Miscommunication Among Healthcare Professionals In The Hospital Setting: A Quality Improvement Project
Bibliographic record
Abstract
Research has identified that 81% of interruptions during handoff were from the nurse receiving handoff(Rhudy, L., Johnson, M., Krecke, C., Keigley, D., Schnell, S., Maxson, P., McGill, S., & Warfield, K., 2019). Miscommunication in the healthcare field is a considerably large issue. It often times goes unmentioned, which can negatively impact patient’s care. A thorough literature review was conducted and a total of 64 studies were reviewed. Common themes that emerged included language barriers and cultural differences can often be associated with miscommunication in the healthcare setting. With miscommunication and the errors that result from it, trainings and policies have been made in order to reduce the incidence from happening. These include things like teamwork enhancement and communication trainings As a result of this literature review a guideline handout was created to illustrate the SBAR (Situation, Background, Assessment, Recommendations) technique for handoff in order to improve how healthcare providers will communicate information about the patients. This guideline will be shared with clinical faculty. The expected results will show an improvement in the communication among healthcare professionals, which will also improve patient outcomes. Miscommunication must be addressed by every and all members within a healthcare setting in order to try and eliminate errors.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".