Health Disparities in Hospital Readmissions in Rural vs Urban Populations in the United States: A Comprehensive Review of Factors and Reduction Strategies
Notice bibliographique
Résumé
Background: Health disparities between rural and urban populations in the United States significantly impact health outcomes, access to medical services, and overall care quality. These disparities are influenced by factors such as socioeconomic status, geographic isolation, availability of healthcare providers, and the prevalence of chronic health conditions. Hospital readmission rates serve as critical indicators of care quality and post-discharge management effectiveness. High readmission rates often highlight issues in patient care management, discharge planning, and follow-up care efficacy, necessitating targeted interventions to improve patient outcomes and reduce healthcare costs. Rural areas face unique challenges in addressing these issues due to limited resources and access barriers. Method: A comprehensive approach was employed to investigate health disparities between rural and urban populations in the United States, focusing on strategies to mitigate hospital readmission rates in rural areas. The literature review involved searching electronic databases, including PubMed, Google Scholar, and Scopus, with keywords like "rural health disparities," "urban-rural differences," "hospital readmission," and "healthcare interventions." Articles were selected based on their relevance to hospital readmissions and interventions targeting rural populations. Data extraction encompassed study characteristics, participant demographics, outcomes related to hospital readmissions, and intervention details such as telehealth and care coordination programs. National data from guides and reports were also included to capture broader trends and efforts at reducing readmissions. Results: The review revealed significant disparities in hospital readmission rates between rural and urban populations in the United States. Rural areas exhibited higher readmission rates due to limited healthcare access, higher prevalence of chronic conditions, and socioeconomic challenges. Strategies such as enhanced telehealth services, improved primary care access, and care coordination programs demonstrated potential in mitigating these disparities. Conclusion: Addressing health disparities between rural and urban populations requires a multifaceted approach. Effective strategies include expanding telehealth services, improving care coordination, and strengthening community-based healthcare resources. Policymakers should focus on addressing socioeconomic disparities and ensuring equitable distribution of funding and resources. Continuous evaluation of healthcare policies can provide insights into improving outcomes for rural populations. Future research should standardize methodologies, foster interdisciplinary collaborations, and incorporate qualitative insights to inform effective, equitable healthcare interventions and policies.
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 | 0,006 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,015 | 0,015 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».