Abstract 267: Improving Inpatient Stroke Care by Implementing Stroke Units Across Health Systems Using an Improvement Collaborative Approach
Bibliographic record
Abstract
Background: Stroke units, defined as a geographic location where stroke patients are cared for by an interdisciplinary team, hold the strongest evidence in reduced mortality and disability for stroke patients. However, according to the 2011 Canadian Stroke Network’s National Stroke Audit, only 23% of stroke patients in Canada were admitted to a Stroke Unit with the Canadian province of British Columbia (BC) lagging at only 4%. The objective of this quality improvement initiative was to increase the number of stroke units and to improve existing stroke units; additionally, we aimed to improve adherence to best practice acute stroke care. Methods: Using the Institute for Healthcare Improvement’s Breakthrough Series Collaborative methodology, a stroke unit Improvement Collaborative was run from January 2013 to December 2013 by Stroke Services BC, a program of the Provincial Health Services Authority in BC. Faculty members were recruited from BC and the Calgary Stroke Program in the province of Alberta. The collaborative had 4 Learning Sessions, a closing workshop, and bi-weekly webinars. Teams followed a structured 7-step framework: understanding current volumes; securing space; establishing the team; ensuring clinical best practice; creating processes for team communication; ensuring patient engagement; and establishing quality improvement mechanisms. Pre and post self-reports of care were collected through electronic polling at Learning Session 2 in February 2013 (pre, n=78) and at the Closing Celebration in December 2013 (post, n=66) using a 4-point Likert scale. There were 20 questions based on best practice. Results: Eleven teams enrolled representing 17 hospitals in BC and a hospital in Saskatoon in the province of Saskatchewan. Teams were either working at the hospital or health region level. There were a total of 75 new stroke beds created in BC, and 12 beds recommended for Saskatoon. Furthermore, the results from the e-voting on best practice showed statistically significant improvement in the following areas: admission to a stroke unit (p=0.005); assessment by an interdisciplinary team within 48 hours of admission (p=0.002); use of standardized valid tools (p=0.002); swallowing screen within 24 hours (p<0.001); core interprofessional team on the stroke unit (p<0.001); care to prevent secondary complication (p<0.001); management of serum lipid levels (p=0.017); patient education (p<0.001); and team education (p=0.02). Conclusions: This inter-provincial Quality Improvement Collaborative was successful in implementing and improving stroke units, and in improving best practice care of inpatient stroke patients. Critical success factors include the engagement of faculty from high-performing centers even if they exist outside the jurisdiction where improvement is sought, and the use of the 7-step framework for implementing stroke units.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".