Structural characteristics and policy differences of digital health management policies in China: A quantitative analysis based on the PMC index model
- 1School of Humanities and Arts, China University of Mining and Technology, Xuzhou, China
Abstract
Digital health governance increasingly relies on policy systems that coordinate data resources, information platforms, standards, service delivery, regulation, security, innovation, and implementation support. However, the structural composition and cross-policy heterogeneity of China’s national digital health policy system remain insufficiently quantified. This study develops a domain-specific Policy Modeling Consistency (PMC) framework to evaluate the structural coverage of national-level digital health management policies in China. Using a source-locked corpus covering 2016–2025, 42 eligible policies were identified, of which 39 with complete and auditable texts were included in the quantitative analysis. The framework comprises nine dimensions and 48 binary indicators, generating 1,872 policy-by-indicator coding decisions. An independently coded 11-policy subset yielded 96.78% agreement and a Cohen’s kappa of 0.914. The mean PMC score was 6.654 (SD = 1.456; median = 6.500; range = 3.467–9.000). Under the study’s structural-coverage thresholds, nine policies were classified as perfect, 11 as good, 16 as acceptable, and three as poor. Regulatory governance, standards and institutional arrangements, and data resources showed the highest average coverage, whereas data protection and industrial innovation showed the lowest coverage and the greatest cross-policy variation. Leave-one-policy-out and policy-family sensitivity analyses confirmed the stability of the principal structural pattern. These findings reveal a policy architecture with relatively extensive governance, institutional, and data-related provisions but more uneven protection- and innovation-related coverage. The proposed framework provides a reproducible basis for comparing policy-text structure while avoiding the interpretation of textual completeness as evidence of implementation quality or policy effectiveness.
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BibTeX
@article{lyu2026structural,
title = {{Structural characteristics and policy differences of digital health management policies in China: A quantitative analysis based on the PMC index model}},
author = {Tong Lyu},
journal = {PLOS One},
year = {2026},
month = nov,
doi = {10.1371/journal.pone.0362220},
url = {https://doi.org/10.1371/journal.pone.0362220},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Structural characteristics and policy differences of digital health management policies in China: A quantitative analysis based on the PMC index model
AU - Tong Lyu
PY - 2026
DA - 2026/11/04
JO - PLOS One
DO - 10.1371/journal.pone.0362220
UR - https://doi.org/10.1371/journal.pone.0362220
AB - Digital health governance increasingly relies on policy systems that coordinate data resources, information platforms, standards, service delivery, regulation, security, innovation, and implementation support. However, the structural composition and cross-policy heterogeneity of China’s national digital health policy system remain insufficiently quantified. This study develops a domain-specific Policy Modeling Consistency (PMC) framework to evaluate the structural coverage of national-level digital health management policies in China. Using a source-locked corpus covering 2016–2025, 42 eligible policies were identified, of which 39 with complete and auditable texts were included in the quantitative analysis. The framework comprises nine dimensions and 48 binary indicators, generating 1,872 policy-by-indicator coding decisions. An independently coded 11-policy subset yielded 96.78% agreement and a Cohen’s kappa of 0.914. The mean PMC score was 6.654 (SD = 1.456; median = 6.500; range = 3.467–9.000). Under the study’s structural-coverage thresholds, nine policies were classified as perfect, 11 as good, 16 as acceptable, and three as poor. Regulatory governance, standards and institutional arrangements, and data resources showed the highest average coverage, whereas data protection and industrial innovation showed the lowest coverage and the greatest cross-policy variation. Leave-one-policy-out and policy-family sensitivity analyses confirmed the stability of the principal structural pattern. These findings reveal a policy architecture with relatively extensive governance, institutional, and data-related provisions but more uneven protection- and innovation-related coverage. The proposed framework provides a reproducible basis for comparing policy-text structure while avoiding the interpretation of textual completeness as evidence of implementation quality or policy effectiveness.
SN - 1932-6203
ER -