{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T22:39:52Z","timestamp":1792449592374,"version":"4.5.0"},"reference-count":9,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T00:00:00Z","timestamp":1789171200000},"content-version":"am","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000272","name":"National Institute for Health Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000272","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Biomedical Research Center"},{"name":"Royal Marsden NHS Foundation Trust and the Institute of Cancer Research"},{"DOI":"10.13039\/100016916","name":"Royal Marsden Cancer Charity","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100016916","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006662","name":"NIHR","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006662","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000276","name":"Department of Health and Social Care","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000276","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Our multidisciplinary team developed a 3-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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The framework distilled 1\u00a0276\u00a0903 events in 7781 women into a final cohort of 5392 women comprising 26\u00a0483 screening attendances between 2010 and 2019. The inclusion audit found no missed cases. The journey audit returned 7 errors (0.77%); 4 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our 3-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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Advances in knowledge<\/jats:title>\n                    <jats:p>Our study provides a practical approach to overcome the technical barriers and utilize 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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bjr\/tqag226","type":"journal-article","created":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T10:23:07Z","timestamp":1789208587000},"source":"Crossref","is-referenced-by-count":0,"title":["Three layers and two revelations: a multidisciplinary framework for curating real-world data from electronic patient records reveals a decade of breast screening performance"],"prefix":"10.1093","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0063-8082","authenticated-orcid":false,"given":"Richard","family":"Sidebottom","sequence":"first","affiliation":[{"name":"Radiology, AI Imaging Hub, The Royal Marsden , Sutton, Surrey SM2 5PT,","place":["United 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