MedVidDeID: Protecting privacy in clinical encounter video recordings

IF 4.5 2区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Sriharsha Mopidevi , Kuk Jin Jang , Basam Alasaly , Sydney Pugh , Jean Park , Ashley Batugo , Sy Hwang , Eric Eaton , Danielle Lee Mowery , Kevin B. Johnson
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引用次数: 0

Abstract

Objective:

The increasing use of audio-video (AV) data in healthcare has improved patient care, clinical training, and medical and ethnographic research. However, it has also introduced major challenges in preserving patient-provider privacy due to Protected Health Information (PHI) in such data. Traditional de-identification methods are inadequate for AV data, which can reveal identifiable information such as faces, voices, and environmental details. Our goal was to create a pipeline for de-identifying AV healthcare data that minimized the human effort required to guarantee successful de-identification.

Methods:

We combined open-source tools with novel methods and infrastructure into a six-stage pipeline: (1) transcript extraction using WhisperX, (2) transcript de-identification with an adapted PHIlter, (3) audio de-identification through scrubbing, (4) video de-identification using YOLOv11 for pose detection and blurring, (5) recombining de-identified audio and video, and (6) validation and correction via manual quality control (QC). We developed two de-identification strategies to support different tolerances for lossy video images. We evaluated this pipeline using 10 h of simulated clinical AV recordings, comprising nearly 1.1 million video frames and approximately 72,000 words.

Results:

In Precision Privacy Preservation (PPP) mode, MedVidDeId achieved a success rate of 50%, while in Greedy Privacy Preservation (GPP) mode, it achieved a 97.5% success rate. Compared to manual methods for a 15 min video segment, the pipeline reduced de-identification time by 26.7% in PPP and 64.2% in GPP modes.

Conclusion:

The MedVidDeID pipeline offers a viable, efficient hybrid solution for handling AV healthcare data and privacy preservation. Future work will focus on reducing upstream errors at each stage and minimizing the role of the human in the loop.

Abstract Image

MedVidDeID:在临床遭遇视频记录中保护隐私。
目的:在医疗保健中越来越多地使用视听(AV)数据,改善了患者护理、临床培训以及医学和人种学研究。然而,由于此类数据中的受保护健康信息(PHI),它也在保护患者-提供者隐私方面带来了重大挑战。传统的去识别方法不适用于自动驾驶数据,因为自动驾驶数据可以显示人脸、声音和环境细节等可识别信息。我们的目标是创建一个去识别AV医疗保健数据的管道,以最大限度地减少保证成功去识别所需的人力。方法:我们将开源工具与新方法和基础设施结合起来,形成了一个六阶段的流水线:(1)使用WhisperX提取文本,(2)使用改编PHIlter进行文本去识别,(3)通过擦除进行音频去识别,(4)使用YOLOv11进行姿态检测和模糊处理进行视频去识别,(5)重新组合去识别的音频和视频,以及(6)通过人工质量控制(QC)进行验证和纠正。我们开发了两种去识别策略来支持有损视频图像的不同容忍度。我们使用10小时的模拟临床AV记录来评估这个管道,包括近110万视频帧和大约72,000个单词。结果:MedVidDeId在精准隐私保护(PPP)模式下的成功率为50%,在贪婪隐私保护(GPP)模式下的成功率为97.5%。与手动方法相比,对于15分钟的视频片段,管道在PPP模式下减少了26.7%的去识别时间,在GPP模式下减少了64.2%。结论:MedVidDeID管道为处理AV医疗数据和隐私保护提供了一种可行、高效的混合解决方案。未来的工作将侧重于减少每个阶段的上游错误,并最大限度地减少人在循环中的作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Biomedical Informatics
Journal of Biomedical Informatics 医学-计算机:跨学科应用
CiteScore
8.90
自引率
6.70%
发文量
243
审稿时长
32 days
期刊介绍: The Journal of Biomedical Informatics reflects a commitment to high-quality original research papers, reviews, and commentaries in the area of biomedical informatics methodology. Although we publish articles motivated by applications in the biomedical sciences (for example, clinical medicine, health care, population health, and translational bioinformatics), the journal emphasizes reports of new methodologies and techniques that have general applicability and that form the basis for the evolving science of biomedical informatics. Articles on medical devices; evaluations of implemented systems (including clinical trials of information technologies); or papers that provide insight into a biological process, a specific disease, or treatment options would generally be more suitable for publication in other venues. Papers on applications of signal processing and image analysis are often more suitable for biomedical engineering journals or other informatics journals, although we do publish papers that emphasize the information management and knowledge representation/modeling issues that arise in the storage and use of biological signals and images. System descriptions are welcome if they illustrate and substantiate the underlying methodology that is the principal focus of the report and an effort is made to address the generalizability and/or range of application of that methodology. Note also that, given the international nature of JBI, papers that deal with specific languages other than English, or with country-specific health systems or approaches, are acceptable for JBI only if they offer generalizable lessons that are relevant to the broad JBI readership, regardless of their country, language, culture, or health system.
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