Correction to: Association between state payment parity policies and telehealth usage at community health centers during COVID-19.
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DOI: 10.1093/jamia/ocac174
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DOI: 10.1093/jamia/ocac174
The lack of diversity, equity, and inclusion continues to hamper the artificial intelligence (AI) field and is especially problematic for healthcare applications. In this article, we expand on the need for diversity, equity, and inclusion, specifically focusing on the composition of AI teams. We call to action leaders at all levels to make team inclusivity and diversity the centerpieces of AI development, not the afterthought. These recommendations take into consideration [...]
Author(s): de Hond, Anne A H, van Buchem, Marieke M, Hernandez-Boussard, Tina
DOI: 10.1093/jamia/ocac156
Climate change, human health, and healthcare systems are inextricably linked. As the climate warms due to greenhouse gas (GHG) emissions, extreme weather events, such as floods, fires, and heatwaves, will drive up demand for healthcare. Delivering healthcare also contributes to climate change, accounting for ∼5% of the global carbon emissions. To rein in healthcare's carbon footprint, clinicians and health policy makers must be able to measure the GHG contributions of [...]
Author(s): Smith, Carolynn L, Zurynski, Yvonne, Braithwaite, Jeffrey
DOI: 10.1093/jamia/ocac113
Digital exposure notifications (DEN) systems were an emergency response to the coronavirus disease 2019 (COVID-19) pandemic, harnessing smartphone-based technology to enhance conventional pandemic response strategies such as contact tracing. We identify and describe performance measurement constructs relevant to the implementation of DEN tools: (1) reach (number of users enrolled in the intervention); (2) engagement (utilization of the intervention); and (3) effectiveness in preventing transmissions of COVID-19 (impact of the intervention) [...]
Author(s): Segal, Courtney D, Lober, William B, Revere, Debra, Lorigan, Daniel, Karras, Bryant T, Baseman, Janet G
DOI: 10.1093/jamia/ocac178
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.
Author(s): Cho, Sylvia, Ensari, Ipek, Elhadad, Noémie, Weng, Chunhua, Radin, Jennifer M, Bent, Brinnae, Desai, Pooja, Natarajan, Karthik
DOI: 10.1093/jamia/ocac166
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 [...]
Author(s): Abdolkhani, Robab, Choo, Dawn, Gilbert, Cecily, Borda, Ann
DOI: 10.1093/jamia/ocac167
Meditation with mobile apps has been shown to improve mental and physical health. However, regular, long-term meditation app use is needed to maintain these health benefits, and many people have a difficult time maintaining engagement with meditation apps over time. Our goal was to determine the length of the timeframe over which usage data must be collected before future app abandonment can be predicted accurately in order to better target [...]
Author(s): Fowers, Rylan, Berardi, Vincent, Huberty, Jennifer, Stecher, Chad
DOI: 10.1093/jamia/ocac169
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DOI: 10.1093/jamia/ocac171
Natural hazards (NHs) associated with climate change have been increasing in frequency and intensity. These acute events impact humans both directly and through their effects on social and environmental determinants of health. Rather than relying on a fully reactive incident response disposition, it is crucial to ramp up preparedness initiatives for worsening case scenarios. In this perspective, we review the landscape of NH effects for human health and explore the [...]
Author(s): Phuong, Jimmy, Riches, Naomi O, Calzoni, Luca, Datta, Gora, Duran, Deborah, Lin, Asiyah Yu, Singh, Ramesh P, Solomonides, Anthony E, Whysel, Noreen Y, Kavuluru, Ramakanth
DOI: 10.1093/jamia/ocac162
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 [...]
Author(s): Shi, Ruian, Zhang, Haoran, Morris, Quaid
DOI: 10.1093/jamia/ocac160