Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Medical Imaging Decision And Support (MIDAS): Study protocol for a multi-centre cluster randomized trial evaluating the ESR iGuide
11
Zitationen
8
Autoren
2023
Jahr
Abstract
OBJECTIVES: Medical imaging plays an essential role in healthcare. As a diagnostic test, imaging is prone to substantial overuse and potential overdiagnosis, with dire consequences to patient outcomes and health care costs. Clinical decision support systems (CDSSs) were developed to guide referring physicians in making appropriate imaging decisions. This study will evaluate the effect of implementing a CDSS (ESR iGuide) with versus without active decision support in a physician order entry on the appropriate use of imaging tests and ordering behaviour. METHODS: A protocol for a multi-center cluster-randomized trial with departments acting as clusters, combined with a before-after-revert design. Four university hospitals with eight participating departments each for a total of thirty-two clusters will be included in the study. All departments start in control condition with structured data entry of the clinical indication and tracking of the imaging exams requested. Initially, the CDSS is implemented and all physicians remain blinded to appropriateness scores based on the ESR imaging referral guidelines. After randomization, half of the clusters switch to the active intervention of decision support. Physicians in the active condition are made aware of the categorization of their requests as appropriate, under certain conditions appropriate, or inappropriate, and appropriate exams are suggested. Physicians may change their requests in response to feedback. In the revert condition, active decision support is removed to study the educational effect. RESULTS/CONCLUSIONS: The main outcome is the proportion of inappropriate diagnostic imaging exams requested per cluster. Secondary outcomes are the absolute number of imaging exams, radiation from diagnostic imaging, and medical costs. TRIAL REGISTRATION NUMBER: Approval from the Medical Ethics Review Committee was obtained under protocol numbers 20-069 (Augsburg), B 238/21 (Kiel), 20-318 (Lübeck) and 2020-15,125 (Mainz). The trial is registered in the ClinicalTrials.gov register under registration number NCT05490290.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.700 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.605 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.133 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.873 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.