ArticleJMIR human factors2026
Determinants of Adoption of a Mechanical Ventilation Dashboard in Intensive Care: Measurement Instrument for Determinants of Innovations-Based, Single-Center, Cross-Sectional Evaluation.
Article in JMIR human factors, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
Background: Lung-protective ventilation (LPV) reduces complications of mechanical ventilation, yet adherence in intensive care units (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: This study aims to identify determinants influencing the 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 health care professionals during a dedicated survey period within a pilot introduction of a mechanical ventilation dashboard at Amsterdam University Medical Center. 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 71 completed questionnaires were analyzed, including responses from nurses, physicians, intensivists, ventilation specialists, and researchers in mechanical ventilation. Six determinants met the criteria for facilitators: outcome expectations, self-efficacy, procedural clarity, low complexity, correctness, and observability. Two determinants met the 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 the adoption of a mechanical ventilation dashboard in the 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 insights into the adoption of digital decision-support tools in high-acuity care settings.
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