Developing and implementation of an acute stroke dashboard to meet reporting requirements for status as a tertiary care stroke center
Bibliographic record
Abstract
Background: Best-practice guidelines for acute stroke recommend a care pathway, despite limited evidence of impact on functional outcomes. Existing pathways lack elements directly targeting improvement in function; this content might better orient care towards functional outcomes. Objective: The aim was to develop and test the feasibility and impact of a patient-centered Acute Stroke Dashboard to track the functional recovery indicators of patients post-stroke. Methods: Three Knowledge Translation (KT) theories were used to inform development of the Dashboard. The methods involved a gap analysis of documentation practices for 240 historical patients. A model was built reflecting current and new outcome-focused content, tested prospectively on 25 patients. An electronic version was developed iteratively, implemented and tested prospectively. Results: For Phase A, documentation over three time periods, revealed consistent documentation of only three functional areas: bladder control, swallowing, and ability to eat independently. Capacity for independent mobility and activities of daily living were rarely documented except for walking capacity when the Stroke Unit was operational which increased from 18% to 59%. For Phase B, the content for the Dashboard was established from information discussed at weekly Stroke Rounds, the items were worded, and ordering and response options selected. Algorithms were created to calculate total scores from the functional recovery indicators. Twelve iterations were carried out over 3 months. For Phase C, during the deployment of the paper-based Dashboard, it was evident that this format could not be adopted by the Stroke Team as it was inflexible and resided on the medical chart duplicating existing required reporting. The Research Team had to complete almost all content. An electronic APP version was developed on mobile devices distributed to each of the nursing stations. The research team facilitated transfer of the electronic charting to the clinical team. For Step D, the APP version deployed by the Stroke Unit staff. Two Nurse Champions self-identified and took on the daily use of the APP Dashboard during daily huddles. Over a 6 month period, 20 iterations of the Dashboard occurred to cover emerging needs. Data from 117 patients revealed that nursing content, some 50 data fields, were completed in more than 90% of patients. Content for other team members was less often completed: PT (10 fields) range of completion ~23%; OT/SLP range 5% to 13%. Conclusions: This Dashboard provided better systematic data on function than routine charting. The Knowledge Translation process was effective to engage Nursing Managers and assistant Nurse Managers in the process of creating a viable electronic charting system to meet the requirements set out by the Ministry of Health in Quebec for designation as a Tertiary Stroke Center.
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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 | 0.154 | 0.209 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".