Human-Guided Agentic AI for End-to-End EHR Research: Sequential Antibody-Drug Conjugate Pathways in Breast Cancer

Abstract

PURPOSE: To demonstrate an end-to-end research workflow combining a large electronic health record (EHR) resource and a multi-agent artificial intelligence (AI) system governed by Human-Guided Orchestration (HGO), using sequential sacituzumab govitecan (SG) and trastuzumab deruxtecan (T-DXd) orders in breast cancer as the clinical use case. METHODS: A June 2026 EHR release contained 4.95 million patients, including 156,051 with breast cancer codes. Muse Spark supported cohort construction, executable statistical analysis, data-quality checks, literature retrieval, and reporting executed through Symbiotic Agent Teaming (SAT) under investigator direction. The main clinical measure was the second-drug record span, from first to last order. Analyses were descriptive and unadjusted; orders did not confirm administration or progression. RESULTS: The workflow identified 106 patients: 73 had T-DXd ordered first and 33 had SG ordered first. Median second-drug spans were 63 days (interquartile range [IQR], 29–116) for SG after T-DXd and 49 days (IQR, 21–126) for T-DXd after SG; the median difference was −14 days (95% bootstrap CI, −41 to 21; distributional P = 0.42). Reported outputs additionally included cohort definitions, aggregate analyses, evidence context, figures, and a preliminary draft manuscript. CONCLUSION: This study provides a concrete model for integrating large EHR resources, agentic AI, and human expertise across the research cycle. The ADC use case illustrates how computational execution and Expert-in-the-Loop (EITL) adjudication can produce interpretable research outputs and refine their scientific meaning, providing a foundation for extending and evaluating this approach across clinical questions.