VideoCap: Enabling REDCap as a Tool for Crowdsourced Multimedia-Labeling Research

Objective: Machine learning systems that use video data require large, diverse, human-labeled datasets, but generating reliable annotations remains labor-intensive, difficult to scale, and often dependent on proprietary tools or small expert annotator pools. We present VideoCap, a secure and context-aware workflow that integrates REDCap with a Content Delivery Network (CDN), backend web service, and crowdsourcing platform to dynamically rotate embedded video segments within a single survey structure. Methods: VideoCap hosts segmented videos on a CDN with restricted downloads, stores segment metadata and session state for automated video selection, and generates REDCap survey URLs populated with embedded video parameters. We implemented the workflow through Amazon Mechanical Turk to annotate simulated patient-provider video segments using open-ended insights, structured scheme selections, and free-text responses. Results: We tested the workflow using 481 simulated patient-provider video segments over a 131-day deployment period. In the valid-only analytic subset, 358 unique video segments received 814 annotations, with an average of 2.27 annotations per segment. The workflow achieved an average annotation-time-to-video-duration ratio of 4.47:1, lower than contextual annotation-time estimates reported in prior multimedia annotation workflows. Conclusion: VideoCap provides a reproducible workflow for dynamic multimedia annotation using broadly accessible tools, demonstrating feasible survey delivery and efficient annotation.