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Enregistrement W2531895919 · doi:10.1186/s12913-016-1813-8

Coordination of care in the Chinese health care systems: a gap analysis of service delivery from a provider perspective

2016· article· en· W2531895919 sur OpenAlexaff
Xin Wang, Stephen Birch, Weiming Zhu, Huifen Ma, Mark Embrett, Qingyue Meng

Notice bibliographique

RevueBMC Health Services Research · 2016
Typearticle
Langueen
DomaineHealth Professions
ThématiqueInterprofessional Education and Collaboration
Établissements canadiensMcMaster University
Organismes subventionnairesCenters for Disease Control and PreventionPeking University
Mots-clésHealth administrationHealth informaticsNursing researchHealth careMedicineIntegrated carePublic healthNursingService delivery frameworkService (business)Family medicineBusinessMarketingEconomic growth

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Increases in health care utilization and costs, resulting from the rising prevalence of chronic conditions related to the aging population, is exacerbated by a high level of fragmentation that characterizes health care systems in China. There have been several pilot studies in China, aimed at system-level care coordination and its impact on the full integration of health care system, but little is known about their practical effects. Huangzhong County is one of the pilot study sites that introduced organizational integration (a dimension of integrated care) among health care institutions as a means to improve system-level care coordination. The purposes of this study are to examine the effect of organizational integration on system-level care coordination and to identify factors influencing care coordination and hence full integration of county health care systems in rural China. METHODS: We chose Huangzhong and Hualong counties in Qinghai province as study sites, with only Huangzhong having implemented organizational integration. A mixed methods approach was used based on (1) document analysis and expert consultation to develop Best Practice intervention packages; (2) doctor questionnaires, identifying care coordination from the perspective of service provision. We measured service provision with gap index, overlap index and over-provision index, by comparing observed performance with Best Practice; (3) semi-structured interviews with Chiefs of Medicine in each institution to identify barriers to system-level care coordination. RESULTS: Twenty-nine institutions (11 at county-level, 6 at township-level and 12 at village-level) were selected producing surveys with a total of 19 schizophrenia doctors, 23 diabetes doctors and 29 Chiefs of Medicine. There were more care discontinuities for both diabetes and schizophrenia in Huangzhong than in Hualong. Overall, all three index scores (measuring service gaps, overlaps and over-provision) showed similar tendencies for the two conditions. The gap indices of schizophrenia (> 5.10) were bigger for diabetes (< 2.60) in both counties. The over-provision indices of schizophrenia (> 3.25) were bigger than diabetes (< 1.80) in both counties. Overlap indices for the two conditions exceeded justified overlaps, especially for diabetes. Gap index scores for schizophrenia interventions at the township-level and over-provision index scores for diabetes interventions at both village- and township-level showed big differences between the two counties. Insufficient medical staff with appropriate competencies, lack of motivation for care coordination and related supportive policies as well as unconnected information system were identified as barriers to system-level care coordination in both counties. CONCLUSION: Findings demonstrate that organizational integration in Huangzhong has not achieved a higher level of care coordination at this stage. System-level care coordination is most problematic at village-level institutions in Hualong, but at county-level institutions in Huangzhong. These findings suggest that attention be given to other aspects of integration (e.g., clinical and service integration) to promote system-level care coordination and contribute to the full integration of health care system in the pilot county.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,272
Score d'incertitude au seuil0,995

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,005
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,053
Tête enseignante GPT0,522
Écart entre enseignants0,469 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations32
Publié2016
Routes d'admission1
Résumé présentoui

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