Feasibility of a quality improvement project to increase adherence to evidence-based pulmonary embolism diagnosis in the emergency department
Bibliographic record
Abstract
Background Many evidence-based clinical decision tools are available for the diagnosis of pulmonary embolism (PE). However, these clinical decision tools have had suboptimal uptake in the everyday clinical practice in emergency departments (EDs), despite numerous implementation efforts. We aimed to test the feasibility of a multi-faceted intervention to implement an evidence-based PE diagnosis protocol. Methods We conducted an interrupted time series study in three EDs in Ontario, Canada. We enrolled consecutive adult patients accessing the ED with suspected PE from January 1, 2018, to February 28, 2020. Components of the intervention were as follows: clinical leadership endorsement, a new pathway for PE testing, physician education, personalized confidential physician feedback, and collection of patient outcome information. The intervention was implemented in November 2019. We identified six criteria for defining the feasibility outcome: successful implementation of the intervention in at least two of the three sites, capturing data on ≥ 80% of all CTPAs ordered in the EDs, timely access to electronic data, rapid manual data extraction with feedback preparation before the end of the month ≥ 80% of the time, and time required for manual data extraction and feedback preparation ≤ 2 days per week in total. Results The intervention was successfully implemented in two out of three sites. A total of 5094 and 899 patients were tested for PE in the period before and after the intervention, respectively. We captured data from 90% of CTPAs ordered in the EDs, and we accessed the required electronic data. The manual data extraction and individual emergency physician audit and feedback were consistently finalized before the end of each month. The time required for manual data extraction and feedback preparation was ≤ 2 days per week (14 h). Conclusions We proved the feasibility of implementing an evidence-based PE diagnosis protocol in two EDs. We were not successful implementing the protocol in the third ED.
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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 | 0.100 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".