User perceptions of machine learning models as decision support for colorectal cancer multidisciplinary team conferences (AID-SIM-2): a qualitative simulation study
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Le résumé fourni par la source
BACKGROUND: Multidisciplinary team (MDT) conferences are considered a cornerstone of decision-making in cancer diagnostics and care. However, the current literature has not demonstrated improved patient outcomes based on the decisions of the MDT conferences. AIM: We aimed to evaluate how four different decision-support modalities impacted the decision-making process and the internal discussions in the MDTs in a multicenter simulation study, with a focus on user perceptions. METHODS: Four colorectal cancer centers with MDTs participated. We performed four simulations in each center. Each simulation used a different decision-support tool: (1) Current standard, (2) Current standard plus a prediction model, (3) A structured data presentation tool, and (4) A structured data presentation tool plus the prediction model. Clinician- and model-estimated risks were compared, the treatment suggestions from each site were compared, questionnaires about user perceptions were conducted after Simulations 2, 3, and 4 using a Google Form link, and a semi-structured interview was conducted at each site after the last simulation. RESULTS: Similar distributions of risk groups between clinicians and models were found; however, distinct discrepancies in predictions arose, particularly with higher-risk patients, highlighting the need for standardization for more complex clinical cases. The primary perceived benefit of decision support was increased standardization of care, independent of the individual physicians' personal views. However, participants emphasized the necessity of clinician autonomy to overrule tool suggestions when identifying clinical nuances not captured by the model. CONCLUSIONS: The colorectal cancer MDTs expressed a positive view regarding the use of prediction models and other forms of decision-support in their workflow. While clinicians and prediction models had similar risk score distributions, they diverged in the assessment of specific individual patients.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- User perceptions of machine learning models as decision support for colorectal cancer multidisciplinary team conferences (AID-SIM-2): a qualitative simulation study
- Date Crossref
- 31/07/2026
- Éditeur
- MJS Publishing, Medical Journals Sweden AB
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Region Zealand pays non établi dans la noticeOrganisme public
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Zealand University Hospital Center for Surgical Sciences pays non établi dans la noticeÉtablissement de santé
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Copenhagen Academy for Medical Education and Simulation pays non établi dans la noticeUniversité ou école supérieure
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Slagelse Hospital pays non établi dans la noticeÉtablissement de santé
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University of Copenhagen pays non établi dans la noticeUniversité ou école supérieure
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Copenhagen University Hospital pays non établi dans la noticeÉtablissement de santé
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Oslo University Hospital Department of Surgical Oncology pays non établi dans la noticeÉtablissement de santé
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Norwegian Cancer Society pays non établi dans la noticeOrganisation à but non lucratif
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The Hospital of Mid and Western Zealand pays non établi dans la noticeÉtablissement de santé
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Charlie Tango pays non établi dans la noticeOrganisation à but non lucratif
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Faculty of Health and Medical Sciences Department of Clinical Medicine pays non établi dans la noticeUniversité ou école supérieure
Region Zealand, Center for Surgical Sciences — Zealand University Hospital et Copenhagen Academy for Medical Education and Simulation, avec 8 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.