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Bakken, S. (2022). Meeting the information and communication needs of health disparate populations. Journal of the American Medical Informatics Association, 29(11), 1827–1828. https://doi.org/10.1093/jamia/ocac164

Mangal, S., Park, L., Reading Turchioe, M., Choi, J., Niño de Rivera, S., Myers, A., Goyal, P., Dugdale, L., & Masterson Creber, R. (2022). Building trust in research through information and intent transparency with health information: representative cross-sectional survey of 502 US adults. Journal of the American Medical Informatics Association, 29(9), 1535–1545. https://doi.org/10.1093/jamia/ocac084

Fang, Y., Idnay, B., Sun, Y., Liu, H., Chen, Z., Marder, K., Xu, H., Schnall, R., & Weng, C. (2022). Combining human and machine intelligence for clinical trial eligibility querying. Journal of the American Medical Informatics Association, 29(7), 1161–1171. https://doi.org/10.1093/jamia/ocac051

Suri, A., Askari, M., Calder, J., Branas, C., & Rundle, A. (2022). A real-time COVID-19 surveillance dashboard to support epidemic response in Connecticut: lessons from an academic-health department partnership. Journal of the American Medical Informatics Association, 29(5), 958–963. https://doi.org/10.1093/jamia/ocac025

Hobensack, M., Ojo, M., Barrón, Y., Bowles, K. H., Cato, K., Chae, S., Kennedy, E., McDonald, M. V., Rossetti, S. C., Song, J., Sridharan, S., & Topaz, M. (2022). Documentation of hospitalization risk factors in electronic health records (EHRs): a qualitative study with home healthcare clinicians. Journal of the American Medical Informatics Association, 29(5), 805–812. https://doi.org/10.1093/jamia/ocac023

Grauer, A., Kneifati-Hayek, J., Reuland, B., Applebaum, J. R., Adelman, J. S., Green, R. A., Lisak-Phillips, J., Liebovitz, D., Byrd, T. F., Kansal, P., Wilkes, C., Falck, S., Larson, C., Shilka, J., VanDril, E., Schiff, G. D., Galanter, W. L., & Lambert, B. L. (2021). Indication alerts to improve problem list documentation. Journal of the American Medical Informatics Association, 29(5), 909–917. https://doi.org/10.1093/jamia/ocab285

Landau, A. Y., Blanchard, A., Cato, K., Atkins, N., Salazar, S., Patton, D. U., & Topaz, M. (2022). Considerations for development of child abuse and neglect phenotype with implications for reduction of racial bias: a qualitative study. Journal of the American Medical Informatics Association, 29(3), 512–519. https://doi.org/10.1093/jamia/ocab275

Landau, A. Y., Ferrarello, S., Blanchard, A., Cato, K., Atkins, N., Salazar, S., Patton, D. U., & Topaz, M. (2022). Developing machine learning-based models to help identify child abuse and neglect: key ethical challenges and recommended solutions. Journal of the American Medical Informatics Association, 29(3), 576–580. https://doi.org/10.1093/jamia/ocab286

Pho, A. T., Bakken, S., Lunn, M. R., Lubensky, M. E., Flentje, A., Dastur, Z., & Obedin-Maliver, J. (2021). Online health information seeking, health literacy, and human papillomavirus vaccination among transgender and gender-diverse people. Journal of the American Medical Informatics Association, 29(2), 285–295. https://doi.org/10.1093/jamia/ocab150

Maurits, M. P., Korsunsky, I., Raychaudhuri, S., Murphy, S. N., Smoller, J. W., Weiss, S. T., Petukhova, L. M., Weng, C., Wei, W.-Q., Huizinga, T. W. J., Reinders, M. J. T., Karlson, E. W., van den Akker, E. B., & Knevel, R. (2022). A framework for employing longitudinally collected multicenter electronic health records to stratify heterogeneous patient populations on disease history. Journal of the American Medical Informatics Association, 29(5), 761–769. https://doi.org/10.1093/jamia/ocac008

Huang, Y., Liu, Y., Steel, P. A. D., Axsom, K. M., Lee, J. R., Tummalapalli, S. L., Wang, F., Pathak, J., Subramanian, L., & Zhang, Y. (2021). Deep significance clustering: a novel approach for identifying risk-stratified and predictive patient subgroups. Journal of the American Medical Informatics Association, 28(12), 2641–2653. https://doi.org/10.1093/jamia/ocab203