Do departmental simulation and team training program reduce medical error and improve quality of patient care? A systemic review
Bibliographic record
Abstract
Aim: Simulation-based learning programs have become increasingly popular over the past 20 years to improve healthcare professionals' knowledge, skills, and attitudes while protecting patients from unnecessary risks and errors. However, recommended practices for simulations in healthcare are still unknown; hence this systematic review aimed to assess whether human simulations or machine stimulations programs would help prevent medical errors and improve patient safety. Methods: We searched for all the publications in the Medline, Web of Science, and Google Scholar databases from January 2000 (when the idea of simulation in healthcare to prevent Medical Errors (ME) was employed for the first time by the Institute of Medicine) to Feb 2022 with only English language-based literature. The risk of bias from A randomized controlled trial (RCTs) was assessed through Cochrane's collaboration tool. The Newcastle-Ottawa Scale was used to evaluate the quality of the cohort studies. The main outcome of this review was the improvement in professional skills among healthcare professionals and reduction in medical errors by employing simulation-based training. Results: Overall, the participants who received simulation-based training for the management of different clinical conditions and for the performance of various diagnostic, therapeutic, and surgical procedures showed better learning than those who were given traditional education and training. Moreover, the studies showed that simulation-based training can improve self-efficacy, confidence, and perceptions among medical professionals. Different simulated adult and pediatric scenarios were created to assess the errors and delays during drug infusion-preparation and administration. The simulation was demonstrated to be an effective way of reducing medical errors. Conclusions: By incorporating simulation-based training into medical education curricula, the acquisition of knowledge and professional skills can be improved. Moreover, this can help improve patient outcomes and reduce medical errors. Based on our findings, we suggest that simulation can be best used as a complement to the other methods of healthcare professionals' teaching and training.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".