Effect of a Controlled Feedback Intervention on Laboratory Test Ordering by Community Physicians
Bibliographic record
Abstract
BACKGROUND: Most studies of interventions to reduce laboratory test utilization have occurred in academic hospital settings, used historical controls, or have had short post intervention follow-up. Interventions with the greatest impact use multiple approaches, are repeated regularly, include comparisons with physician peers, and have a personal approach. We determined whether laboratory test utilization by community physicians could be reduced by a multifaceted program of education and feedback. METHODS: We identified 200 physicians who ordered the largest number of common laboratory tests during 1 year in a nonhospital, commercial community (reference) laboratory. They were assigned to intervention and control groups (100 each). Intervention physicians were visited individually up to three times by laboratory representatives over a 2-year period. At each visit, educational material and the physician's personal laboratory test utilization data were presented and discussed briefly in general terms, with the latter compared with utilization data for the physician's peers. Overall test utilization rates 1 year before, during, and 2 years after the intervention were measured using population-based databases. Time-series analysis was used to determine the effect of the intervention on laboratory test utilization. RESULTS: The two groups began with similar test utilization: control group, 4.06 x 10(6) tests in 1.48 x 10(6) visits (2.73 tests/visit); intervention group, 3.90 x 10(6) tests in 1.41 x 10(6) visits (2.77 tests/visit). During the 2-year intervention, intention-to-treat analysis showed that utilization decreased significantly in the intervention group compared with the controls [relative reduction of 7.9% (P <0.0001); absolute reduction of 0.22 tests/visit (95% confidence interval, 0.20-0.24)]. This difference persisted until the end of study observation, or more than 2 years after the intervention ended. CONCLUSION: A multifaceted education and feedback strategy can significantly and persistently decrease laboratory utilization by practicing community physicians.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".