<i>Extubation Advisor</i> : Implementation and Evaluation of A Novel Extubation Clinical Decision Support Tool

Importance: Extubation Advisor (EA) is a novel software tool that generates a synoptic report for each Spontaneous Breathing Trial (SBT) conducted to inform extubation decision-making. Objectives: To assess bedside EA implementation, perceptions of utility, and identify barriers and facilitators of use. Design, Setting and Participants: We conducted a phase I mixed-methods interventional study in three mixed intensive care unit (ICUs) in two academic hospitals. We interviewed critical care physicians (MDs) and respiratory therapists (RTs) regarding user-centered design principles and usability. Analysis: We evaluated our ability to consent participants (feasibility threshold 50%), capture complete data (threshold 90%), generate and review EA reports in real-time (thresholds 75% and 80%, respectively), and MD perception of tool usefulness (6-point Likert scale). We analyzed interview transcripts using inductive coding to identify facilitators and barriers to EA implementation and perceived benefit of tool use. Results: We enrolled 31 patients who underwent 70 SBTs. Although consent rates [31/31 (100%], complete data capture [68/68 (100%)], and EA report generation [68/70 (97.1%)] exceeded feasibility thresholds, reports were reviewed by MDs for [55/70 (78.6%)] SBTs. Mean MD usefulness score was 4.0/6. Based on feedback obtained from 36 interviews (15 MDs, 21 RTs), we revised the EA report twice and identified facilitators (ability to track patient progress, enhance extubation decision-making, and provide support in resource-limited settings) and barriers (resource constraints, need for education) to tool implementation. Half of respondents (9 MDs, 9 RTs; combined 50%) perceived definite or potential benefit to EA tool use. Conclusion: This is the first study of a waveform-based variability-derived, predictive clinical decision support tool evaluated in adult ICUs. Our findings support the feasibility of integrating the EA tool into bedside workflow. Clinical trials are needed to assess the utility of the EA tool in practice and its impact on extubation decision-making and outcomes.Trial RegistrationNCT04708509.

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Publication Details

Journal
Journal of Intensive Care Medicine
Published
2024-10-24
DOI
https://doi.org/10.1177/08850666241291524
Citations
1
Primary Topic
Healthcare Decision-Making and Restraints
Type
article
Field-Weighted Citation Impact
0.64
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article

Extubation Advisor : Implementation and Evaluation of A Novel Extubation Clinical Decision Support Tool

Brett N. Hryciw, Karen E. A. Burns, Nathan Scales, Christophe L. Herry et al.
1 citations
Journal of Intensive Care Medicine
Healthcare Decision-Making and Restraints
0.64
article

Extubation Advisor : Implementation and Evaluation of A Novel Extubation Clinical Decision Support Tool

Brett N. Hryciw, Karen E. A. Burns, Nathan Scales, Christophe L. Herry, Aimee Sarti, Natasha Hudek, Andrew Seely, Jamie Brehaut, Emma Lee
article en
1 citations

Abstract

Importance: Extubation Advisor (EA) is a novel software tool that generates a synoptic report for each Spontaneous Breathing Trial (SBT) conducted to inform extubation decision-making. Objectives: To assess bedside EA implementation, perceptions of utility, and identify barriers and facilitators of use. Design, Setting and Participants: We conducted a phase I mixed-methods interventional study in three mixed intensive care unit (ICUs) in two academic hospitals. We interviewed critical care physicians (MDs) and respiratory therapists (RTs) regarding user-centered design principles and usability. Analysis: We evaluated our ability to consent participants (feasibility threshold 50%), capture complete data (threshold 90%), generate and review EA reports in real-time (thresholds 75% and 80%, respectively), and MD perception of tool usefulness (6-point Likert scale). We analyzed interview transcripts using inductive coding to identify facilitators and barriers to EA implementation and perceived benefit of tool use. Results: We enrolled 31 patients who underwent 70 SBTs. Although consent rates [31/31 (100%], complete data capture [68/68 (100%)], and EA report generation [68/70 (97.1%)] exceeded feasibility thresholds, reports were reviewed by MDs for [55/70 (78.6%)] SBTs. Mean MD usefulness score was 4.0/6. Based on feedback obtained from 36 interviews (15 MDs, 21 RTs), we revised the EA report twice and identified facilitators (ability to track patient progress, enhance extubation decision-making, and provide support in resource-limited settings) and barriers (resource constraints, need for education) to tool implementation. Half of respondents (9 MDs, 9 RTs; combined 50%) perceived definite or potential benefit to EA tool use. Conclusion: This is the first study of a waveform-based variability-derived, predictive clinical decision support tool evaluated in adult ICUs. Our findings support the feasibility of integrating the EA tool into bedside workflow. Clinical trials are needed to assess the utility of the EA tool in practice and its impact on extubation decision-making and outcomes.Trial RegistrationNCT04708509.

Journal of Intensive Care MedicineVol. 40(4)
St. Michael's Hospital (CA), University of Ottawa (CA), University of Toronto (CA), Ottawa Hospital (CA)
Openalex Percentile: Top 27%
Healthcare Decision-Making and Restraints
0.64
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