Paving the Way for Electronic Patient-Centered Measurement in Team-Based Primary Care: Integrated Knowledge Translation Approach (Preprint)
Notice bibliographique
Résumé
<sec> <title>BACKGROUND</title> Patient-centered measurement (PCM) aims to improve the overall quality of care through the collection and sharing of patient values, outcomes, and perspectives. However, the use of PCM in care team decisions remains limited. Integrated knowledge translation (IKT) offers a collaborative, adaptive approach to explore best practices for incorporating PCM into primary care practices by involving knowledge users, including patients and providers, in the exploratory process. </sec> <sec> <title>OBJECTIVE</title> This study aims to test the feasibility of using patient-generated data in team-based care; describe the use of these data for team-based mental health care; and summarize patient and provider care experiences with PCM. </sec> <sec> <title>METHODS</title> We conducted a multi-method exploratory study in a rural team-based primary care clinic using IKT to co-design, implement, and evaluate the use of PCM in team-based mental health care. Care pathways, workflows, and quality improvement activities were adjusted iteratively to improve integration efforts. Patient and provider experiences were evaluated using individual interviews relating to the use of PCM and patient portals in practice. All meeting notes, interview summaries, and emails were analyzed to create a narrative evaluation. </sec> <sec> <title>RESULTS</title> During co-design, a care workflow was developed to incorporate electronically collected patient-generated data from the patient portal into the electronic medical record, and customized educational tools and resources were added. During implementation, care pathways and patient workflows for PCM were developed. Patients found portal use easy, educational, and validating, but data entries were not used during care visits. Providers saw the portal as extra work, and the lack of portal and electronic medical record integration was a major barrier. The IKT approach was invaluable for addressing workflow changes and understanding the ongoing barriers to PCM use and quality improvement. </sec> <sec> <title>CONCLUSIONS</title> Although the culture toward using PCM is changing, the use of PCM during care has not been successful. Patients felt validated and supported through portal use and could be empowered to bring these data to their visits. Training, modeling, and adaptable PCM methods are required before PCM can be integrated into routine care. </sec>
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».