Implementation of an in situ simulation-based training adapted from Morbidity and Mortality conference cases: effect on the occurrence of adverse events—study protocol of a cluster randomised controlled trial
Bibliographic record
Abstract
BACKGROUND: Morbidity and Mortality conference provides the necessary improvement measures for patient safety. However, they are an underused resource mainly because the conclusions to be drawn from the discussion and their implications for practice are not always well integrated by inpatient care teams. We therefore propose in this study two interventions to optimise their effectiveness: a passive feedback with wide dissemination by e-mail and/or on paper of the results of the Morbidity and Mortality conference to inpatient care teams and an active feedback with in situ inter-professional simulation-training programme in which scenarios will be based on cases studied in Morbidity and Mortality conference. In the present study, we hypothesise that the greatest reduction the occurrence of adverse event will be in the active feedback arm. METHODS: A cluster randomised controlled study will be performed at four study sites. The unit of randomisation is wards within the study sites. Fifteen wards will be randomly assigned to passive feedback, active feedback, or a standard MMC (control arm). Passive feedback and active feedback arms will be compared to standard arm in terms of occurrence of adverse events. The trigger tool methodology used to identify adverse events is a retrospective review of inpatient records using "triggers": an adverse event is defined as a patient's stay with at least one positive trigger. DISCUSSION: The in situ simulation training based on cases processed in Morbidity and Mortality conference is built according to the main topics identified for the successful implementation of healthcare simulation in patient safety programmes: technical skills, nontechnical skills, assessment, effectiveness, and system probing. The in situ simulation-training programme conducted as part of the study has the potential to improve patient safety during hospitalisation. We therefore expect the greatest reduction in the occurrence of adverse events in patients hospitalised in the active feedback arm. This expected result would have a direct impact on patient safety and would place in situ simulation at the highest level of the Kirkpatrick model. TRIAL REGISTRATION: Clinicaltrials.gov NCT02771613. Registered on May 12, 2016. All items from the WHO Trial Registration Data Set can be found within the protocol.
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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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".