Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Abstract PD11-08: Time Implications of an Oncology Intelligence Platform vs Standard Practice: A UK Single Centre Breast Cancer Multi Disciplinary Team Meeting Simulation Trial
0
Zitationen
14
Autoren
2026
Jahr
Abstract
Abstract Background Multi-Disciplinary Team Meetings (MDTMs) are "gold standard" in the UK cancer care continuum. The National Health Service (NHS) England conducts 55,000 MDTMs annually, consuming over 1.2 million hours of clinician time. This involves pre MDTM case preparation to discussion during MDTM. At the Isle of Wight (IOW), weekly breast cancer MDTMs typically review 30-40 cases over Microsoft (MS) Teams as standard practice, occupying ∼2 hours of clinicians’ time. For the same case volumes, case preparation requires an estimated 50-80 hours per week across three key roles: surgeon, radiologist, and pathologist. The OncoflowTM platform uses Artificial Intelligence, primarily large language models (LLMs) to perform Data Extraction and Treatment Matching to assist Cancer MDTMs. A simulation trial was performed with the objective of evaluating time savings benefit of this platform against standard practice in MDTM case preparation. Methods 2 prospective, synchronous, simulation (non-EHR/Electronic Health Record integrated) breast cancer MDTMs were conducted via MS Teams in 2 phases. The MDT consisted of 1 MDT Coordinator, 1 Surgeon, 1 Medical Oncologist and Radiation Oncologist equivalent from the IOW. Phase 1 was the Standard Arm whereas Phase 2 was the Intervention Arm which implemented the OncoflowTM AI powered Cancer MDTM Coordinator CoPilot software platform, a class 1 UKCA (UK Conformity Assessed) MHRA (Medicines and Healthcare products Regulatory Agency) registered medical device (RN 32434). A set of 10 breast cancer cases each were discussed in each Arm. These were different cases but complexity matched, equal number of "simple" (5), "edge" (2) and "complex" (3). These had variable stage of disease - 5 each of early post-operative and metastatic. Results OncoFlow achieved 100% accuracy in LLM-assisted data extraction and parameter validation. Standard vs Intervention had following time implications - the entire MDTM case preparation time was 120 minutes vs 38 seconds and active MDTM case discussion lasted 29 vs 26 minutes. Dissecting further, these were 14m 7s vs 8m 17s for the 5 “Simple” cases, 5m 12s vs 5m 40s for the 2 “Edge” cases and 10m 26s vs 12m 20s for the 3 “Complex” cases. Projecting these results to the Real-World IOW Breast Cancer MDTM of 30-40 cases meant Case Preparation Time Savings of 6-8 hours/meeting and 310-414 hours annually. A paired T-Test proved that this is statistically significant vs standard practice (p=0.000000002535; 95% CI: 10.77,13.11). Using OncoFlow, time savings of 35-47 minutes/MDTM and 30-40 hours/year were noted during Simple Case active discussion along with 12-16 minutes/meeting and 10-14 hours/year of Net MDT time. Complex Case Discussions were longer, i.e., 19-25 minutes/MDTM and 16-22 hours/year vs standard. Conclusion The OncoflowTM Intelligent Platform was 190 times faster than standard manual case preparation for MDTM presentation. It also demonstrated MDTM time streamlining implications - cutting “simple” case discussion time by 40% and creating more capacity for highly robust “complex” case discussions with a 20% time surplus. These findings are aligned well with the NHS England 2020 Streamlining MDTMs guidance. Engagement with other sites nationally is ongoing for a multicentre, national, simulation MDTM experience with greater sample size. Citation Format: J. Tan, L. Cook, R. Williams, A. Khan, A. Tiwari, P. Garodia, M. Hasanova, B. Lamb, O. Ayodele, S. Adomah, G. Langton, R. Pearson, A. Ghose, A. Maniam. Time Implications of an Oncology Intelligence Platform vs Standard Practice: A UK Single Centre Breast Cancer Multi Disciplinary Team Meeting Simulation Trial [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PD11-08.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.200 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.051 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.416 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.410 Zit.
Autoren
Institutionen
- University of Manchester(GB)
- Isle of Wight NHS Trust(GB)
- Medway School of Pharmacy(GB)
- University of Kent(GB)
- Princess Alexandra Hospital NHS Trust(GB)
- University College London(GB)
- The London College(GB)
- Health and Safety Executive(GB)
- London Cancer(GB)
- University of Leicester(GB)
- NIHR Leicester Biomedical Research Centre(GB)
- Royal Marsden NHS Foundation Trust(GB)
- Clatterbridge Cancer Centre NHS Foundation Trust(GB)
- Department of Health and Social Care(GB)
- General Medical Council(GB)