Preventing laboratory error and improving patient safety – The role of non-laboratory trained healthcare professionals
Bibliographic record
Abstract
In 2023, the World Health Organization estimates that 1 in every 10 patients experiences harm from unsafe care, with 3 million deaths occurring yearly from the same. Over half the cases of patient harm are preventable and resultant from errors. As 70% of medical decision making involves the laboratory, laboratory medicine is looked upon to improve patient safety. However, laboratory errors are not isolated and unpredictable entities, but rather reflective of the overall healthcare system. Laboratory errors often occur outside the laboratory, as medical testing and procedures are performed by individuals with various levels of quality control training. We describe a case of a specimen labeling error which prolonged a patient’s hospitalization, hindered the medical team’s clinical decision making and increased healthcare cost utilization. We review categories of laboratory errors, outline steps to prevent pre-analytical laboratory errors (defined as those that occur before, during or after specimen collection) and describe metrics to measure quality improvement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".