The fast-changing clinical guidelines and fragmented electronic health records (EHRs) have made it difficult for clinicians to make timely, personalized decisions for breast cancer patients. The team used Carta Healthcare’s hybrid intelligence platform to extract structured and unstructured EHR data—such as clinical notes, imaging, pathology, and genomics—into a longitudinal patient view. This comprehensive timeline enabled precise identification of patients who met "Choosing Wisely" criteria for omitting sentinel lymph node biopsy (SLNB), a procedure often unnecessary in older women with early-stage, ER-positive, node-negative breast cancer.
The technology achieved high accuracy (95%) in identifying key clinical variables and was particularly effective at detecting lymphedema, a complication often underreported in traditional datasets. Importantly, the platform’s EHR integration included real-time visual alerts (“nudges”) to guide surgeons during scheduling, leading to a 49% reduction in low-value SLNB procedures.
The intelligent data abstraction is more than a technical solution—it transforms how clinical teams engage with data. Institutions should prioritize tools that synthesize unstructured information and maintain clinical accuracy to support evidence-aligned, patient-specific decision-making. This innovation exemplifies how big data can improve care by recommending less intervention when appropriate, advancing the core mission of precision medicine.
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