The Cost of Pre-Analytical Errors in INR Testing at a Tertiary-Care Hospital Laboratory: Potential for Significant Cost Savings
Bibliographic record
Abstract
BACKGROUND: Preanalytical errors account for most laboratory errors. Although the frequencies of preanalytical errors are well characterized in the literature, little is known regarding the costs of these errors to the laboratory. OBJECTIVE: To analyze costs associated with preanalytical errors associated with the international normalized ratio (INR) test. METHODS: We performed a retrospective analysis of INR requests associated with preanalytical error codes from January 2009 through September 2013. Preanalytical error types were those related to order entry (no specimen collected) and those unrelated to order entry (insufficient specimen quantity or specimen-integrity concerns). We calculated the cost of analysis of a specimen and the cost of investigating errors. RESULTS: During the study period, there were 557,411 INR requests, 13.1% of which were associated with a preanalytical error code. The total annual cost of INR testing was USD $379,222.50. Investigation and reporting of preanalytical errors not related to order entry represented 10.5% of our annual INR testing budget (USD $39,939.00). CONCLUSIONS: Minimizing preanalytical errors has the potential to result in significant cost savings.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".