Jordan Overcash, RN, MSN
Jordan is a key clinical expert for Veradigm Life Sciences, spear-heading our natural language processing capabilities and providing crucial clinical input and oversight into many of the Real World Evidence studies.
AREA OF EXPERTISE
Real World Data / Evidence Medical/Clinical Informatics Natural Language ProcessingJordan Overcash, Director, Solutions Management, is a registered nurse with a master’s degree in nursing informatics. She manages a large team of clinical informaticists and data scientists that play an integral part in Veradigm’s product development and natural language processing (NLP) capabilities. She currently acts as a core clinical resource for many of the Veradigm products, participating in tasks such as therapeutic area code review/lookup, guideline reviews and updates, clinical workflow assessments, and reporting.
Prior to joining Veradigm, she practiced as a surgical nurse until completing graduate school. She transitioned to working on physician and nursing content development and reporting within an EHR application for ambulatory, emergency department, and inpatient workflows.
Ms. Overcash obtained her registered nursing degree from the University of North Carolina at Chapel Hill, where she later completed her Master's Degree in Nursing Administration, with a concentration in Nursing Informatics.
Outside of work, Jordan enjoys coaching her two boys in sports and spending quality time with her family.
References to published articles by this expert
- Sudaria T, Overcash J, Nguyen N, Oguntuga A. RWD112 Categorizing telemedicine visits using natural language processing and machine learning. In: Value Health. Vol 25. Elsevier; 2022:S597. doi:10.1016/j.jval.2022.04.1636
- Overcash J, Nguyen N. RWD72 SARS-COV-2 vaccine breakthrough infection rates based data from 3 EHRS. In: Value Health. Vol 25. Elsevier; 2022:S589. doi:10.1016/j.jval.2022.04.1596
- Nguyen N, Connolly T, Overcash J, Hubbard A, Sudaria T. RWD103 Evaluating a privacy preserving record linkage (PPRL) solution to link de-identified patient records in RWD using default matching methods and machine learning methods. In: Value Health. Vol 25. Elsevier; 2022:S595. doi:10.1016/j.jval.2022.04.1627
- Morgan P, Overcash J, Sudaria T, Nguyen N. RWD52 Comparing techniques for mining HbA1c data in ambulatory EHRs for use in RWE research. In: Value Health. Vol 25. Elsevier; 2022:S585. doi:10.1016/j.jval.2022.04.1576
- Oguntuga A, Overcash J, Tyagi G, Nguyen NT. Feature importance of a diabetes risk classification model. In: AMIA. 2020. https://amia.org/education-events/amia-2020-virtual-annual-symposium
- Sadiq G, Oguntuga A, Sudaria T, Overcash J, Hubbard A, Nguyen N. PCV81 Mining for heart failure symptoms in clinical notes. In: Value Health. Vol 23. Elsevier; 2020:S106. doi:10.1016/j.jval.2020.04.182
- Oguntuga A, Overcash J, Nguyen N. PCV82 Development and validation of a method to extract left ventricular ejection fraction data from EHR physician notes. In: Value Health. Vol 23. Elsevier; 2020:S106. doi:10.1016/j.jval.2020.04.183
- Farah J, Martinez E, Fischer L, et al. Social determinants of health, chronic conditions, and provider specialties: A retrospective electronic health record-derived analysis. Veradigm LLC. Published January 23, 2020. Accessed September 23, 2025. https://veradigm.com/img/resource-social-determinants-of-health-a-retrospective-analysis.pdf