Influence of educational, audit and feedback, system based, and incentive and penalty interventions to reduce laboratory test utilization: a systematic review
Bibliographic record
Abstract
Laboratory and radiographic tests are often ordered unnecessarily. This excess testing has financial costs and is a burden on patients. We performed a systematic review to determine the effectiveness interventions to reduce test utilization by physicians. The MEDLINE and EMBASE databases were searched for the years 1946 through to September 2013 for English articles that had themes of test utilization and cost containment or optimization. Bibliographies of included papers were scanned to identify other potentially relevant studies. Our search resulted in 3236 articles of which 109 met the inclusion criteria of having an intervention aimed at reducing test utilization with results that could be expressed as a percent reduction in test use relative to the comparator. Each intervention was categorized into one or more non-exclusive category of education, audit and feedback, system based, or incentive or penalty. A rating of study quality was also performed. The percent reductions in test use ranged from a 99.7% reduction to a 27.7% increase in test use. Each category of intervention was effective in reducing test utilization. Heterogeneity between interventions, poor study quality, and limited time horizons makes generalizations difficult and calls into question the validity of results. Very few studies measure any patient safety or quality of care outcomes affected by reduced test use. There are numerous studies that use low investment strategies to reduce test utilization with one time changes in the ordering system. These low investment strategies are the most promising for achievable and durable reductions in inappropriate test use.
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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 | 0.017 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".