A67 REDUCING INAPPROPRIATE GAMMA-GLUTAMYL TRANSFERASE TESTING FOR INPATIENTS: A QUALITY IMPROVEMENT INITIATIVE IN LAB WASTE REDUCTION APPLYING THE MODEL FOR CONTINUOUS IMPROVEMENT
Bibliographic record
Abstract
Abstract Background Review of the literature identifies a rising trend in laboratory testing, with over 30% of tests estimated to be inappropriately repeated. Laboratory overutilization increases healthcare costs, and can lead to overdiagnosis, overtreatment and negative health outcomes. Indications for repeat Gamma Glutamyl Transferase (GGT) testing in adults are limited, particularly repeat testing within the same admission. Aims Our aim was to reduce the inappropriate ordering of repeat GGT testing by 25% for all inpatients at the London Health Sciences Centre (LHSC) over a one-year study period. Methods An interprofessional team was created to help engage relevant stakeholders, collect baseline data and reassess the indications for GGT testing. A combination of root cause analysis tools, specifically the Ishikawa diagram and Pareto chart, were employed to identify potential factors contributing to the overutilization of GGT testing. After prioritizing potential solutions, intervention bundles were developed, and Plan-Do-Study-Act (PDSA) cycles were created to target correctable factors. In PDSA cycle #1, the process started by eliminating GGT as a laboratory testing option in the three most commonly used admission order care sets. Considering the hierarchy of intervention effectiveness, PDSA cycle #2 involved implementing a computerized Clinical Decision Support (CDS) system to restrict the reordering of GGT tests within 72 hours of the same admission. Results Baseline data showed that in 2022, a total of 62,542 GGT tests were ordered, with an average of approximately 5,200 GGT tests ordered per month. Of these, 16.4% were ordered through the top 3 most prevalent admission order care sets, and around 25% of all GGT tests were repeats within 72 hours of admission. Referring to Figure 1, PDSA cycle #1 yielded no significant reduction in GGT testing. PDSA cycle #2 successfully reduced the proportion of repeat GGT tests ordered by 12% within two months of implementation, leading to an estimated annualized cost savings of approximately $37,440. Conclusions Our results establish the effectiveness of CDS systems in reducing laboratory testing overutilization, suggesting their superiority to individual care set targeting interventions, and emphasize the potential for cost-effective CDS development in contemporary healthcare. Funding Agencies None
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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 | 0.024 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".