17 Improving communication in a multidisciplinary team using digital monitors and a handover tool (ATMIST mnemonic) during paediatric traumas
Why this work is in the frame
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Bibliographic record
Abstract
<h3>Background</h3> Paediatric traumas have been described as high-stake, low occurrence emergencies and are a leading cause of morbidity and mortality worldwide. Delivering appropriate care is imperative and relies on interdisciplinary teamwork. <h3>Assessment of issue</h3> Informal interviews revealed that interruptions from various team members, as they enter the trauma bay, to the trauma team leader (TTL) was felt to contribute to poor communication and teamwork. Digital monitors in the trauma bay at our institution were therefore introduced to display information using the ‘ATMIST (Age, Time, Mechanism, Injury, Signs, Treatment)’ mnemonic. Unfortunately, uptake had been poor, being used in only half of the cases. <h3>Strategy for improvement</h3> We identified factors (figure 1) contributing to the inconsistent use of the ATMIST tool and then implemented various strategies to improve use of the tool such as we increased awareness by email communication and added the ATMIST tool as pre-arrival checkbox to the trauma intake form. Improvement of interventions were studied through structured observation of traumas. The outcome measure was defined as the proportion of total trauma activations with ATMIST tool partially or fully completed. Additional measures included number of interruptions to TTL (clinical measure), number of incorrectly entered items (balancing measure), and TTL satisfaction (qualitative measure). There was an increased use of the ATMIST tool from 50% to 66% in a 2 month period following all interventions. Interruptions to the TTL were observed less frequently and there was no increase in incorrect items displayed. <h3>Lessons learnt</h3> Continuous QI methodology help identify obstacles and strategies to improve overall care. More trauma observations and further PDSA cycles, now that strategies have been implemented, are required in order to determine whether the tool continues to be used, reduces TTL interruptions, improves overall communication and teamwork.
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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 | 0.000 | 0.000 |
| 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 it