Paediatric Practitioners' Acceptance of the National Paediatric Early Warning System (PEWS).

Background and Rationale

Early detection of physiological deterioration in hospitalised children is crucial in ensuring timely critical care unit admission and optimising outcomes [1,2,3]. Although our hospital had used a PEWS in both paper and electronic form since 2006, it was believed that standardisation of PEWS used across England was needed. Thus, in 2025, NHS England implemented a national standardised approach [4]; the digital National Paediatric Early Warning System (PEWS).

However, as is the case for any type of digital implementation, it is vital to understand users'acceptanceof these. Increasing research has examined technology acceptance [5,6,7,8,9] and the psychological factors relating to technology adoption. The Technology Integration Model (TIM; [10]) is such a model which has been applied to understanding acceptance of digital health systems [11]. Our project applied the TIM to understand paediatric staffs' acceptance of PEWS. This evaluation was situated to measure acceptance both before and after the initiation of a new digital method of PEWS delivery. We asked:What are the key barriers and enablers associated with PEWS?To what extent are there changes in acceptance of PEWS over time?

Method and Analysis

Study Design and Sample

Prior to commencement, our project was registered as a service evaluation with Alder Hey Children's Hospital (Service Evaluation ID reference number: 7364). This evaluation is reported according to the SQUIRE reporting checklist.

Paediatric clinical staff (N= ~1400) who represented the following sub‐groups were invited to take part in an online survey: paediatric nurses (staff nurse to nurse manager level), non‐qualified nursing support staff student nurses, and the staff who respond to PEWS triggers, comprising medical staff, advanced nurse practitioners and the hospital Medical Emergency/Outreach team (who intercept warning alerts). The survey was completed at two time points; May 2025 (T1), prior to new delivery roll‐out, and from August 2025 (T2) at post‐roll‐out. At each time point, the survey took approximately 10 min to complete. A total of 124 participants took part in the survey over the two time points (n= 62 at both T1 and T2), but after removing incomplete data, this resulted in a final sample of 55 in total.

The 21‐item survey is comprised of a short demographics section and the Technology Integration Evaluation Resource (TIER) to measure users' perceptions of factors associated with acceptance of PEWS, which is based on factors of the TIM [10]. See Appendix1for TIER items. This includes the following factors: technological features and functions, user agency, situational context, user motivation, cost–benefit analysis (extent of deliberate decision‐making) and self‐extension (extent to which technology system extends vs. reduces capabilities). Mean scores were calculated to generate a total acceptance score (TIER total) and for each sub‐scale. The TIER scale showed good levels of internal consistency, with a Cronbach's α of 0.73 (Time 1) and 0.74 (Time 2).

Data Analysis

To evaluate any change in acceptance between Time 1 and Time 2, a MANOVA was conducted. Additionally, to understand what were perceived key barriers and enablers of acceptance, we used the median for each of the TIER scales to identify frequencies of responders who scored above versus below this at each time‐point. TIER variables with a significantly higher frequency of high compared to low responses is indicative of the TIER variable being an enabler. To assess any differences in frequencies of high vs. low responses between Time 1 and Time 2, a Chi‐squared test was performed for total TIER as well as the sub‐scales. Finally, to assess the extent to which high and low scores (determined by median split) on each TIER sub‐scale related to overall PEWS acceptance, we conducted a Pearson's correlation.

Results

At Time 1 (N= 34), the majority of participants were female (97.1%), with an average age of 37.53 years (SD = 10.23), and an average length of service in their respective role at the trust of 6.87 years (SD = 7.83). In terms of role at the trust, the majority of respondents were nurses (n= 27), most of whom were staff nurses (n= 17) with the remaining participants being care assistants (n= 3) and response team members (n= 4). At Time 2 (N= 21), all participants were female, with an average age of 35.05 years (SD = 9.90), and an average length of service in their respective role at the trust of 7.74 years (SD = 6.97). In terms of role at the trust, the majority of respondents were nurses (n= 18), most of whom were staff nurses (n= 11) with the remaining participants being care assistants (n= 3).

Change in Acceptance

Change analysis revealed that there were no significant differences in PEWS acceptance between Time 1 and Time 2F(6, 36) = 1.22,p= 0.317; Wilk's Λ = 0.83, partialη2= 0.169 (Table1).

Table: Descriptive analysis of acceptance factors.

Key Barriers and Enablers

When using the median values to determine the frequency of cases that were enablers (greater frequency of high scores) versus barriers (greater frequency of low scores) for acceptance, enablers were agency (T1), extension of self (T1&T2), motivation (T2) and cost–benefit analysis (T2). Barriers were motivation (T1), technological features (T1&T2), agency (T2) and situation context (T2). However, our inferential analysis did not find any significant changes in the proportion of high versus low scores per variable between time points 1 and 21(see Table2).

Table: Frequencies of responders for high versus low per TIER factor between time points.

