Improving Transfer of Patient Care Information Between OBGYNs and Pathology Department [23E]
Bibliographic record
Abstract
INTRODUCTION: The greatest number of medical errors occurs at the time of transfer of patient information. This performance improvement project seeks to improve patient safety by improving the communication between obstetricians/gynecologists and pathologists at single tertiary care facility. Ensuring no errors in the surgical pathology forms ensures that patients receive appropriate and timely pathologic evaluation. METHODS: A multidisciplinary team of clinicians and leaders from multiple departments developed educational materials for physician and staff handling pathological specimens, provided individual and group feedback and optimized pathology forms to decrease the amount of errors in communication as pathologic specimens change hands. The number of pathology form errors was identified at the end of every quarter from 2014 to 2016 at Kaiser Permanente Santa Clara. RESULTS: Prior to implementation of this project 11 errors were found in a single quarter. Following the implementation of this project, there was a 68% decrease in yearly pathology form errors. This included a steadily quarterly decline that contained 4 quarters without any errors. There was one outlier in the first quarter of 2015 which contained 6 errors. CONCLUSION: This project demonstrates that communication between different departments, educational training, directed feedback and optimization of pathology forms can decrease the amount of errors in transfer of patient care information, leading to improved patient care and safety.
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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 | 0.015 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".