P020: Development and early experience with the Foothills Medical Center Pulmonary Embolism Response Team (PERT)
Bibliographic record
Abstract
Background: Pulmonary embolism (PE) is a common illness with significant mortality without appropriate treatment. Its disease severity is variable, difficult to prognosticate and triage of severe PE remains a patient safety concern. Some PE may benefit from invasive and advanced medical therapy, but these decisions require complex multi-disciplinary coordinated care. We have launched a multi-disciplinary rapid response team at the Foothills Medical Center Hospital (FMC) to assist prognostication, treatment, disposition planning, and followup for high-risk PE: The Pulmonary Embolism Response Team (PERT). Aim Statement: PERT has been implemented to improve patient-oriented outcomes however, as severe PE is infrequent, we initially target process measures. In the first year of PERT rollout, we aim for: 1) 100% of high risk PE be detected by emergency for PERT consult 2) PERT response be within 45 minutes of activation 3) PERT treatment and disposition be made within 1 hour of consult. 4) > 80% of patient dispositions match those informed by evidence-based risk stratification tools. Measures & Design: Through collaboration between emergency medicine, radiology, cardiac sciences, medical specialties and critical care, a collective evidence-based PE risk stratification/treatment pathway was developed. This has been disseminated to providers and embedding into electronic medical records (EMR) for computer assisted decision-making support. EMR data has been harmonized with standardized radiographic reporting for PE to cue reporting of high risk imaging findings. Standardized imaging and EMR prognostic factors flag high risk PE suggesting PERT activation. PERT standard operating procedures have been developed, including evidenced-based pathways for further therapy, advanced imaging, and subspecialized disposition planning. Clinical services meet quarterly, and review dashboard summary data on clinical adverse events, resource utilization, and time data of patient flow to revise PE care pathways. Evaluation/Results: PERT activations occur approximately 2 times weekly. Adherence to operating procedures is high. Feedback post implementation cites improved adherence to evidence-based practice, clearer communication, and faster patient disposition. Quantitative analysis of performance is limited by infrequency of cases. Discussion/Impact: Our project shows feasibility of a PERT service. Pre-implementation data is collected, and we are currently measuring these post. We suspect signal of improved patient-oriented outcomes will be detected with more cases.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".