Scaling an Evidence‐Based Community Health Worker Program With Fidelity: Results and Lessons Learned
Notice bibliographique
Résumé
Policy Points Effectively implemented community health worker (CHW) programs improve patient health outcomes and quality of care, reduce health care costs, and are a key strategy for addressing social and structural drivers of health. As policymakers consider funding mechanisms for CHW programs, it is crucial to tie funding to evidence-based best practices while also allowing for innovation and context-specific adaptations. CONTEXT: Community health worker (CHW) programs represent a key strategy for addressing social and structural drivers of health and have the potential to improve patient health outcomes and enhance quality of care while reducing health care costs. However, challenges such as high staff turnover, lack of program infrastructure, and inadequate CHW support and supervision can hinder implementation and sustainment of effective CHW programs. Furthermore, few CHW programs have been successfully scaled across multiple organizations and communities. Individualized Management for Person-Centered Targets (IMPaCT) is an evidence-based CHW model designed to address these challenges by standardizing processes for CHW hiring, training, support, and supervision while still allowing for context-specific adaptation and tailoring. In this dissemination and implementation project, we evaluated implementation of IMPaCT across five geographically and structurally distinct sites serving diverse and varied patient populations. METHODS: Model fidelity was assessed across seven best practice domains via structured virtual observations with CHWs, supervisors, and program directors at each implementation site. Acute care use was evaluated using difference-in-differences regression modeling for patients enrolled in IMPaCT compared with a propensity score-matched control group. All implementation sites examined total hospital days per patient, and several sites chose to incorporate additional measures of acute care use such as the number of hospitalizations and emergency department visits. FINDINGS: We found that core program components were implemented consistently across sites, and three of five sites were able to both sustain implementation over a three-year period and demonstrate significant reductions in acute care use, consistent with previous randomized controlled trials of this program. CONCLUSIONS: Health systems may be able to address social drivers of health and improve population health for patients who are low-income and patients of color by implementing evidence-based CHW programs with fidelity.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».