Developing and implementation of an acute stroke dashboard to meet reporting requirements for status as a tertiary care stroke center
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,154 | 0,209 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».