ThP1.11 - Harnessing digital consent analytics to highlight multicentre operative and consent trends in breast surgery

Abstract Introduction Paper-based consent is difficult to audit at scale, therefore hospital episode statistics data is used to provide insights into national breast surgical practice. However, as digital consent is becoming routine, analysis of amalgamated data is possible to provide a better understanding of consent practice. Methods Between 22/04/2020-29/11/23, patient anonymised digital consent data was analysed retrospectively using Concentric Health analytics (Audit ID:1123ABS). Inclusion criteria required the responsible clinician to be a breast surgeon, with non-breast surgical procedures excluded. Results n=6860 digital consent episodes were performed across 10 NHS Trusts and 4 private institutions (n=890 unique operations. n=82 unique responsible clinicians, n=191 consenting clinicians). 45.6% (n=3125/6860) of the cases were consented by consultants. 18.7% (n=1286/6860) underwent a mastectomy; 78.5% simple & 21.5% reconstruction (of which 82.6% implant and 17.4% autologous reconstruction). 40.1% (n=2750/6860) underwent breast conserving surgery (79.2% WLE, 18.9% therapeutic mammoplasty, 1.9% Intercostal perforator). Re-excision of margins accounted for 16% of BCS cases (n=441/2750). SLNB was performed in 40.6% and ANC in 11.5% of all breast surgery cases. Regarding localisation; 44.5% Seed, 30.6% Wire, 21.7% palpable/other, 3.2% USS skin mark. Conclusion As well as providing benefits in terms of information sharing and improved patient satisfaction, digital consent also provides an opportunity to provide high-level data, regarding which operations are being consented to, when and by whom.

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Journal
British journal of surgery
Published
2024-09-01
DOI
https://doi.org/10.1093/bjs/znae197.233
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
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article

ThP1.11 - Harnessing digital consent analytics to highlight multicentre operative and consent trends in breast surgery

Connor Moore, Edward St John
British journal of surgery
Artificial Intelligence in Healthcare and Education
article

ThP1.11 - Harnessing digital consent analytics to highlight multicentre operative and consent trends in breast surgery

Connor Moore, Edward St John
article en

Abstract

Abstract Introduction Paper-based consent is difficult to audit at scale, therefore hospital episode statistics data is used to provide insights into national breast surgical practice. However, as digital consent is becoming routine, analysis of amalgamated data is possible to provide a better understanding of consent practice. Methods Between 22/04/2020-29/11/23, patient anonymised digital consent data was analysed retrospectively using Concentric Health analytics (Audit ID:1123ABS). Inclusion criteria required the responsible clinician to be a breast surgeon, with non-breast surgical procedures excluded. Results n=6860 digital consent episodes were performed across 10 NHS Trusts and 4 private institutions (n=890 unique operations. n=82 unique responsible clinicians, n=191 consenting clinicians). 45.6% (n=3125/6860) of the cases were consented by consultants. 18.7% (n=1286/6860) underwent a mastectomy; 78.5% simple & 21.5% reconstruction (of which 82.6% implant and 17.4% autologous reconstruction). 40.1% (n=2750/6860) underwent breast conserving surgery (79.2% WLE, 18.9% therapeutic mammoplasty, 1.9% Intercostal perforator). Re-excision of margins accounted for 16% of BCS cases (n=441/2750). SLNB was performed in 40.6% and ANC in 11.5% of all breast surgery cases. Regarding localisation; 44.5% Seed, 30.6% Wire, 21.7% palpable/other, 3.2% USS skin mark. Conclusion As well as providing benefits in terms of information sharing and improved patient satisfaction, digital consent also provides an opportunity to provide high-level data, regarding which operations are being consented to, when and by whom.

British journal of surgeryVol. 111(Supplement_8)
Portsmouth Hospitals NHS Trust (GB)
Openalex Percentile: Top 89%
Artificial Intelligence in Healthcare and Education
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