Pre-analytical specimen labelling and processing errors in the ICU
Bibliographic record
Abstract
Introduction: Specimen rejections have been associated with increased in-hospital stay and cost. The majority of errors occur in the pre-analytic phase. Specimen rejection can lead to high rate of recollection, delay in result availability, and high rate of test abandonment. These factors affect patient care and safety. Methods: This study conducted a retrospective review of Patient Safety Learning System (PSLS) reports for the intensive care unit (ICU) at The Ottawa Hospital General Campus (TOH) between 2010 and 2018, and a prospective review using interviews, surveys, and process mapping. Results: From 2098 PSLS reports, 52.6% were related to laboratory specimen collection and processing (pre-analytic phase). Specimen mislabelling with the wrong patient identifier accounted for 9.8% of pre-analytical error reports, while 16.4% of errors were due to non-sufficient quantity (NSQ) of specimen. 12.2 % of pre-analytical error reports involved cytology specimens. Conclusions: Pre-analytical errors are not only costly and resource draining, but may also place a burden on patients. Areas where errors were found include labels and requisitions stored in bedside cabinets, inconsistencies between specimen labels and requisitions, out-dated and difficult to access laboratory manuals, and non-sufficient quantity specimen collection. In the future we hope to start new initiatives to tackle these issues to improve patient safety and hospital efficiencies. This includes the development of a website for the laboratory manual, so that it is more easily accessible and user-friendly. With a new electronic medical record (EMR) system at TOH in 2019, we will explore the affects of pre-analytical processing of specimens.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".