RESUMEN
As the reliance on clinical epidemiological information from human specimens grows, so does the need for effective clinical information management systems, particularly for biobanks. Our study focuses on enhancing the Korea Biobank Network's (KBN) system with data quality verification features. By comparing the quality of data collected before and after these enhancements, we observed a notable improvement in data accuracy, with the error rate decreasing from 0.1198% to 0.0492%. This advancement underscores the importance of robust data quality management in supporting high-quality clinical research and sets a precedent for the development of clinical information management systems.
Asunto(s)
Bancos de Muestras Biológicas , Exactitud de los Datos , República de Corea , HumanosRESUMEN
Biobanks serve as vital repositories for human biospecimens and clinical data, promoting biomedical and clinical research. The integration of electronic health records particularly enhances research opportunities in the era of genomics and personalized medicine, improving understanding of tumor development and disease progression. Based on the Korea Biobank Network Common Data Model, it is possible to expand data collection across various diseases. We have developed an innovative big data platform designed to efficiently collect large-scale clinical information within the KBN. By implementing the system structure, data quality management processes, and basic statistical preprocessing functionalities, we have collected data from 136,473 individuals from 2021 to 2023, demonstrating the platform's continuous and efficient data collection capabilities. Integration with hospital systems and robust quality management ensure the acquisition of high-quality data.
Asunto(s)
Macrodatos , Bancos de Muestras Biológicas , Registros Electrónicos de Salud , República de Corea , HumanosRESUMEN
The amount of research on the gathering and handling of healthcare data keeps growing. To support multi-center research, numerous institutions have sought to create a common data model (CDM). However, data quality issues continue to be a major obstacle in the development of CDM. To address these limitations, a data quality assessment system was created based on the representative data model OMOP CDM v5.3.1. Additionally, 2,433 advanced evaluation rules were created and incorporated into the system by mapping the rules of existing OMOP CDM quality assessment systems. The data quality of six hospitals was verified using the developed system and an overall error rate of 0.197% was confirmed. Finally, we proposed a plan for high-quality data generation and the evaluation of multi-center CDM quality.
Asunto(s)
Exactitud de los Datos , Manejo de Datos , Instituciones de Salud , HospitalesRESUMEN
The amount of research on the gathering and handling of healthcare data keeps growing. To support multi-center research, numerous institutions have sought to create a common data model (CDM). However, data quality issues continue to be a major obstacle in the development of CDM. To address these limitations, a data quality assessment system was created based on the representative data model OMOP CDM v5.3.1. Additionally, 2,433 advanced evaluation rules were created and incorporated into the system by mapping the rules of existing OMOP CDM quality assessment systems. The data quality of six hospitals was verified using the developed system and an overall error rate of 0.197% was confirmed. Finally, we proposed a plan for high-quality data generation and the evaluation of multi-center CDM quality.