Evaluation of an electronic health record structured discharge summary to provide real time adverse event reporting in thoracic surgery
Bibliographic record
Abstract
BACKGROUND: The reporting of adverse events (AE) remains an important part of quality improvement in thoracic surgery. The best methodology for AE reporting in surgery is unclear. An AE reporting system using an electronic discharge summary with embedded data collection fields, specifying surgical procedure and complications, was developed. The data are automatically transferred daily to a web-based reporting system. METHODS: We determined the accuracy and sustainability of this electronic real time data collection system (ERD) by comparing the completeness of record capture on procedures and complications with coded discharge data (administrative data), and with the standard of chart audit at two intervals. All surgical procedures performed for 2 consecutive months at initiation (Ti) and 1 year later (T1yr) were audited by an objective trained abstractor. A second abstractor audited 10% of the charts. RESULTS: The ERD captured 71/72 (99%) of charts at Ti and 56/65 (86%) at T1yr. Comparing the presence/absence of complications between ERD and chart audit demonstrated at Ti a high sensitivity and specificity, positive predictive value (PPV) of 95.5%, negative predictive value (NPV) of 93.9% with a kappa of 0.872 (95% CI 0.750 to 0.994), and at T1yr a sensitivity, specificity, PPV and NPV of 100% with a kappa of 1.0 (95% CI 1.0). Comparing the presence/absence of complications between administrative data and chart audit at Ti demonstrated a low sensitivity, high specificity and a kappa of 0.471 (95% CI 0.256 to 0.686), and at T1yr a low sensitivity, high specificity of 85% and a kappa of 0.479 (95% CI 0.245 to 0.714). CONCLUSIONS: We found that the ERD can provide accurate real time AE reporting in thoracic surgery, has advantages over previous reporting methodologies and is an alternative system for surgical clinical teams developing AE reporting systems.
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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 | 0.086 | 0.189 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".