RESUMO
According to the Ethical Review Measures for Life Sciences and Medical Research Involving Humans jointly issued by the National Health Commission, the Ministry of Education, the Ministry of Science and Technology and the State Administration of Traditional Chinese Medicine in 2023, to optimize the ethical review process and reduce the burden on clinical researchers, it is proposed that some eligible situations can be "exempted from ethical review". This is a breakthrough progress in China’s ethical review management measures that firstly aimed at "exemption from ethical review". This paper reviewed and sorted out the relevant situations about exemption from review at home and abroad, focused on analyzing and exploring the four situations of exemption from review, especially discussed and analyzed the understanding of anonymization and personal sensitive information in exemption from review, and proposed practical suggestions for the four situations. Based on the actual situation of ethical review work, this paper also explored the establishment of practical standards and processes for exemption from review, providing reference for other medical institutions to implement the exemption from ethical review process.
RESUMO
De-identification of personal health information is essential in order not to require written patient informed consent. Previous de-identification methods were proposed using natural language processing technology in order to remove the identifiers in clinical narrative text, although these methods only focused on narrative text written in English. In this study, we propose a regular expression-based de-identification method used to address bilingual clinical records written in Korean and English. To develop and validate regular expression rules, we obtained training and validation datasets composed of 6,039 clinical notes of 20 types and 5,000 notes of 33 types, respectively. Fifteen regular expression rules were constructed using the development dataset and those rules achieved 99.87% precision and 96.25% recall for the validation dataset. Our de-identification method successfully removed the identifiers in diverse types of bilingual clinical narrative texts. This method will thus assist physicians to more easily perform retrospective research.