Weaponizing EHRs to Close that Diagnosis Gap - Coupling Agentic AI with EHRs for Earlier, More Equitable Diagnosis with Minority Estrogenopathies

Electronic Health Records (EHRs) are no longer passive repositories built for regulatory compliance. They are evolving into dynamic engines that can actively improve care quality, equity, and outcomes. When coupled with agentic AI, EHRs have the potential to address one of healthcare’s most persistent failures: delayed and inequitable diagnosis among minority women with estrogenopathies. Endometriosis, estrogen receptor-positive breast cancer, ovarian and cervical cancers, and osteoporosis, disproportionately affect women and are strongly influenced by estrogen dysregulation. While biology plays a role in estrogen-related diseases, delayed diagnosis is not a biological issue. Across healthcare systems, women experience longer diagnostic timelines than men, and these delays are amplified among racial and ethnic minority women. The consequences are profound - later-stage disease at presentation, delayed treatment initiation, higher morbidity, and poorer long-term outcomes. We promulgate leveraging of agentic AI in EHRs to transition healthcare from reactive documentation to proactive, equitable diagnosis, particularly for minority estrogenopathies.