Abstract T P333: Impact of Tele-Stroke Network on Door to Needle Times and TPA Use in Rural and Urban Community Hospitals
Bibliographic record
Abstract
Background: This project describes one healthcare system’s journey to expand the outreach of the Primary Stroke Center. Premier Health (PH) is a hospital system based in Dayton Ohio that provides services for over 2,200 stroke patients annually. Premier Health consists of 5 community hospitals 3 of which are Joint Commission certified Primary Stroke Centers. The requirement for Stroke Specialized Physicians on-call 24 hours a day had become more difficult with expansion of services to respond to community needs. With a limited number of Stroke Physicians within system, it was not feasible for available Physician’s to cover the 50 mile radius. A Tele-Stroke Network was developed to provide lifesaving services as well as 24/7 coverage for stroke call. Program results include synergistic unity of best practices and improved patient outcomes with the majority of patients remaining in their community. Purpose: Implement a Tele-Stroke System to provide specialty coverage and favorable patient outcomes for a Primary Stroke Center that provides coverage for a large region in the Midwest. Methods: In 2013 a stroke telehealtlh Clinical Nurse Specialist role was added and became pivotal in facilitating the following outcomes: 1) Restructuring of the Stroke Alert Call Schedules across the system. 2) Streamlining Stroke Alert Process across the system and redesign of work flows 3) Development of standard system order sets to streamline care delivery. 4) Providing IT training to end users and physicians at five hospitals. Results: • 304 Tele-Stroke consults conducted since implementation. • 33% increase in the volume of patient’s receiving T-PA • Average of 20 minutes reduction in Door to Needle for 2 of the 5 hospitals • Post telemedicine implementation there was a reduction in transfers from spoke hospitals to hub. On average, 83 % of the PH Tele-Stroke patients were able to stay in their respective communities while receiving Primary Stroke Center Care via telemedicine. Conclusion: Telemedicine implementation with standardization of stroke alert processes and order sets, restructuring of physician scheduling and IT training for the Primary Stroke Team resulted in improved t-PA use, lower door to needle time and reduction in unnecessary transfers of patients.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".