Impact of a web-based clinical decision-support system on pulmonary embolism diagnoses
Bibliographic record
Abstract
IntroductionPulmonary embolism (PE) is a disease that offers a diagnostic challenge for physicians. Literature suggests a gap remains between PE diagnostic guidelines and adherence to such guidelines in practice. While computerized decision support systems (CDSS) for PE exist, evidence is lacking on their impact in clinical settings. Objectives and ApproachThe objective is to evaluate the impact of a web-based PE-CDSS on physician adherence to diagnostic guidelines by collecting and linking chart review data, hospital administrative data, and PE-CDSS usage data from six months before and after the CDSS is deployed. This CDSS was integrated into an electronic medical record system and deployed at two inpatient hospital sites in early 2018. Pre- and post-intervention workups are assigned a score based on their adherence to PE diagnostic guidelines, then compared. Data from a third hospital site with no access to the PE-CDSS will be used as a control. ResultsPreliminary results will be available by mid-2018. Based on previous research, the investigators expect to see increased provider adherence to diagnostic guidelines for PE in settings where the PE-CDSS was deployed. Conclusion/ImplicationsImplementing a PE-CDSS may increase provider adherence to evidence-based diagnostic guidelines by providing supportive information about PE diagnosis and addressing uncertainties about clinical decision making. This could result in greater diagnostic accuracy for PE and improved outcomes for patients with suspected PE.
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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 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".