What the Doctor Ordered: Improving the Use and Value of Laboratory Testing
Bibliographic record
Abstract
About 70 percent of medical decisions are based on the results of laboratory tests (Forsman 1996). If testing amounts to inappropriate over-utilization, it could lead to further unnecessary testing, inaccurate diagnosis and potentially inappropriate treatments that could be accompanied by adverse and unnecessary side-effects. A test may also be inappropriately underutilized – it should be ordered, but isn’t – which leads to delayed diagnosis and treatment and potential worsening of the patient’s condition. The importance of laboratory testing in diagnosis, in addition to its significant cost, makes it a primary target for quality improvement. Reducing inappropriate laboratory testing would have the dual benefits of making the health system as a whole more efficient and improving patient outcomes and experience. This Commentary investigates the use and cost of laboratory testing in Canada and finds variation across the country. To decrease the amount of unnecessary laboratory testing and the associated downstream medical costs, strategies must balance effectiveness with maintaining doctor and patient autonomy in choosing treatments. We propose a number of options for policymakers to reduce inappropriate laboratory testing: adjusting physician compensation to align incentives with improving appropriateness; utilization management via practice variation and feedback information; reforming requisition orders and care paths to more closely adhere to clinical guidelines; and development of provincial formularies for diagnostic testing.
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 | 0.013 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.038 | 0.027 |
| Insufficient payload (model declined to judge) | 0.004 | 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".