Improved Appropriateness of Advanced Diagnostic Imaging After Implementation of Clinical Decision Support Mechanism
Bibliographic record
Abstract
The Protecting Access to Medicare Act (PAMA) mandates clinical decision support mechanism (CDSM) consultation for all advanced imaging. There are a growing number of studies examining the association of CDSM use with imaging appropriateness, but a paucity of multicenter data. This observational study evaluates the association between changes in advanced imaging appropriateness scores with increasing provider exposure to CDSM. Each provider's first 200 consecutive anonymized requisitions for advanced imaging (CT, MRI, ultrasound, nuclear medicine) using a single CDSM (CareSelect, Change Healthcare) between January 1, 2017 and December 31, 2019 were collected from 288 US institutions. Changes in imaging requisition proportions among four appropriateness categories ("usually appropriate" [green], "may be appropriate" [yellow], "usually not appropriate" [red], and unmapped [gray]) were evaluated in relation to the chronological order of the requisition for each provider and total provider exposure to CDSM using logistic regression fits and Wald tests. The number of providers and requisitions included was 244,158 and 7,345,437, respectively. For 10,123 providers with ≥ 200 requisitions (2,024,600 total requisitions), the fraction of green, yellow, and red requisitions among the last 10 requisitions changed by +3.0% (95% confidence interval +2.6% to +3.4%), -0.8% (95% CI -0.5% to -1.1%), and -3.0% (95% CI 3.3% to -2.7%) in comparison with the first 10, respectively. Providers with > 190 requisitions had 8.5% (95% CI 6.3% to 10.7%) more green requisitions, 2.3% (0.7% to 3.9%) fewer yellow requisitions, and 0.5% (95% CI -1.0% to 2.0%) fewer red (not statistically significant) requisitions relative to providers with ≤ 10 requisitions. Increasing provider exposure to CDSM is associated with improved appropriateness scores for advanced imaging requisitions.
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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 | 0.004 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".