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Record 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 on OpenAlexaff
Xin Wang, Stephen Birch, Weiming Zhu, Huifen Ma, Mark Embrett, Qingyue Meng

Bibliographic record

VenueBMC Health Services Research · 2016
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsMcMaster University
FundersCenters for Disease Control and PreventionPeking University
KeywordsHealth administrationHealth informaticsNursing researchHealth careMedicineIntegrated carePublic healthNursingService delivery frameworkService (business)Family medicineBusinessMarketingEconomic growth

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.272
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.005
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.053
GPT teacher head0.522
Teacher spread0.469 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations32
Published2016
Admission routes1
Has abstractyes

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