Reporting of medical errors: An intensive care unit experience
Bibliographic record
Abstract
OBJECTIVE: To determine the occurrence and type of medical errors in an intensive care setting using a voluntary reporting method. DESIGN: Prospective, single-center, observational study. SETTING: The medical intensive care unit (19 beds) at an urban teaching hospital. PATIENTS: Adult patients requiring at least 48 hrs of intensive care. INTERVENTIONS: Prospective reporting of medical errors. MEASUREMENTS AND MAIN RESULTS: During a 6-month period, 232 medical events were reported involving 147 patients. A total of 2598 patient days were surveyed yielding 89.3 medical events reported per 1000 intensive care unit days. The source of the reports included nurses, who reported most of the medical events (59.1%), followed by physicians-in-training (27.2%) and intensive care unit attending physicians (2.6%). One hundred thirty (56.2%) medical events occurred within the intensive care unit and were judged to involve patient careproviders who were working directly in the intensive care unit area. One hundred and two (43.8%) medical events were commissions or omissions that occurred outside of the intensive care unit during patient transports or in the emergency department and hospital floors. Twenty-three (9.9%) medical events leading to a medical error resulted in the need for additional life-sustaining treatment, and seven (3.0%) medical errors may have contributed to patient deaths. CONCLUSION: Medical errors appear to be common among patients requiring intensive care. Medical events resulting in an error can result in the need for additional life-sustaining treatments and, in some circumstances, can contribute to patient death. Patient healthcare providers appear to be in a unique position to identify medical errors. Institutions should develop formalized methods for the reporting and analysis of medical errors to improve patient care.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".