Challenges and promising solutions to engaging patients in healthcare implementation in the United States: an environmental scan
Notice bibliographique
Résumé
BACKGROUND: One practice in healthcare implementation is patient engagement in quality improvement and systems redesign. Implementers in healthcare systems include clinical leadership, middle managers, quality improvement personnel, and others facilitating changes or adoption of new interventions. Patients provide input into different aspects of health research. However, there is little attention to involve patients in implementing interventions, especially in the United States (U.S.), and this might be essential to reduce inequities. Implementers need clear strategies to overcome challenges, and might be able to learn from countries outside the U.S. METHODS: We wanted to understand existing work about how patients are being included in implementation activities in real world U.S. healthcare settings. We conducted an environmental scan of three data sources: webinars, published articles, and interviews with implementers who engaged patients in implementation activities in U.S. healthcare settings. We extracted, categorized, and triangulated from data sources the key activities, recurring challenges, and promising solutions using a coding template. RESULTS: We found 27 examples of patient engagement in U.S. healthcare implementation across four webinars, 11 published articles, and seven interviews, mostly arranging patient engagement through groups and arranging processes for patients that changed how engaged they were able to be. Participants rarely specified if they were engaging a population experiencing healthcare inequities. Participants described eight recurring challenges; the two most frequently identified were: (1) recruiting patients representative of those served in the healthcare system; and (2) ensuring processes for equitable communication among all. We matched recurring challenges to promising solutions, such as logistic solutions on how to arrange meetings to enhance engagement or training in inclusivity and power-sharing. CONCLUSION: We clarified how some U.S. implementers are engaging patients in healthcare implementation activities using less and more intensive engagement. It was unclear whether reducing inequities was a goal. Patient engagement in redesigning U.S. healthcare service delivery appears similar to or less intense than in countries with more robust infrastructure for this, such as Canada and the United Kingdom. Challenges were common across jurisdictions, including retaining patients in the design/delivery of implementation activities. Implementers in any region can learn from those in other places.
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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 | 0,022 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 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 ».