Feasibility of Automated Image-Based Data Capture for AI-Based Analytics of Trauma ED Workflow

Abstract Automating clinical order entry and data capture in electronic medical records (EMRs) can help ease the workload. Most healthcare facilities have electronic health data in Emergency Departments (EDs), but they still rely on manual documentation as well. Manual documentation is unable to track performance due to the lack of automated data. For physicians accustomed to paper, scanning completed forms offers the least disruptive transition. However, many are reluctant to adopt more advanced computerized physician order entry technologies, such as electronic forms on tablet PCs or voice recognition. This study aimed to determine the feasibility of implementing a near real-time, automated clinical data capture platform for neurotrauma patients within the trauma ED. A pilot study was conducted in a simulated ED environment. Internet of Things (IoT) scanners were used to capture images of ED notes of trauma patients. The accuracy of data extracted from these images using an artificial intelligence (AI)-powered optical character recognition (OCR) algorithm. The AI-powered OCR algorithm achieved excellent results for extracting the data of trauma patients from scanned ED notes with an accuracy of 97% for handwritten notes and 99.5% for typed data. All typed and handwritten notes could be processed into a structured dataset for further analytics. Automated image-based data capture using IoT scanners is a feasible solution for streamlining ED workflows, extracting KPI, and digitizing handwritten notes. This platform ensures data integrity and authenticity with the images serving as the “ground truth.” As there is negligible change in existing workflows, it is easy to implement and integrate. Further validation is, however, needed to assess large-scale implementation.