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Opportunities and Challenges in Using Electronic Health Record Systems to Study Postacute Sequelae of SARS-CoV-2 Infection: Insights From the NIH RECOVER Initiative

These studies have been developing and validating algorithms to identify patients with long COVID within EHR networks for clinical and epidemiological characterization, risk factor prediction, and identification of prevention and treatment opportunities.

Hannah L Mandel, Shruti N Shah, L Charles Bailey, Thomas Carton, Yu Chen, Shari Esquenazi-Karonika, Melissa Haendel, Mady Hornig, Rainu Kaushal, Carlos R Oliveira, Alice A Perlowski, Emily Pfaff, Suchitra Rao, Hanieh Razzaghi, Elle Seibert, Gelise L Thomas, Mark G Weiner, Lorna E Thorpe, Jasmin Divers, RECOVER EHR Cohort

J Med Internet Res 2025;27:e59217

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Estimating the Prevalence of Schizophrenia in the General Population of Japan Using an Artificial Neural Network–Based Schizophrenia Classifier: Web-Based Cross-Sectional Survey

These findings are expected to support the versatility of pretrained models in epidemiological research.

Pichsinee Choomung, Yupeng He, Masaaki Matsunaga, Kenji Sakuma, Taro Kishi, Yuanying Li, Shinichi Tanihara, Nakao Iwata, Atsuhiko Ota

JMIR Form Res 2025;9:e66330

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A Digital Tool for Clinical Evidence–Driven Guideline Development by Studying Properties of Trial Eligible and Ineligible Populations: Development and Usability Study

Such judgments have to be informed by an epidemiological understanding of the clinical population and how it compares to the population studied in RCTs. However, guideline developers are constrained by the lack of tailored epidemiological data analysis designed for this purpose due to data governance, consistency of disease definitions, and reduplicated effort in analysis code.

Shahzad Mumtaz, Megan McMinn, Christian Cole, Chuang Gao, Christopher Hall, Magalie Guignard-Duff, Huayi Huang, David A McAllister, Daniel R Morales, Emily Jefferson, Bruce Guthrie

J Med Internet Res 2025;27:e52385

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SARS-CoV-2 Infection Risk by Vaccine Doses and Prior Infections Over 24 Months: ProHEpiC-19 Longitudinal Study

To apply these survival models, it is necessary to have a well-characterized cohort of individuals, such as the Professionals’ Health in Epidemiological Crises COVID-19 (Pro HEpi C-19) cohort. This cohort provides a well-characterized population of health care workers (HCWs) to investigate the multiple determinants of risk of new infection [20].

Pere Torán-Monserrat, Noemí Lamonja-Vicente, Anna Costa-Garrido, Lucía A Carrasco-Ribelles, Bibiana Quirant, Marc Boigues, Xaviera Molina, Carla Chacón, Rosalia Dacosta-Aguayo, Fernando Arméstar, Eva María Martínez Cáceres, Julia G Prado, Concepción Violán, ProHEpiC-19 study group