Trauma Association of Canada Abstracts 2017
Bibliographic record
Abstract
Background: Emergency general surgery conditions are often thought of as being too acute and unpredictable for the development of standardized approaches to quality improvement (QI).However, process mapping, a concept that has been applied extensively in manufacturing, has been used to understand opportunities for improvement in complex health care processes.This study uses process mapping to deconstruct the surgical care of patients presenting to emergency general surgery (EGS) services with acute small bowel obstruction (SBO).Methods: The American College of Surgeons Emergency General Surgery Quality Improvement Program (EQIP) pilot database was used to identify patients presenting to a single, large teaching hospital over a 1-year period (Mar. 1, 2015, to Mar. 1, 2016) for the nonoperative or operative management of SBO.The EQIP database and chart and electronic health records were used to create process maps for each patient.These maps were evaluated to identify important process issues and areas for improvement.Results: Eighty-seven patients with SBO (34 operative, 53 nonoperative) were identified.Three were excluded for not being admitting to general surgery.Operative SBO had a complication rate of 32%.The processes of care from the time of presentation to the time of follow-up were highly elaborate and variable in terms of duration; however, the sequences of care were found to be consistent.Data visualization strategies were used to identify bottlenecks in care and demonstrated substantial variability in terms of operating room access.Conclusion: Complication rates in the operative care of SBO are high and represent an important QI opportunity in general surgery.Process mapping can identify common themes, even in acute care, and suggest specific performance improvement measures.At our centre, we are directing plan-do-study-act (PDSA) cycles and developing standardized orders and approaches based on process map inputs.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.686 | 0.378 |
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".