Abstract 3536: Expanding Telestroke Partnerships: Building A Quality Assessment And Improvement Program To Support A Stroke System Of Care
Bibliographic record
Abstract
Throughout the U.S., many hospitals lack infrastructure and resources to provide acute stroke care rescue therapy. In a Washington state review (2008), it was determined that of hospitals which admit stroke patients only 50.7% had a neurologist available and 18.3% had a stroke team. Telestroke, although an efficient and cost effective way to bring neurological expertise to patients, has not been traditionally implemented to address other components of a system of care, such as transfer decisions and inpatient care requirements. A partnership (philanthropically supported) was created with the goal of supporting the expansion of traditional telestroke relationships commensurate with system of stroke care requirements. This consortium was created in Washington state amongst a tertiary stroke center and four centers who receive Telestroke services. This expanded relationship built upon basic telemedicine technology and training infrastructure and added in clinical training for inpatient unit staff, and development and implementation of a collaborative dashboard with quality metrics benchmarked against Joint Commission Primary Stroke Centers. Implementation strategies comprised partnering agreements, deployment of teams responsible for IT, training, and administrative tasks. Quality was addressed during monthly meetings and quarterly workshops. Comprehensive training programs were administered and tailored to meet specific needs assessment of each site with additional training offered via live streaming and archived series designed to address systems of stroke care. Communities, organizations and patients have benefited by the creation of a system which promotes local provision of care and the early development of a system of stroke care. Outcomes have included assured access to specialists when needed, improved patient selection for thrombolysis with some sites witnessing an increase in the number of patients who were appropriately treated with rtPA. In addition to rtPA usage and availability, patients have benefited from access to tertiary services such as advanced imaging, care consultations with vascular neurologists, and interventional services. All organizations have benefited from the collaboration resulting in local implementation of shared best practices. The program has been useful as a model for the establishment of Stroke Systems of Care in Washington. This implementation program may be a model for developing patient centered stroke systems of care recognizing that care needs extend beyond the IV thrombolytic process.
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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 | 0.033 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".