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We introduce a widely applicable model-based approach for estimating individual-level Social Determinants of Health (SDoH) and evaluate its effectiveness using the All of Us Research Program.

The goal of this case report is to detail experiences and challenges experienced in the training of Primary Care residents in secondary analysis using All of Us Researcher Workbench. At our large, urban safety net hospital, Primary Care/Internal Medicine residents in their third year undergo a research intensive block, the Research Practicum, where they work as a team to conduct secondary data analysis on a dataset with faculty facilitation. In [...]

This case study describes how an All of Us engagement project returned value to community by strengthening high school students' capacity to serve as health advocates.

Summaries of health research can be a complementary way to return value to participants. We assess how research participants engage with summaries via email communication and how this can be improved.

The National Institutes of Health's All of Us Research Program addresses gaps in biomedical research by collecting health data from diverse populations. Pregnant individuals have historically been underrepresented in biomedical research, and pregnancy-related research is often limited by data availability, sample size, and inadequate representation of the diversity of pregnant people. All of Us integrates a wealth of health-related data, providing a unique opportunity to conduct comprehensive pregnancy-related research. We [...]

With its size and diversity, the All of Us Research Program has the potential to power and improve representation in clinical trials through ancillary studies like Nutrition for Precision Health. We sought to characterize high-level trial opportunities for the diverse participants and sponsors of future trial investment.

We propose and validate a domain knowledge-driven classification model for diagnosing post-acute sequelae of SARS-CoV-2 infection (PASC), also known as Long COVID, using Electronic Health Records (EHRs) data.

This study applies predictive analytics to identify patients at risk of missing appointments at a novel post-discharge clinic (PDC) in a large academic health system. Recognizing the critical role of appointment adherence in the success of new clinical ventures, this research aims to inform future targeted interventions to increase appointment adherence.

To assess the current state of sex, sexual orientation, and gender identity (SSOGI) data collection options in US electronic health record (EHR) platforms.

Digital interventions are increasingly in demand to address mental health concerns, with significant potential to reach populations that disproportionately face barriers to accessing mental health care. Challenges with user engagement, however, persist. The goal of this study was to develop user personas to inform the development of a digital mental health intervention (DMHI) for a perinatal population.