Engaging Residents in Quality Improvement: A Multidisciplinary Collaboration to Decrease the Primary Cesarean Delivery Rate at a New Academic Medical Center
Bibliographic record
Abstract
BACKGROUND: Our facility performs more than 9,000 deliveries per year, with a primary cesarean birth rate of 35% and is the primary teaching site for a new Ob/Gyn residency program. An innovative curriculum includes a formal course in quality improvement methods for new faculty, and active participation of all residents in a QI project. In an effort to decrease the primary cesarean birth rate, a multidisciplinary QI project was developed. METHODS: The team was led by two community faculty members and included two residents, nursing and hospital administration. Interventions included mandatory completion of a FHR course, provider education on current terminology and recommended interventions, and monthly reporting of cesarean delivery rates. The team used a fishbone analysis of the steps leading to a cesarean delivery to create a data collection checklist. Charts were reviewed by the residents to confirm the indication for cesarean delivery along with other pertinent variables. RESULTS: The primary cesarean birth rate decreased from 35% in the first quarter of 2015 to 27% in the first quarter of 2016, a 24% decrease. The project team continues to meet quarterly to discuss ongoing activities necessary to maintain and increase the improvement observed in the initial quarter. Residents and faculty report a strong sense of accomplishment as a result of the project, and an interest in continued participation in QI activities. DISCUSSION: Interdisciplinary partnerships between OBGYN residents, faculty and hospital administration/staff to develop and implement quality improvement projects can improve patient care and provide learners with the knowledge and expertise to engage in quality projects in their future practice.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".