections and deaths. By [...]

To design and evaluate an interactive data quality (DQ) characterization tool focused on fitness-for-use completeness measures to support researchers' assessment of a dataset.

Integration of environmentally sustainable digital health interventions requires robust evaluation of their carbon emission life-cycle before implementation in healthcare. This scoping review surveys the evidence on available environmental assessment frameworks, methods, and tools to evaluate the carbon footprint of digital health interventions for environmentally sustainable healthcare.

Visual timelines of patient-reported outcomes (PRO) can help prostate cancer survivors manage longitudinal data, compare with population averages, and consider future trajectories. PRO visualizations are most effective when designed with deliberate consideration of users. Yet, graph literacy is often overlooked as a design constraint, particularly when users with limited graph literacy are not engaged in their development. We conducted user testing to assess comprehension, utility, and preference of longitudinal PRO [...]

This study aimed is to: (1) extend the Integrating the Biology and the Bedside (i2b2) data and application models to include medical imaging appropriate use criteria, enabling it to serve as a platform to monitor local impact of the Protecting Access to Medicare Act's (PAMA) imaging clinical decision support (CDS) requirements, and (2) validate the i2b2 extension using data from the Medicare Imaging Demonstration (MID) CDS implementation.

Prediction models can be useful tools for monitoring patient status and personalizing treatment in health care. The goal of this study was to compare the relative strengths and weaknesses of 2 different approaches for predicting functional recovery after knee arthroplasty: a neighbors-based "people-like-me" (PLM) approach and a linear mixed model (LMM) approach.