journal of biomedical informatics
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Development of Continuous Validation Model on Standard Codes Mapping for Multi-Institutional Collaborative Data-Driven Medical Study

Author(s): Jinsang Park, Takanori Yamashita, Atsushi Takada, Taeko Hotta, Chinatsu Nojiri, Rieko Izukura, Yoshiaki Fujimura, Michio Kimura, Masaharu Nakayama, Kazuhiko Ohe, Takao Orii, Eizaburo Sueoka, Takahiro Suzuki, Hideto Yokoi, Dongchon Kang and Naoki Nakashima

Background: The Medical Information Database Network (MID-NET) is a national project that promotes effective safety measures for the active surveillance of drug safety assessments through pharmacoepidemiological methods, using real-world data in Japan. The MID-NET contains the data of approximately 5.05 million patients (as of December 2019) across 10 medical institutions, including 23 hospitals. One of the most important conditions for conducting pharmacoepidemiological research using multiple medical databases is to systematically verify of data standardization.

Objectives: To evaluate the effect of improving the accuracy of standard data quality control by the development of a validation model for standard code mapping in multiple medical information databases.

Methods: We established the standard code mapping validation center at one of the cooperating medical institutions of the MID-NET that could collect and manage information about the standard code interoperability. Additionally, we used the mapping table for the four standard codes, including the Japan Laboratory Test Standard Code, 10th Revision (JLAC10) code were collected from MID-NET cooperating institutions, and the accuracy of the mapping table was evaluated.

Results: The observed four standard codes mapping ratio between institutions varied from >2,000 to <100. Moreover, the accuracies of standard codes were not standardized. We used a centralized standard code mapping validation model to provide feedback for standardizing JLAC-10 for each institution and meaningful differences between institutions were improved. Conclusions: The developed model visualized information differences and improved the data quality between multiple medical institutions.

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