Quality Improvement and Patient Safety Rounds—A New Paradigm in Cardiothoracic Surgery
Bibliographic record
Abstract
Background: The delivery of cardiothoracic health care is complex, and despite best efforts, adverse patient outcomes do occur. There is significant heterogeneity in morbidity and mortality rounds within and between institutions as well as undertones of a "blame and shame" culture prohibitive to meaningful quality assessment and improvement of patient care. The Quality Improvement and Patient Safety (QIPS) program was designed to address these issues. Methods: QIPS is built on the Donabedian model of quality and established phase-of-care adverse event analysis. It focuses on cultivating a culture of transparency. Every case review yields important demographic data, event factors, and the case inflection point, creating an institution-specific QIPS database. The QIPS format was presented by The Society of Thoracic Surgeons Workforce on Patient Safety at 2 Society of Thoracic Surgeons webinar series in December 2021 and August 2022. Descriptive data on both presentations were collected. Results: The December 2021 webinar had 75 unique viewers from 13 countries, and there were 343 asynchronous playbacks. The August 2022 webinar had 38 unique viewers from 9 countries and 310 asynchronous playbacks. Conclusions: The standardized QIPS methods allow consistent recording of event factors and the inflection point, which are root causes of case morbidity or mortality. Dedication to the program will build a granular databank and identify recurring individual or system issues to launch quality improvement initiatives that address institution-specific needs. QIPS is simple, effective, and reproducible and seeks to create a culture of patient-centered quality and excellence in cardiothoracic surgery programs.
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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 | 0.062 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".