Assessing the electronic Bedside Paediatric Early Warning System: A simulation study on decision-making and usability
Bibliographic record
Abstract
BACKGROUND: The Bedside Paediatric Early Warning System (BedsidePEWS) is a clinical decision support tool designed to augment clinician expertise, objectively identify children at risk for clinical deterioration, and standardize and prioritize care to improve outcomes in community settings. Although the paper-based BedsidePEWS documentation record has been shown to improve clinicians' perception of their ability to detect deterioration and follow care recommendations, research is needed to asses this impact empirically. Furthermore, as hospitals progressively move toward electronic clinical systems, knowledge regarding the impact of BedsidePEWS' novel electronic interface on clinicians' performance and user experience is required. OBJECTIVES: The primary objectives of this study were (1) to compare adherence to evidence-based care recommendations using a) electronic health record software, b) paper BedsidePEWS, and c) a novel electronic BedsidePEWS interface, and (2) to describe end-users' experiences of usability and opportunities for improvement of both paper and electronic BedsidePEWS. METHODS: Paediatric nurses participated in a repeated measures simulation study. Participants assessed simulated patients, documented patient data, and responded to a series of questions regarding follow-up care for each patient. Three patient types (i.e., stable, mild deterioration, severe deterioration) were assessed in each of three intervention conditions (i.e., electronic health record, paper BedsidePEWS, electronic BedsidePEWS). Following simulation scenarios, participants provided comments regarding the usability of the paper and electronic tools. RESULTS: Participants made 12.7% and 18.0% more appropriate care decisions with paper and electronic BedsidePEWS, respectively, than with the electronic health record intervention (p < 0.001). Accurate BedsidePEWS severity of illness score calculation was related to better adherence to evidence-based care recommendations (65%), compared to inaccurate calculation (55%), and electronic BedsidePEWS was associated with 15.7% fewer calculation errors than paper (p < 0.005). Electronic BedsidePEWS demonstrated usability benefits over its paper predecessor, including automatic score calculation and data plotting, and the potential to eliminate double charting, and participants expressed a preference for electronic BedsidePEWS in all aspects of the debrief questionnaire (p < 0.001). CONCLUSIONS: BedsidePEWS in both paper and electronic formats significantly improved participants' ability to detect deterioration and follow care recommendations compared to electronic health record software. Furthermore, results suggest that electronic BedsidePEWS would afford improved patient care in excess of the paper-based original and further contribute to the standardization, prioritization, and improvement of care in community settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".