Effective interventions to improve the quality of critically high point-of-care glucose meter results
Bibliographic record
Abstract
OBJECTIVES: Point-of-care testing (POCT) is testing performed outside the traditional laboratory, often at the patient bedside. In hospital settings, blood glucose is the most common POCT. Staff performing POCT are not usually laboratory trained; they are clinical staff with a primary focus on treating patients. Clinical staff find POCT quality assurance (QA) practices burdensome and are often non-compliant. In hospitals within EORLA (Eastern Ontario Regional Laboratories Association), all critically high POCT glucose results must be repeated prior to acting, according to policy. Compliance with this policy is audited regularly. DESIGN: and methods: All POCT glucose tests performed in participating sites between January and June 2018 and June and December 2019 were audited for compliance with the critical repeat policy. The discordant repeat rate was also determined for each audit period. Between January and May 2019, there were interventions aimed at improving compliance with the repeat policy. RESULTS: Compliance with the critical repeat policy increased from 30 to 57% in 2019 compared to 2018, following nursing education and implementation of notifications on the glucose meters themselves. The rate of discordant repeat results (>20% different from initial) also improved at most sites in 2019 compared to 2018. Nurses cited insufficient cleaning of patient hands prior to initial testing as the primary reason for discordant repeats. CONCLUSIONS: Operator compliance with POCT QA policies is an ongoing challenge requiring continual audit, feedback and education. A strong POCT multi-disciplinary committee with supports from senior and clinical leadership in an organization are key to improving compliance.
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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 | 0.020 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".