Abstract TP383: Use of Plan-Do-Study-Act and Clinical Simulation Methodology to Develop a Mobile Prehospital Telestroke System
Bibliographic record
Abstract
Background: When early and accurate identification of stroke occurs in the field by Emergency Medical Service (EMS) providers, the chances of reperfusion increases dramatically. Telestroke has made significant strides over the years and hold promise for potential use in the prehospital setting. However, the introduction of such a novel technology within a healthcare system can be complex. Purpose: To describe the use of the combination of clinical simulation and Plan-Do-Study-Act (PDSA) methodology in the design of a mobile prehospital telestroke system. Methods: A multi-stakeholder team with representation from the Departments of Neurology and Emergency Medicine, Office of Telemedicine, Center of Human Simulation and Patient Safety, local EMS agency and a telemedicine developer collaborated to develop a mobile prehospital telestroke system over a 6 month time period. Modifications to telemedicine equipment, teleconferencing software and protocol were implemented based on the results of serial PDSA cycles and clinical simulation testing. We used data collected via direct observation notes, survey results and informal interviews during serial PDSA cycles and clinical simulation to test changes to improve our mobile prehospital telestroke system. Results: (see image of PDSA chart) Conclusion: The combined clinical simulation and PDSA model led to the identification of technical and operational barriers not considered in the original design of the mobile telemedicine platform and placement. Before implementation and financial investment in a mobile prehospital telestroke program, the use of combined clinical simulation and PDSA methodology can improve the quality and optimize the system use. Further PDSA and simulation cycles are needed to improve the design of our mobile system.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".