How to develop a good patient information leaflet of a medicine

2016 Fiscal Year Final Research Report

• Project/Area Number: 26460604
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Research Field: Medical sociology
• Research Institution: Kagawa University
• Principal Investigator: Ikeda Masayuki 香川大学, 医学部, 客員研究員 (10242215)
• Project Period (FY): 2014-04-01 – 2017-03-31
• Keywords: 医薬品情報提供 / 添付文書 / 自然言語処理 / レギュラトリー・サイエンス / 後発品 / 文書解析

Outline of Final Research Achievements

We demonstrated that our NLP (Natural language processing) system, based on the Kachako platform, helped quantify consistency among SPCs and extracted inconsistencies between generic and original SPCs. Inconsistencies among SPCs for the same drug authorized by the same authority indicate that the existing regulatory scheme does not work effectively in achieving consistency across generic SPCs. However, NLP can address the challenge of checking large numbers of regulatory documents for consistency. Further research on rapidly comparing, correcting, and updating SPCs should contribute toward harmonization across generic SPCs and, ultimately, a centralized online drug information and safety resource.

Free Research Field

医薬品評価学（レギュラトリー・サイエンス）