Currently submitted to: JMIR Human Factors
Date Submitted: Jan 28, 2026
Open Peer Review Period: Feb 2, 2026 - Mar 30, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Determinants of Adoption of a Mechanical Ventilation Dashboard in Intensive Care: A MIDI-Based Evaluation
ABSTRACT
Background:
Lung-protective ventilation (LPV) reduces complications of mechanical ventilation, yet adherence in intensive care (ICUs) remains inconsistent. Digital dashboards may support LPV by improving situational awareness and supporting protocol adherence. However, adoption of such tools in high-acuity clinical environments depends on a range of cognitive, professional and contextual determinants. The Measurement Instrument for Determinants of Innovations (MIDI) provides a validated framework to systematically assess these factors.
Objective:
To identify determinants influencing adoption of a newly piloted mechanical ventilation dashboard in the ICU using the MIDI framework.
Methods:
We conducted a single-center, cross-sectional evaluation among ICU healthcare professionals during a dedicated survey period within a pilot introduction of a mechanical ventilation dashboard at Amsterdam UMC. Participants completed a structured questionnaire consisting of 24 MIDI items adapted to the ICU context rated on a 5-point Likert scale (completely disagree to completely agree), supplemented by open-ended questions on perceived barriers and facilitators to its use. Determinants were classified as facilitators when ≥80% of respondents selected “agree” or “completely agree” and as barriers when ≥20% selected “disagree” or “completely disagree”. Open-ended responses were analyzed using a general inductive thematic approach.
Results:
A total of seventy-one completed questionnaires were analyzed, including responses from nurses, physicians, intensivists, ventilation specialists, and researchers in mechanical ventilation. Six determinants met criteria for facilitators: outcome expectations; self-efficacy; procedural clarity; low complexity; correctness; and observability. Two determinants met criteria for barriers: relevance for client; and professional obligation. Analysis of open-ended responses highlighted perceived barriers such as additional workload, the need for an extra device, overlap with existing systems, and limited role-specific relevance. Facilitators included improved situational overview, educational value, easier trend monitoring, and increased efficiency.
Conclusions:
This evaluation identified key determinants influencing adoption of a mechanical ventilation dashboard in ICU. While the dashboard was generally perceived as useful and easy to understand, adoption was shaped by determinants related to workflow integration, role-specific relevance, and professional responsibility. These findings suggest that successful introduction of digital clinical support tools in intensive care requires attention not only to technical design, but also to how such tools align with users’ roles, daily work processes, and shared clinical responsibilities. Systematic assessment of determinants provides actionable insight into adoption of digital decision-support tools in high-acuity care settings. Clinical Trial: Not applicable; this study was not a registered clinical trial.
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