Effective stewardship strategies to enhance appropriateness of refer-out test requests in a Canadian tertiary centre laboratory
Bibliographic record
Abstract
OBJECTIVES: Specialized testing conducted in reference laboratories is costly and often not optimally directed. Since 2016, our institution has worked to ensure the appropriateness of refer-out (RO) tests. We examine the impact of utilization initiatives on the patterns of requests and completed tests. DESIGN AND METHODS: In 2016, 81 RO tests were selected for a more rigorous approval process. Physicians not pre-approved for testing received a prompt to consult with laboratory subject matter experts (SMEs) for further detail. After review, SMEs provided responses, approving or rejecting requests based on clinical relevance. Stewardship activities also included: repatriating tests locally, preferring Canadian over foreign institutions, unbundling tests, distributing educational memos, and introducing staged testing. We collected data on the number of requested (NoR) and number of completed (NoC) tests in 2015, before the implementation of the new vetting procedures, and for the post-implementation phase from 2016-2022. RESULTS: For 62 targeted RO tests (including trace metals, vitamins, antibodies, and endocrine-related tests), there was a 33% reduction in NoR and a 51% reduction in NoC in 2022 compared to 2015. The total savings for the study period based on NoC was $807,736. The NoC rate for Neuronal antibody tests decreased to 48.6% in 2022, with cost savings of $17,123, and an additional $50,000 saved by changing the testing site. Insourcing apolipoprotein B and fecal calprotectin tests resulted in cost savings of $3,380 and $3,371, respectively, in 2022. CONCLUSIONS: Automated messaging followed by a formal review of RO test requests is an effective utilization strategy that prevents redundant or clinically unjustified testing. This approach leads to significant economic savings and is expected to improve the efficiency of patient care.
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 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 | 0.023 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".