Quantitative evaluation of the medicine innovation policy in China: based on the PMC-Index model.

INTRODUCTION

Medicine innovation is crucial in promoting the sustainable development of medicine undertakings, which has significant economic and social benefits. China is the main force in global medicine consumption, with a huge demand for innovative medicines. Thus, the Chinese government releases a series of policies aimed at providing scientific and reasonable guidance for medicine innovation. However, there is inadequate quantitative evaluation and comparison of various medicine innovation policies in the existing studies.

METHODS

This paper adopts the approach of text mining and the Policy Modeling Consistency Index (PMC-Index) model to construct an evaluation system and then quantitatively evaluates and compares the traditional Chinese medicine innovation policies (TCMIPs), the biological medicine innovation policies (BMIPs), and the multiple medicine innovation policies (MMIPs) in China.

RESULTS

The results indicate that: (1) The three types of drug innovation policies have similarities in content and goal through comparative analysis of high-frequency words, while they also have their own characteristics. (2) The average PMC-Index of 29 TCMIPs is 5.77, which has the highest policy bad rate (21%); the average PMC-Index of 12 BMIPs is 6.21, which has the highest policy good rate (92%); moreover, the average PMC-Index of 35 MMIPs is 6.06, which has the highest policy excellence rate (26%). (3) The BMIPs, MMIPs, and TCMIPs have similar scores on policy object, policy orientation, policy timeliness, policy evaluation, and policy accessibility, while they differ significantly mainly on policy nature, incentive method, policy function, policy issuing agency, and policy instrument.

DISCUSSION

This study contributes to a comprehensive understanding of medicine innovation policies in China, in order to provide theoretical support for future policy formulation and optimization in the medicine industry. Moreover, we expand the application scenarios of policy diffusion theory.