Validation of Electronic Health Record Detection of Patient Safety Outcomes
Bibliographic record
Abstract
Background Adverse events are common for hospitalized Canadians and lead to worse patient outcomes. We aimed to validate the use of our electronic health record (EHR) to monitor important patient safety outcomes. Methods EHR data were abstracted for four high-priority safety outcomes: venous thromboembolism (VTE), hypoglycemia, Clostridium difficile ( C. difficile) infection, and prolonged nil per os (NPO) orders. A manual chart review was performed to determine the sensitivity and specificity of the EHR for each patient safety outcome. Results The sensitivity and specificity were: 94.3% and 99.2% for C. difficile infections, 34.3% and 88.0% for VTE, 96.9% and 96.3% hypoglycemia, 61.8% and 98.5% for prolonged NPO status. Conclusion The EHR is reasonably sensitive and specific in monitoring rates of hypoglycemia, C. difficile infection, and prolonged NPO in medical inpatients. Importantly, validation of EHR data with manual chart review is necessary before using this data to monitor patient safety outcomes.
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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 | 0.104 | 0.258 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".