Journal article
Comparative analysis of personal protective equipment nonadherence detection: computer vision versus human observers
Journal of the American Medical Informatics Association : JAMIA, v 32(1), pp 163-171
14 Oct 2024
PMID: 39401253
Abstract
Human monitoring of personal protective equipment (PPE) adherence among healthcare providers has several limitations, including the need for additional personnel during staff shortages and decreased vigilance during prolonged tasks. To address these challenges, we developed an automated computer vision system for monitoring PPE adherence in healthcare settings. We assessed the system performance against human observers detecting nonadherence in a video surveillance experiment.
The automated system was trained to detect 15 classes of eyewear, masks, gloves, and gowns using an object detector and tracker. To assess how the system performs compared to human observers in detecting nonadherence, we designed a video surveillance experiment under 2 conditions: variations in video durations (20, 40, and 60 seconds) and the number of individuals in the videos (3 versus 6). Twelve nurses participated as human observers. Performance was assessed based on the number of detections of nonadherence.
Human observers detected fewer instances of nonadherence than the system (parameter estimate -0.3, 95% CI -0.4 to -0.2, P < .001). Human observers detected more nonadherence during longer video durations (parameter estimate 0.7, 95% CI 0.4-1.0, P < .001). The system achieved a sensitivity of 0.86, specificity of 1, and Matthew's correlation coefficient of 0.82 for detecting PPE nonadherence.
An automated system simultaneously tracks multiple objects and individuals. The system performance is also independent of observation duration, an improvement over human monitoring.
The automated system presents a potential solution for scalable monitoring of hospital-wide infection control practices and improving PPE usage in healthcare settings.
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Details
- Title
- Comparative analysis of personal protective equipment nonadherence detection: computer vision versus human observers
- Creators
- Mary S Kim - Children's NationalBeomseok Park - Rutgers, The State University of New JerseyGenevieve J Sippel - Children's NationalAaron H Mun - Children's NationalWanzhao Yang - Rutgers, The State University of New JerseyKathleen H McCarthy - Children's NationalEmely Fernandez - Division of Trauma and Burn Surgery, Children's National Hospital, Washington, DC 20010, United StatesMarius George Linguraru - Children's NationalAleksandra Sarcevic - Drexel UniversityIvan Marsic - Rutgers, The State University of New JerseyRandall S Burd (Corresponding Author) - Children's National
- Publication Details
- Journal of the American Medical Informatics Association : JAMIA, v 32(1), pp 163-171
- Publisher
- Oxford University Press; OXFORD
- Number of pages
- 9
- Grant note
- R01LM011834 / National Institutes of Health Research Project Grant Program R56EB032819 / NIBIB NIH HHS
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:001332472200001
- Scopus ID
- 2-s2.0-85212991069
- Other Identifier
- 991021930064504721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Information Systems
- Computer Science, Interdisciplinary Applications
- Health Care Sciences & Services
- Information Science & Library Science
- Medical Informatics