Clinical practice, decision-making, and use of clinical decision support systems in invasive mechanical ventilation: a narrative review

Abstract

Invasive mechanical ventilation is a key supportive therapy for patients on intensive care. There is increasing emphasis on personalised ventilation strategies. Clinical decision support systems (CDSS) have been developed to support this. We conducted a narrative review to assess evidence that could inform device implementation. A search was conducted in MEDLINE (Ovid) and EMBASE. Twenty-nine studies met the inclusion criteria. Role allocation is well described, with interprofessional collaboration dependent on culture, nurse:patient ratio, the use of protocols, and perception of responsibility. There were no descriptions of process measures, quality metrics, or clinical workflow. Nurse-led weaning is well-described, with factors grouped by patient, nurse, and system. Physician-led weaning is heterogenous, guided by subjective and objective information, and 'gestalt'. No studies explored decision-making with CDSS. Several explored facilitators and barriers to implementation, grouped by clinician (facilitators: confidence using CDSS, retaining decision-making ownership; barriers: undermining clinician's role, ambiguity moving off protocol), intervention (facilitators: user-friendly interface, ease of workflow integration, minimal training requirement; barriers: increased documentation time), and organisation (facilitators: system-level mandate; barriers: poor communication, inconsistent training, lack of technical support). One study described factors that support CDSS implementation. There are gaps in our understanding of ventilation practice. A coordinated approach grounded in implementation science is required to support CDSS implementation. Future research should describe factors that guide clinical decision-making throughout mechanical ventilation, with and without CDSS, map clinical workflow, and devise implementation toolkits. Novel research design analogous to a learning organisation, that considers the commercial aspects of device design, is required.

Keywords: clinical decision support system; clinical practice; decision-making; implementation; invasive mechanical ventilation; personalised ventilation.

Fig 1

Flow chart of study selection. AI, artificial intelligence; ARDS, acute respiratory distress syndrome; CDSS, clinical decision support system; ML, machine learning.

Fig 2

Schematic representation of nurse involvement in invasive mechanical ventilation decision-making by country in adult patients. This summarises the findings of the literature identified in Supplementary Table S1 by country. It illustrates that nurses are more likely to be involved with titration of ventilator settings, assessing weaning readiness, and weaning failure. Nurses in Switzerland and the UK have the greatest level of invasive mechanical ventilation (IMV) decision-making involvement, whereas Saudi Arabia, Greece and Italy have the lowest.

Fig 3

Factors influencing nurse-led weaning from invasive mechanical ventilation. Factors influencing nurse-led weaning can be grouped into those affecting the patient (blue bars), nurse (dark green bars), and system (purple bars).