Assessing the effectiveness of artificial intelligence (AI) in prioritising CT head interpretation: study protocol for a stepped-wedge cluster randomised trial (ACCEPT-AI)

Journal article


Vimalesvaran, K., Robert, D., Kumar, S., Kumar, A., Narbone, M., Dharmadhikari, R., Harrison, M., Ather, S., Novak, A., Grzeda, M., Gooch, J., Woznitza, N., Hall, M., Shuaib, H. and Lowe, D. J. 2024. Assessing the effectiveness of artificial intelligence (AI) in prioritising CT head interpretation: study protocol for a stepped-wedge cluster randomised trial (ACCEPT-AI). BMJ Open. 14, pp. 1-7. https://doi.org/10.1136/bmjopen-2023-078227
AuthorsVimalesvaran, K., Robert, D., Kumar, S., Kumar, A., Narbone, M., Dharmadhikari, R., Harrison, M., Ather, S., Novak, A., Grzeda, M., Gooch, J., Woznitza, N., Hall, M., Shuaib, H. and Lowe, D. J.
Abstract

Introduction Diagnostic imaging is vital in emergency departments (EDs). Accessibility and reporting impacts ED workflow and patient care. With radiology workforce shortages, reporting capacity is limited, leading to image interpretation delays. Turnaround times for image reporting are an ED bottleneck. Artificial intelligence (AI) algorithms can improve productivity, efficiency and accuracy in diagnostic radiology, contingent on their clinical efficacy. This includes positively impacting patient care and improving clinical workflow. The ACCEPT-AI study will evaluate Qure.ai’s qER software in identifying and prioritising patients with critical findings from AI analysis of non-contrast head CT (NCCT) scans.

Methods and analysis This is a multicentre trial, spanning four diverse sites, over 13 months. It will include all individuals above the age of 18 years who present to the ED, referred for an NCCT. The project will be divided into three consecutive phases (pre-implementation, implementation and post-implementation of the qER solution) in a stepped-wedge design to control for adoption bias and adjust for time-based changes in the background patient characteristics. Pre-implementation involves baseline data for standard care to support the primary and secondary outcomes. The implementation phase includes staff training and qER solution threshold adjustments in detecting target abnormalities adjusted, if necessary. The post-implementation phase will introduce a notification (prioritised flag) in the radiology information system. The radiologist can choose to agree with the qER findings or ignore it according to their clinical judgement before writing and signing off the report. Non-qER processed scans will be handled as per standard care.

KeywordsDiagnostic imaging ; radiology workforce ; clinical efficacy
Year2024
JournalBMJ Open
Journal citation14, pp. 1-7
PublisherBMJ Journals
ISSN2044-6055
Digital Object Identifier (DOI)https://doi.org/10.1136/bmjopen-2023-078227
Web address (URL)https://bmjopen.bmj.com/content/14/6/e078227
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File Access Level
Open
Output statusPublished
Publication dates
Online16 Jun 2024
Publication process dates
Deposited24 Jul 2024
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https://repository.derby.ac.uk/item/q7808/assessing-the-effectiveness-of-artificial-intelligence-ai-in-prioritising-ct-head-interpretation-study-protocol-for-a-stepped-wedge-cluster-randomised-trial-accept-ai

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