Protecting and Utilizing Health and Medical Big Data: Policy Perspectives from Korea / 대한의료정보학회지

OBJECTIVES: We analyzed Korea's data privacy regime in the context of protecting and utilizing health and medical big data and tried to draw policy implications from the analyses. METHODS: We conducted comparative analyses of the legal and regulatory environments governing health and medical big data with a view to drawing policy implications for Korea. The legal and regulatory regimes considered include the following: the European Union, the United Kingdom, France, the United States, and Japan. We reviewed relevant statutory materials as well as various non-statutory materials and guidelines issued by public authorities. Where available, we also examined policy measures implemented by government agencies. RESULTS: In this study, we investigated how various jurisdictions deal with legal and regulatory issues that may arise from the use of health and medical information with regard to the protection of data subjects' rights and the protection of personal information. We compared and analyzed various forms of legislation in various jurisdictions and also considered technical methods, such as de-identification. The main findings include the following: there is a need to streamline the relationship between the general data privacy regime and the regulatory regime governing health and medical big data; the regulatory and institutional structure for data governance should be more clearly delineated; and regulation should encourage the development of suitable methodologies for the de-identification of data and, in doing so, a principle-based and risk-based approach should be taken. CONCLUSIONS: Following our comparative legal analyses, implications were drawn. The main conclusion is that the relationship between the legal requirements imposed for purposes of personal information protection and the regulatory requirements governing the use of health and medical data is complicated and multi-faceted and, as such, their relationship should be more clearly streamlined and delineated.

Comparison of Methods for Automatic De-identification of Clinical Texts / 医学信息学杂志

The paper introduces the common methods for automatic de-identification of clinical texts,including the method based on rules,machine learning method,comprehensive method,etc.,states the challenges for clinical texts practicability,system universality and scalability of clinical texts de-identification research,analyzes the further research direction,and provides reference for researchers of this field.

A De-identification Method for Bilingual Clinical Texts of Various Note Types

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.