Impact on clinical guideline adherence of Orient-COVID, a CDSS based on dynamic medical decision trees for COVID19 management: a randomized simulation trial
Mouin Jammal, Antoine Saab, Cynthia Abi Khalil, Charbel Mourad, Rosy, Tsopra, Melody Saikali, Jean-Baptiste Lamy

TL;DR
This study demonstrates that an innovative visual decision support system, Orient-COVID, significantly enhances clinicians' adherence to COVID-19 management guidelines in a simulated environment, outperforming traditional paper guidelines.
Contribution
The paper introduces a novel interactive visual interface using a fisheye technique to navigate large decision trees, improving guideline adherence in COVID-19 management.
Findings
Orient-COVID significantly improved guideline adherence (p<0.0003).
No impact of paper guidelines on adherence (p=0.97).
Improved decision-making on key COVID-19 management points.
Abstract
Background: The adherence of clinicians to clinical practice guidelines is known to be low, including for the management of COVID-19, due to their difficult use at the point of care and their complexity. Clinical decision support systems have been proposed to implement guidelines and improve adherence. One approach is to permit the navigation inside the recommendations, presented as a decision tree, but the size of the tree often limits this approach and may cause erroneous navigation, especially when it does not fit in a single screen. Methods: We proposed an innovative visual interface to allow clinicians easily navigating inside decision trees for the management of COVID-19 patients. It associates a multi-path tree model with the use of the fisheye visual technique, allowing the visualization of large decision trees in a single screen. To evaluate the impact of this tool on guideline…
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Taxonomy
TopicsChronic Disease Management Strategies · Medication Adherence and Compliance · Machine Learning in Healthcare
MethodsFocus
