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COVID-19 quickly immobilized healthcare systems in the United States during the early stages of the outbreak. While much of the ensuing response focused on supporting the medical infrastructure, Columbia University College of Dental Medicine pursued a solution to triage and safely treat patients with dental emergencies amid the pandemic. Considering rapidly changing guidelines from governing bodies, dental infection control protocols, and our clinical faculty's expertise, we modeled, built, and implemented [...]

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 [...]

During the first 9 months of the coronavirus disease 2019 (COVID-19) pandemic, many emergency departments (EDs) experimented with telehealth applications to reduce virus exposure, decrease visit volume, and conserve personal protective equipment. We interviewed ED leaders who implemented telehealth programs to inform responses to the ongoing COVID-19 pandemic and future emergencies.

This exploratory study investigated the web accessibility of 54 official COVID-19 vaccine registration websites in the US and their concordance with the WCAG 2.0 and 2.1 guidelines. We employed AChecker, WAVE, and SortSite web accessibility evaluation tools to conduct automated analyses of these websites. The results showed suboptimal compliance with WCAG 2.0 and 2.1 guidelines, as determined using the AChecker, WAVE, and SortSite tools. These shortcomings in compliance may pose [...]

We propose a bidirectional GPS imputation method that can recover real-world mobility trajectories even when a substantial proportion of the data are missing. The time complexity of our online method is linear in the sample size, and it provides accurate estimates on daily or hourly summary statistics such as time spent at home and distance traveled.

To compare the accuracy of computer versus physician predictions of hospitalization and to explore the potential synergies of hybrid physician-computer models.

To assess primary care teams' perceptions of a health information exchange (HIE) event notification intervention for geriatric patients in 2 Veterans Health Administration (VHA) medical centers.

To compare Cox models, machine learning (ML), and ensemble models combining both approaches, for prediction of stroke risk in a prospective study of Chinese adults.

The United States is experiencing an opioid epidemic. In recent years, there were more than 10 million opioid misusers aged 12 years or older annually. Identifying patients at high risk of opioid use disorder (OUD) can help to make early clinical interventions to reduce the risk of OUD. Our goal is to develop and evaluate models to predict OUD for patients on opioid medications using electronic health records and deep [...]