Factors on Digital Acceptance

At Time 1, we found that extension of self (r= 0.59,p< 0.01), and motivation (r= 0.57,p< 0.01) were positively related to acceptance. At Time 2, similarly, we found extension of self (r= 0.55,p< 0.05) and motivation (r= 0.75,p< 0.001) to be positively related to PEWS acceptance, in addition to agency (r= 0.56,p< 0.01) and cost–benefit analysis (r= 0.68,p< 0.001).

Discussion

Overall, we did not find that paediatric staff's acceptance changed significantly from pre‐ to post‐roll‐out of the new national PEWS system. This might be explained by the fact that acceptance at Time 1 was relatively high for most TIER factors (with most items averaging above the mid‐point of the scale), indicating that the baseline acceptance was perhaps already established to some extent or other. Interestingly, practitioners' perceptions of PEWS being an extension of self, and its relationship to overall acceptance demonstrates that there are important identity aspects to consider in the use of digital systems when used in organisational contexts. Additionally, perceptions of agency and being in control of PEWS were found to be important post‐roll‐out, indicating that users' self‐efficacy of using digital systems is also a key factor towards overall acceptance.

Limitations

This was only a small‐scale evaluation, conducted within a single institution with a small sample size, which may limit the generalisability of the findings to other settings.

Conclusion

Overall, we note the value of adopting a psychologically informed perspective to understand factors that might support digital transformation within critical care settings. This suggests the need to focus not only on the technical aspects of digital system implementation but also the human factors relating to practitioners' self‐efficacy, identity and motivation relating to its use in the paediatric care context.

Funding

The authors have nothing to report.

Ethics Statement

As this is a change in the existing service at Alder Hey Children's Hospital, the project was registered as a service evaluation with the trust. This was reviewed and approved by the Clinical Audit Team within the Trust's Governance and Quality Assurance Department (Service Evaluation ID reference number: 7364).

Consent

The authors have nothing to report.

Conflicts of Interest

The authors declare no conflicts of interest.

References

1. A. Bracken, S. Lane, S. Siner, et al. , “Assessing the Performance of Paediatric Early Warning Scores to Predict Critical Deterioration Events in Hospitalised Children (The DETECT Study): A Retrospective Matched Case‐Control Study, ”BMC Pediatrics25(2025): 520, . doi.org/10.1186/s12887-025-05754-x
2. V. Lambert, A. Matthews, R. MacDonell, andJ. Fitzsimons, “Paediatric Early Warning Systems for Detecting and Responding to Clinical Deterioration in Children: A Systematic Review, ”BMJ Open7, no. 3(2017): e014497, . doi.org/10.1136/bmjopen-2016-014497
3. G. Sefton, C. McGrath, L. Tume, S. Lane, P. J. Lisboa, andE. D. Carrol, “What Impact Did a Paediatric Early Warning System Have on Emergency Admissions to the Paediatric Intensive Care Unit? An Observational Cohort Study, ”Intensive & Critical Care Nursing31, no. 2(2015): 91–99, . doi.org/10.1016/j.iccn.2014.01.001
4. NHS England, “National Paediatric Early Warning System (PEWS), ”(2026), . doi.org/10.7748/ncyp.2025.e1544
5. F. D. Davis, R. P. Bagazzie, andP. R. Warshaw, “User Acceptance of Computer Technology: A Comparison of Two Theoretical Model Authors, ”Inform35, no. 8(1989): 982.
6. N. MarangunicandA. Granic, “Technology Acceptance Model: A Literature Review From 1986 to 2013, ”Universal Access in the Information Society14, no. 1(2015): 81–95, . doi.org/10.1007/s10209-014-0348
7. V. Venkatesh, M. G. Morris, G. B. Davis, andF. D. Davies, “User Acceptance of Information Technology: Toward a Unified View, ”Management Information Systems Quarterly27, no. 3(2003): 425–478.
8. V. Venkatesh, J. Y. L. Thong, andX. Xu, “Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology (February 9, 2012), ”MIS Quarterly36, no. 1(2012): 157–178, .
9. S. Y. Yousafzai, G. R. Foxall, andJ. G. Pallister, “Technology Acceptance: A Meta‐Analysis of the TAM: Part 1, ”Journal of Modelling in Management2, no. 3(2007): 251–280.
10. H. Shaw, D. A. Ellis, andF. V. Ziegler, “The Technology Integration Model (TIM): Predicting the Continued Use of Technology, ”Computers in Human Behavior83(2018): 204–214.
11. L. Liverpool, K. Fletcher, C. Tahira, D. Jay, F. Walters, andL. K. Kaye, “Implementing a Mental Health App Intervention in a University Setting: Multi‐Methods Evaluation Study, ”Mental Health and Digital Technologies2, no. 1(2024): 43058, . doi.org/10.1108/MHDT-07-2024-0015

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Republished from the open web under CC-BY. Authors: Kaye LK, Tume LN, White P. Read the original.