Impact on clinical guideline adherence of Orient-COVID, a CDSS based on dynamic medical decision trees for COVID19 management: a randomized simulation trial

TL;DR

Guideline adherence is often suboptimal for COVID-19 management. Orient-COVID applies an ontology-based CDSS with fisheye visualization and multi-path decision-tree navigation to guide clinicians through guideline-conformant decisions. In a randomized near-real simulation with six representative cases, Orient-COVID significantly improved overall adherence compared with paper guidelines and no guidance, notably on troponin assessment, anticoagulation, oxygen therapy, and clinical-status decisions. The study supports the potential of ontology-driven, navigable guideline systems to enhance point-of-care decision making and highlights future work to integrate with electronic health records and extend to other clinical conditions.

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 adherence, we conducted a randomized controlled trial in a near-real simulation setting, comparing the decisions taken by medical students using Orient-COVID with those taken with paper guidelines or without guidance, when performing on six realistic clinical cases. Results: The results show that paper guidelines had no impact (p=0.97), while Orient-COVID significantly improved the guideline adherence compared to both other groups (p<0.0003). A significant impact of Orient-COVID was identified on several key points during the management of COVID-19: ordering troponin lab tests, prescribing anticoagulant and oxygen therapy. A multifactor analysis showed no difference between male and female participants. Conclusions: The use of an interactive decision tree for the management of COVID-19 significantly improved the clinician adherence to guidelines. Future works will focus on the integration of the system to electronic health records and on the adaptation of the system to other clinical conditions.

Figures (4)

• Figure 1: Screenshot of the interactive decision tree for the management of hospitalized Covid-19 patients, before any user interaction. It gives an overview of the entire decision process, at a glance. Most nodes are yes/no questions, and use checked/unchecked radio buttons as symbols on the edge. To interact with the tree, the user can either click on the button at the bottom of a current node (e.g. “Yes” or “No”), or directly click on any node, for performing a faster or backward navigation.
• Figure 3: Screenshot of the patient data entry form.
• Figure 4: Boxplot showing the score obtained for each group, the mean and the 95% confidence intervals, and p-values for two-by-two comparisons (* = significant difference).
• Figure 5: Boxplot showing the score obtained for each clinical case (labeled as 1-6 numbers in the box label at the bottom), without or with Orient-COVID (labeled in the box label as AB and C, respectively).