Population Health Innovations and Payment to Address Social Needs Among Patients and Communities With Diabetes
Notice bibliographique
Résumé
Policy Points Population health efforts to improve diabetes care and outcomes should identify social needs, support social needs referrals and coordination, and partner health care organizations with community social service agencies and resources. Current payment mechanisms for health care services do not adequately support critical up-front investments in infrastructure to address medical and social needs, nor provide sufficient incentives to make addressing social needs a priority. Alternative payment models and value-based payment should provide up-front funding for personnel and infrastructure to address social needs and should incentivize care that addresses social needs and outcomes sensitive to social risk. CONTEXT: Increasingly, health care organizations are implementing interventions to improve outcomes for patients with complex health and social needs, including diabetes, through cross-sector partnerships with nonmedical organizations. However, fee-for-service and many value-based payment systems constrain options to implement models of care that address social and medical needs in an integrated fashion. We present experiences of eight grantee organizations from the Bridging the Gap: Reducing Disparities in Diabetes Care initiative to improve diabetes outcomes by transforming primary care and addressing social needs within evolving payment models. METHODS: Analysis of eight grantees through site visits, technical assistance calls, grant applications, and publicly available data from US census data (2017) and from Health Resources and Services Administration Uniform Data System Resources data (2018). Organizations represent a range of payment models, health care settings, market factors, geographies, populations, and community resources. FINDINGS: Grantees are implementing strategies to address medical and social needs through augmented staffing models to support high-risk patients with diabetes (e.g., community health workers, behavioral health specialists), information technology innovations (e.g., software for social needs referrals), and system-wide protocols to identify high-risk populations with gaps in care. Sites identify and address social needs (e.g., food insecurity, housing), invest in human capital to support social needs referrals and coordination (e.g., embedding social service employees in clinics), and work with organizations to connect to community resources. Sites encounter challenges accessing flexible up-front funding to support infrastructure for interventions. Value-based payment mechanisms usually reward clinical performance metrics rather than measures of population health or social needs interventions. CONCLUSIONS: Federal, state, and private payers should support critical infrastructure to address social needs and incentivize care that addresses social needs and outcomes sensitive to social risk. Population health strategies that address medical and social needs for populations living with diabetes will need to be tailored to a range of health care organizations, geographies, populations, community partners, and market factors. Payment models should support and incentivize these strategies for sustainability.
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 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 | 0,000 | 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 | 0,003 | 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 ».