Novel EHR-Embedded Risk Communication Tool for Prediabetes — UCLA Trial Tests Machine Learning Approach to Patient Understanding
247 Health News Editorial
·14 May 2026
·4 min read
·clinical-trialsUCLA researchers are testing whether machine learning-enhanced laboratory result communication can improve patient understanding and reduce unnecessary healthcare use in older adults with prediabetes. The 1,200-participant trial examines EHR-embedded risk stratification tools.
Originally reported by ClinicalTrials.gov
References
- 1.Australian Institute of Health and Welfare. (2023). Diabetes in Australia. AIHW. https://www.aihw.gov.au/reports/diabetes/diabetes-in-australia
- 2.Cavanaugh, K., Huizinga, M. M., Wallston, K. A., Gebretsadik, T., Shintani, A., Davis, D., ... & Rothman, R. L. (2008). Association of numeracy and diabetes control and self-care. Journal of General Internal Medicine, 23(10), 1683-1688. https://doi.org/10.1007/s11606-008-0875-7
- 3.Centers for Disease Control and Prevention. (2024). National Diabetes Statistics Report. CDC. https://www.cdc.gov/diabetes/data/statistics-report/index.html
- 4.Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358. https://doi.org/10.1056/NEJMra1814259
- 5.University of California, Los Angeles. (2024). Evaluation of Patient and Provider Facing EHR-embedded Risk Stratification Tools. ClinicalTrials.gov. NCT06995378. https://clinicaltrials.gov/study/NCT06995378
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247 Health News Editorial
247 Health News is a medical intelligence platform delivering evidence-based research appraisals, clinical trial monitoring, and health technology news for healthcare professionals.
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