Three layers and two revelations: A multidisciplinary framework for curating real-world data from electronic patient records reveals a decade of breast screening performance.

OBJECTIVES: To develop and validate a scalable, semi-automated framework for extracting high-granularity research data from legacy Electronic Patient Records (EPR), using a decade of family history breast screening as the exemplar. METHODS: Our multidisciplinary team developed a three-layer architecture distinguishing raw EPR data, a context layer holding a structured patient journey, and analysis-ready output variables. The context layer was implemented in Structured Query Language (SQL) with explicit rules for cohort identification, exclusions, imaging-event linkage, and outcome derivation. Validation comprised a cohort inclusion audit and an independent patient-journey audit of 904 attendances. RESULTS: The framework distilled 1,276,903 events in 7,781 women into a final cohort of 5,392 women comprising 26,483 screening attendances between 2010 and 2019. The inclusion audit found no missed cases. The journey audit returned seven errors (0.77%); four shared a systematic pattern of clinical recall with normal mammographic coding. Encoding this pattern as an additional SQL rule flagged 82 additional recalls and reduced the effective error rate to 0.33%. Screening performance (cancer detection rate 0.5%, recall rate 4.1%) reproduced the FH01 benchmark. CONCLUSION: Our three-layer framework combining programmatic extraction with iterative clinician validation, and limited manual curation transformed inaccessible real-world EPR data into an audit-ready research dataset at scale. ADVANCES IN KNOWLEDGE: Our study provides a practical approach to overcome the technical barriers and utilise EPR data at a scale not feasible manually. It demonstrates that semi-automated curation can benchmark clinical performance and validate new technologies like DBT in real-world settings where prospective data collection is absent.