Interprofessional Collaboration in Building In Situ Simulations to Identify Threats to Patient Safety Before Transitioning to a New Healthcare Environment: Neonatal Intensive Care as an Example
Bibliographic record
Abstract
Background and objective While transitioning to a new healthcare environment (HCE) offers opportunities to enhance patient safety and outcomes, it can also introduce hidden risks. This study aimed to explore how interprofessional collaboration (IPC) and in situ simulations (ISS) can proactively identify and resolve these latent safety threats (LSTs) before transitioning to a new single-patient room neonatal ICU (NICU). Methodology We conducted a prospective, simulation-based intervention study involving healthcare professionals (HPs) and prior NICU parents. Three simulation activities were conducted to identify LSTs before the transition. The Canadian Interprofessional Competency Framework was employed to formulate realistic scenarios. Results A total of 108 HPs participated in six simulation sessions, identifying 89 LSTs across eight themes. The majority (76%) of these threats were resolved before the transition. Survey analysis revealed significant increases in systems readiness and staff preparedness post-simulations (p<0.001). Parental involvement significantly enhanced the focus on patient-centered care, leading to improvements in environmental design and communication systems. Conclusions The study demonstrates the efficacy of IPC and ISS in identifying and mitigating LSTs during HCE transitions, fostering a collaborative and safety-oriented culture. This approach prepares healthcare teams for new environments and emphasizes the value of incorporating family perspectives. Interprofessional ISS is a pivotal strategy to enhance patient safety and system readiness during transitions to new HCEs. The study also highlights the importance of IPC in conducting ISS before transitioning to a new HCE. Coordinating large-scale simulations is worth the time and cost investment necessary to identify LSTs, optimize systems readiness, and promote patient safety. We hope that the shared lessons can help future interprofessional teams in terms of plan testing and transitions to other HCEs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".