PDF icon PDF herunterladen
Open Access
CC BY 4.0 · Indian Journal of Neurotrauma
DOI: 10.1055/s-0045-1809970
Original Article

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

Megha Gautam
1   Department of Neurosurgery, All India Institute of Medical Sciences, New Delhi, India
,
Tej P. Sinha
2   Department of Emergency Medicine, All India Institute of Medical Sciences, New Delhi, India
,
Sanjeev Bhoi
2   Department of Emergency Medicine, All India Institute of Medical Sciences, New Delhi, India
,
Deepak Agrawal
1   Department of Neurosurgery, All India Institute of Medical Sciences, New Delhi, India
› Institutsangaben
Funding None.
› Weitere Informationen
Auch verfügbar aufeRefMedOne
  Lizenzen und Reprints
Preview

Abstract

Background

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.

Objective

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.

Materials and Methods

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.

Results

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.

Conclusion

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.

Keywords

automated data - emergency - trauma - artificial intelligence - healthcare


Publikationsverlauf

Artikel online veröffentlicht:
27. Juni 2025

© 2025. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

Thieme Medical and Scientific Publishers Pvt. Ltd.
A-12, 2nd Floor, Sector 2, Noida-201301 UP, India