From leadership research to district action: Perceptions and knowledge of machine learning for paediatric related decision support in emergency care – A UK and Ireland network survey study of clinician leaders

AI-assisted editorial implementation guide. Proposed district actions are separate from the original authors’ findings and recommendations. Independent peer review of this guide is not asserted.

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Evidence and transfer

Education-related source: verify which participants, institutions and outcomes were actually studied before district adoption. Source publication type: research-article. License and identity checks do not establish study quality or causality.

Original authors’ abstract, reproduced under the source license:

This study explores clinician leaders understanding and perception at site level towards machine learning (ML) decision support tools for paediatric related emergency care across the UK and Ireland, essential in guiding safe and effective frontline implementation. A cross-sectional online survey was distributed via Paediatric Emergency Research United Kingdom and Ireland (PERUKI) to the lead for digital systems or PERUKI site lead, with one response sought per site. Survey development was in REDCap, and descriptive analysis (counts, percentages) was primarily performed. The response rate was 86.7% (65/75), mostly from England (83.1%). While 80.0% understood ‘Artificial Intelligence’, fewer understood advanced concepts such as ‘Deep Learning’ (32.3%). Most clinicians believed ML will support decision making (83.1%), would be willing to use (87.7%), and the future of decision making is a combination of human and ML (83.1%). Barriers included concerns about bias (61.5%), ML accuracy (56.9%), and inadequate information technology infrastructure (67.7%). Digital leads were more concerned about ML accuracy than non-digital (68.2% vs. 51.2%). Among potential applications, antimicrobial stewardship ranked highest (90.8%), and diagnosis of mental health conditions lowest (24.6%). Strong interest in ML tools for decision support in paediatric emergency care was evident, though concerns about bias, accuracy, and infrastructure must be addressed. Ongoing co-design with clinicians is critical in ensuring these tools are trusted, useful and suited to paediatric emergency care. Targeted education, digital leadership, and strategic investment in infrastructure and governance are essential for the successful adoption and integration of ML in clinical workflows. Author summary Within emergency care we are seeing a rapid growth in the research, development and frontline use of machine learning based tools for decision support, yet very little is known about the intended users understanding, opinions, experience of this technology and supporting structures. With any new technology a greater understanding leads to better adoption and the clinicians who will use these tools should be directly involved in their design, implementation and evaluation to ensure that these tools are clinically relevant and usable in practice. Through our survey of clinical site leads (key drivers, influencing the adoption of ever advancing technology), the findings provide critical insight into what clinicians are most concerned about, their perceptions, understanding, what applications they view as most clinically relevant and their willingness to be involved in future research and development of these tools. The results also revealed many important themes such as infrastructure readiness, trust, explainability, clinical integration, targeted education and human-artificial intelligence collaboration. These findings will contribute to shaping the future of research, development, education, governance and policy within this rapidly growing area.

Study methods

A cross-sectional online and anonymised survey study was conducted. Two separate surveys were developed as part of a broader study. This paper reports on the first survey, which targeted clinician site leaders across the UK and Ireland. A second survey, aimed at capturing the knowledge and views of the wider ED workforce (including frontline doctors, nurses, and ED support staff), was also conducted and will be reported and published separately. The survey questions were primarily informed by previous research [18–23] and refined through input, discussion and consensus among the study investigators (survey sections in Table 1), several of whom are extensively experienced in survey design. Survey usability and technical testing was undertaken by the study investigators and an independent emergency clinician reviewer, with revisions made based on feedback received. The study proposal and survey questions were initially reviewed by the Paediatric Emergency Research in the United Kingdom and Ireland (PERUKI) governance and research steering committees, whose feedback from 21 independent emergency medicine clinician respondents resulted in refinements to the study design, survey questions and the development of a video explaining key AI concepts to help respondents understand questions being asked in the survey. The survey questions are detailed in S1 File.

Limitations

The survey was distributed through the PERUKI network, eliciting one response per site, reflecting the opinions of the leads as opposed to the wider ED workforce. Findings depended on the accurate self-reporting from a single respondent per site. Some respondents struggled with AI concepts that were explained in the educational video, evident from the low percentage of respondents who confirmed using knowledge (rule) based systems at 38.5%. Fewer digital leads compared to non-digital responded, potentially impacting on responses around AI readiness and infrastructure challenges. Inferential analyses were limited by sample size and group imbalance, particularly between digital and non-digital leads. This may have reduced the power to detect statistically significant differences, even where descriptive trends were observed. Finally, the variability in digital maturity across the sites would have influenced responses, however this was also an important insight.

A 90-day district inquiry

Days 1–15 · Appraise. Read the source methods, recruitment, comparator, outcomes and limitations. Record how the study setting differs from your district and which claim the design can support.
Days 16–30 · Design. Choose one recurring leadership practice described in the source. Map its participants, incentives and constraints to a district setting, then design a small reversible staff pilot with an accountable owner. Agree participation, privacy protections, baseline and stopping rules before starting.
Days 31–60 · Observe. Collect the pre-agreed measures, keep a log of implementation changes and invite confidential voluntary feedback. Preserve existing support for staff and learners.
Days 61–90 · Decide. Record implementation fidelity, staff review time, task quality and unintended burdens against a baseline. Use a comparison team where feasible and document alternative explanations before expanding. Make a dated decision to stop, revise or extend the inquiry.

Board decision brief

Present the local problem, source setting, evidence limits, intervention, responsible owner, total cost, measured changes and uncertainty. Separate staff perceptions, implementation measures and student learning outcomes.

Attribution and reuse

Fiona Leonard; Mark D Lyttle; Dympna O’Sullivan; John Gilligan; Damian Roland; Michael Barrett; on behalf of PERUKI. Perceptions and knowledge of machine learning for paediatric related decision support in emergency care – A UK and Ireland network survey study of clinician leaders. PLOS Digital Health (2026-02-09). https://doi.org/10.1371/journal.pdig.0001213

Prepared 2026-11-07. Source authorship and DOI are preserved. The abstract and source passages are attributed extracts; this editorial guide is offered under the same CC BY 4.0 license.