AUTOMATED DEIDENTIFICATION OF PATHOLOGY AND RADIOLOGY DATA

总体项目目标是优化和自动化放射学数据（DICOM）和病理学数据（全载玻片成像- WSI）中的PHI检测和编辑-包括标头元数据和“烧录”像素数据-从而最大限度地减少对人工在环审查/手动编辑的需求，并最大限度地提高去识别数据吞吐量。Impact Business Information Solutions，Inc（IBIS）为该项目的第二阶段制定了四个主要目标。第一，增强第一阶段开始的深度学习模型，针对真实世界的数据（而不是第一阶段的合成数据）执行算法，并逐步提高其成功率。IBIS还将通过更复杂的算法增强NLP在文本中检测潜在危险的能力。二、全面落实 人在回路工作流程。这将需要在EICON DEID中添加几个新模块，包括用户管理、访问控制和用于事件驱动通知的消息传递子系统。第三，使用不确定性量化算法开发去识别置信度的更准确的自动测量，该算法将用于以知情的精度确定启动人在环过程的触发点。第四，通过以下方式生产和验证EICON DEID解决方案： 重点关注系统性能、可扩展性、可用性和法规遵从性。

The overall project objective is to optimize and automate the detection and redaction of PHI in radiology data (DICOM) and pathology data (Whole Slide Imaging - WSI) – both header metadata and “burned in” pixel data – thereby minimizing the need for human-in-the-loop review/manual redaction and maximizing deidentified data throughput. Impact Business Information Solutions, Inc (IBIS) has established four major goals for Phase II of this project. One, to enhance the deep learning models begun in Phase I, executing the algorithms against real-world data (as opposed to synthetic data, in Phase I), and incrementally improving their success rates. IBIS will also enhance the NLP capability for PHI detection in text with a more sophisticated algorithm. Two, to put in place a comprehensive human-in-the-loop workflow. This will require the addition of several new modules to EICON DEID including User Management, Access Control, and a Messaging subsystem for event-driven notifications. Three, to develop a more accurate automated measurement of de-identification confidence using an uncertainty quantification algorithm, which will be used to determine with informed precision the trigger point for initiation of the human-in-the-loop process. And four, to productize and validate the EICON DEID solution with a strong focus on system performance, scalability, usability and regulatory compliance.