Author(s)

Xiu Luo ¹, Yibing Li ¹, Panpan Yang ¹

Affiliation(s)

¹ School of International Pharmaceutical Business, China Pharmaceutical University, Nanjing, China

Corresponding Author

Yibing Li

ABSTRACT

In order to provide reference for the subsequent policy development and system optimization, this paper design systematic evaluation of the existing Healthcare Big Data policy. The relevant policy documents issued at the central level are collected to analyse the inner logic of the main points, and the PMC model is constructed to evaluate the nine typical policies selected. Results show that all nine policies are at or above the acceptable level, and pass rate is 100%. In detail, all policies have significant advantages in policy areas, policy levels, and policy disclosure indicators, while there are common problems in indicators such as policy timeliness and incentive methods. The PMC indexes of the nine policy documents show a fluctuating downward trend, mainly due to the stage variability of policy points and policy target indicators. The PMC index scores of the highly directional and special policies are low. On this basis, suggestions for optimization of China's Healthcare Big Data policies are proposed.

KEYWORDS

Healthcare Big Data, Policy Evaluation, Internal Consistency, PMC Index Model

CITE THIS PAPER

Xiu Luo, Yibing Li, Panpan Yang, Research on the Quantitative Evaluation and Optimization Path of China's Healthcare Big Data Policy. Social Security and Administration Management (2023) Vol. 4: 18-24. DOI: http://dx.doi.org/10.23977/socsam.2023.040404.

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