Interventions to Reduce the Overuse of Imaging for Pulmonary Embolism: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Imaging use in the diagnostic workup of pulmonary embolism (PE) has increased markedly in the last 2 decades. Low PE prevalence and diagnostic yields suggest a significant problem of overuse. PURPOSE: The purpose of this systematic review is to summarize the evidence associated with the interventions aimed at reducing the overuse of imaging in the diagnostic workup of PE in the emergency department and hospital wards. DATA SOURCES: PubMed, MEDLINE, Embase, and EBM Reviews from 1998 to March 28, 2017. STUDY SELECTION: Experimental and observational studies were included. The types of interventions, their efficacy and safety, the impact on healthcare costs, the facilitators, and barriers to their implementation were assessed. DATA SYNTHESIS: Seventeen studies were included assessing clinical decision support (CDS), educational interventions, performance and feedback reports (PFRs), and institutional policy. CDS impact was most comprehensively documented. It was associated with a reduction in imaging use, ranging from 8.3% to 25.4%, and an increase in diagnostic yield, ranging from 3.4% to 4.4%. The combined implementation of a CDS and PFR resulted in a modest but significant increase in the adherence to guidelines. Few studies appraised the safety of interventions. There was a lack of evidence concerning economic aspects, facilitators, and barriers. CONCLUSIONS: A combined implementation of an electronic CDS and PFRs is more effective than purely educational or policy interventions, although evidence is limited. Future studies of high-methodological quality would strengthen the evidence concerning their efficacy, safety, facilitators, and barriers.
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.007 | 0.006 |
| 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) | 0.005 | 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".