• LYDUS Consortium Data Quality Management Guideline

    The LYDUS consortium has developed quality assessment guidelines that focus on the reliability of medical data. These guidelines focus on indicators that exclude qualitative factors and reflect the characteristics of medical data. Indicators for specific tasks are excluded, and the goal is to improve the diverse utilization of data. Finally, these guidelines are dedicated to automating the evaluation process by developing software.

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  • Medical Data Curation Full-Cycle Process Guidelines

    The Medical Data Curation Full-Cycle Process Guidelines consist of four volume series, covering comprehensive processes for medical data curation and quality management. These guidelines cover the full cycle from data collection, cleansing, and processing to utilization and evaluation, including quality management methods and the use of supporting software.

  • Volume 1: General Overview of Medical Data Curation Guidelines

    This volume presents a comprehensive direction for full-cycle curation and quality management of medical data. It addresses all stages including data collection, cleansing, and processing, as well as setting quality indicators, validation, and utilization. It outlines essential components for data management and quality improvement. The volume also includes guidance on efficient data management using quality management programs and QUIQ tables.

  • Volume 2: From Initiation to Utilization of Medical Data Curation

    This volume offers guidelines covering all phases of medical data curation, from initiation to utilization. It addresses early stages such as acquisition, collection, and cleansing, and progresses through processing, labeling, annotation, final use, and validation. It also provides quality management methods for each phase. Appendices include practical examples such as IRB research protocols and DRB review applications to assist with real-world medical data curation work.

  • Volume 3: Medical Data Quality Management Indicators

    This volume provides a variety of indicators and management strategies for assessing and maintaining the quality of medical data. It defines key quality indicators such as validity, completeness, accuracy, consistency, diversity, and informativeness, and describes methods for evaluating data based on these indicators. Concrete strategies for maintaining and improving medical data quality are also included.

  • Volume 4: Guidelines for Using Quality Management Programs

    This volume provides guidelines for using programs to manage medical data quality systematically. It includes details on the structure and application of the QUIQ and VIA tables, as well as quality indicators for both structured and unstructured data. Additionally, it contains information on Python packages required to operate the quality management software and recommended system specifications, supporting practical implementation of data quality management.

  • Medical Imaging Data Curation Guidelines

    This guideline provides standards for the collection, management, and analysis of medical imaging data. It covers the development of research plans using medical imaging data, preprocessing and analysis methodologies, and explains the roles and ethical considerations related to IRB and DRB. The guideline also includes detailed explanations of various quantitative indicators for assessing the quality of medical imaging data.

  • Standard Operating Procedures (SOP) for Medical Imaging Data Collection

    This SOP offers consistent instructions for the collection, storage, and management of medical imaging data. It is designed to help effectively and efficiently collect and manage medical imaging data. The document also describes essential procedures to ensure patient privacy, as well as the accuracy and security of the data.