MedVidDeID: Protecting privacy in clinical encounter video recordings

Sriharsha Mopidevi¹, Kuk Jin Jang², Basam Alasaly³, Sydney Pugh⁴, Jean Park⁵, Ashley Batugo⁶, Sy Hwang⁷, Eric Eaton⁸, Danielle Lee Mowery⁹, Kevin B Johnson¹⁰

Affiliations

¹ Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, 19104, PA, USA. Electronic address: sriharsha.mopidevi@pennmedicine.upenn.edu.
² Department of Computer Engineering, Hongik University, Seoul, 04066, Republic of Korea. Electronic address: jangkj@hongik.ac.kr.
³ Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, 19104, PA, USA. Electronic address: basam.alasaly@pennmedicine.upenn.edu.
⁴ Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, 19104, PA, USA. Electronic address: sydney.pugh@pennmedicine.upenn.edu.
⁵ Department of Computer and Information Science, University of Pennsylvania, Levine Hall, 3330 Walnut St, Philadelphia, 19104, PA, USA. Electronic address: hlpark@seas.upenn.edu.
⁶ Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, 19104, PA, USA. Electronic address: ashley.batugo@pennmedicine.upenn.edu.
⁷ Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, 19104, PA, USA. Electronic address: sy.hwang@pennmedicine.upenn.edu.
⁸ Department of Computer and Information Science, University of Pennsylvania, Levine Hall, 3330 Walnut St, Philadelphia, 19104, PA, USA. Electronic address: eeaton@seas.upenn.edu.
⁹ Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, 19104, PA, USA. Electronic address: dlmowery@pennmedicine.upenn.edu.
¹⁰ Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, 19104, PA, USA; Department of Computer and Information Science, University of Pennsylvania, Levine Hall, 3330 Walnut St, Philadelphia, 19104, PA, USA. Electronic address: kevin.johnson1@pennmedicine.upenn.edu.

PMID: 40886813
DOI: 10.1016/j.jbi.2025.104901

Free article

MedVidDeID: Protecting privacy in clinical encounter video recordings

Sriharsha Mopidevi et al. J Biomed Inform. 2025.

Free article

. 2025 Aug 29:170:104901.

doi: 10.1016/j.jbi.2025.104901. Online ahead of print.

Authors

Sriharsha Mopidevi¹, Kuk Jin Jang², Basam Alasaly³, Sydney Pugh⁴, Jean Park⁵, Ashley Batugo⁶, Sy Hwang⁷, Eric Eaton⁸, Danielle Lee Mowery⁹, Kevin B Johnson¹⁰

Affiliations

¹ Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, 19104, PA, USA. Electronic address: sriharsha.mopidevi@pennmedicine.upenn.edu.
² Department of Computer Engineering, Hongik University, Seoul, 04066, Republic of Korea. Electronic address: jangkj@hongik.ac.kr.
³ Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, 19104, PA, USA. Electronic address: basam.alasaly@pennmedicine.upenn.edu.
⁴ Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, 19104, PA, USA. Electronic address: sydney.pugh@pennmedicine.upenn.edu.
⁵ Department of Computer and Information Science, University of Pennsylvania, Levine Hall, 3330 Walnut St, Philadelphia, 19104, PA, USA. Electronic address: hlpark@seas.upenn.edu.
⁶ Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, 19104, PA, USA. Electronic address: ashley.batugo@pennmedicine.upenn.edu.
⁷ Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, 19104, PA, USA. Electronic address: sy.hwang@pennmedicine.upenn.edu.
⁸ Department of Computer and Information Science, University of Pennsylvania, Levine Hall, 3330 Walnut St, Philadelphia, 19104, PA, USA. Electronic address: eeaton@seas.upenn.edu.
⁹ Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, 19104, PA, USA. Electronic address: dlmowery@pennmedicine.upenn.edu.
¹⁰ Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, 19104, PA, USA; Department of Computer and Information Science, University of Pennsylvania, Levine Hall, 3330 Walnut St, Philadelphia, 19104, PA, USA. Electronic address: kevin.johnson1@pennmedicine.upenn.edu.

PMID: 40886813
DOI: 10.1016/j.jbi.2025.104901

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.

Keywords: Audio-video; De-identification; Healthcare; Privacy; Protected health information.

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Conflict of interest statement

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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MedVidDeID: Protecting privacy in clinical encounter video recordings

Affiliations

MedVidDeID: Protecting privacy in clinical encounter video recordings

Authors

Affiliations

Abstract

Conflict of interest statement

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