Abstract PO-095: The PROState AI Cancer–Decision Support (PROSAIC-DS) pilot study: Clinical decision support technology and its role in prostate cancer MDT meetings
Abstract Background Multidisciplinary teams (MDT/tumour boards) were first introduced in the 1990s and have experienced little change to their methodology since. Universally used for the treatment of prostate cancer (CaP) in the UK new interventions are proposed to improve MDT efficiency and patient...
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| Published in: | Clinical cancer research Vol. 27; no. 5_Supplement; p. PO-095 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
01-03-2021
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| Online Access: | Get full text |
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| Summary: | Abstract
Background Multidisciplinary teams (MDT/tumour boards) were first introduced in the 1990s and have experienced little change to their methodology since. Universally used for the treatment of prostate cancer (CaP) in the UK new interventions are proposed to improve MDT efficiency and patient outcomes. Clinical practice guidelines (CPGs), designed to increase the uptake of evidence-based practice, suffer from lack of proper implementation. There is increasing evidence to suggest a gap between CPGs and actual treatment and the use of artificial intelligence an (AI) systems can help to increase the efficiency of the MDT. Methods We evaluated differences in MDT concordance with guidelines in the primary treatment of localised or locally advanced prostate cancer using the Deontics AI based custom clinical decision support software (CDSS) software. 59 paper cases were created by an expert clinician, 9 of which were excluded as they did not meet eligibility criteria. The remaining 50 cases were provided to the CDSS for evaluation. Simultaneously, two physicians assessed each patient case and provided their treatment recommendations. The results were assessed for concordance with UK, European and American guidelines, inter-rater reliability and trends in concordance based on patient variables.Results Overall clinician concordance with guidelines was 76%, while total concordance with all three guidelines was 28%. Overall concordance was highest with NICE guidelines, while total concordance was highest for NCCN guidelines. Inter-rater reliability was highest for the NCCN guidelines. Age < 75 (p <0.001; odds ratio [OR], 35.000), prostate volume < 46.5ml (p =0.047; OR, 4.909), and a Gleason score ≠ 8 (p =0.013; OR, 12.333), were all significantly associated with increased guideline concordance in this study. Conclusions Concordance with CPGs needs to be improved in specific patient groups. This may reflect cognitive bias, cognitive overload, or conflicting guideline recommendations and evidence base. One potential solution may be the integration of CDSS technology into the MDT setting.
Citation Format: Vishal Santis, Deborah Enting, Vivek Patkar, Anastasia Chalkidou, John Fox, Danny Ruta, Jonathan K. Makanjuola. The PROState AI Cancer–Decision Support (PROSAIC-DS) pilot study: Clinical decision support technology and its role in prostate cancer MDT meetings [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-095. |
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| ISSN: | 1078-0432 1557-3265 |
| DOI: | 10.1158/1557-3265.ADI21-PO-095 |