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

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

Introduction

Emergency Departments (EDs) serve as critical points of care, managing a high volume of patients under intense time constraints. Efficient workflow management in EDs is essential to ensure timely and accurate diagnosis, treatment, and patient throughput.[1] However, documentation remains a significant challenge in ED settings, where clinical staff must balance patient care with meticulous record-keeping. While many healthcare facilities have transitioned to electronic medical records (EMRs), manual documentation continues to be a prevalent practice, particularly in trauma and neurotrauma cases where rapid decision-making is crucial.[2] The reliance on manual documentation often results in inefficiencies, data inconsistencies, and delayed access to critical patient information.

Automating clinical order entry and data capture has emerged as a potential solution to these challenges, promising to enhance workflow efficiency, reduce documentation burden, and improve data accuracy.[3] However, transitioning to advanced computerized physician order entry systems remains a challenge. Many healthcare professionals, particularly those accustomed to paper-based workflows, exhibit resistance to adopting digital systems such as tablet-based electronic forms or voice recognition technologies. A viable alternative is the use of image-based automated data capture, which offers a minimally disruptive transition by enabling physicians to continue using handwritten notes while ensuring structured data extraction through artificial intelligence (AI)-powered technologies.[4]

Automated image-based data capture leverages optical character recognition (OCR) algorithms, deep learning, and natural language processing (NLP) to convert handwritten clinical notes into structured digital records. This approach bridges the gap between traditional documentation practices and modern data-driven healthcare analytics, enabling near real-time integration of ED workflow data into EMRs. AI-driven OCR has shown promising results in extracting valuable insights from handwritten medical records, significantly enhancing the accessibility and usability of patient information.[5] Furthermore, integrating Internet of Things (IoT) scanners into ED environments facilitates the seamless capture of clinical documentation, reducing the dependency on manual data entry while preserving workflow efficiency.

The implementation of automated image-based data capture in ED settings aligns with the broader objective of AI-based analytics in healthcare. By structuring unstructured handwritten data, AI-powered systems can facilitate predictive analytics, operational efficiency, and real-time decision support. For trauma and neurotrauma cases, where timely intervention is critical, automated data capture ensures that clinicians have immediate access to comprehensive and organized patient information. Additionally, streamlining documentation processes contributes to improved compliance with clinical guidelines, enhanced resource allocation, and reduced administrative workload on ED personnel.[6] [7]

Despite its potential, the feasibility of automated image-based data capture for ED workflow analytics requires rigorous evaluation. Key considerations include the accuracy of AI-powered OCR algorithms, the adaptability of IoT scanning technologies in fast-paced clinical environments, and the integration of structured data into existing EMRs. The effectiveness of these technologies must be assessed in terms of data extraction accuracy, processing speed, and overall impact on ED workflow dynamics.[8]

This study aims to evaluate the feasibility of implementing a near real-time, AI-driven automated clinical data capture platform within the trauma ED. A pilot study was conducted in a simulated ED environment, wherein IoT scanners were employed to capture images of handwritten ED notes from neurotrauma patients. The extracted data were analyzed using an AI-based OCR algorithm to determine the accuracy and reliability of automated transcription into structured formats. By assessing the performance of this system, the study seeks to provide insights into its applicability in real-world ED settings and its potential to enhance clinical workflow efficiency. ([Fig. 1])

Fig. 1 Illustrative diagram of image processing using an IoT scanner and OCR software.

Materials and Methods

This study was conducted as a pilot project to evaluate the feasibility and effectiveness of automated image-based data capture for AI-driven analytics in ED workflow. The methodology consisted of the following key components:

Study Design and Setting

A simulated ED environment was set up to replicate near real-world emergency care conditions. The study focused on trauma and neurotrauma cases, where rapid decision-making and accurate documentation are critical. The workflow was designed to integrate automated data capture with minimal disruption to existing clinical processes.

Data Capture Using IoT Scanners

IoT-enabled document scanners were deployed to capture handwritten ED notes from neurotrauma patients. These scanners were positioned strategically within the ED to allow for seamless data acquisition without interfering with clinical activities. Each document was scanned for AI-based processing.

Image Preprocessing for Enhanced Accuracy

To ensure accurate text extraction, images undergo preprocessing steps including noise reduction using filters like OpenCV, contrast enhancement through histogram equalization, and binarization with adaptive thresholding for clear black-and-white text. Tools such as Tesseract OCR and Google Vision API are then used to detect and isolate text regions, improving recognition accuracy.

Optical Character Recognition and AI Processing

The scanned images were processed using an AI-powered OCR algorithm designed for medical handwriting recognition. The AI model was trained on a diverse dataset of clinical notes to enhance accuracy in extracting patient information, medical orders, and procedural details. The extracted text was then structured into a digital format suitable for integration with EMRs.

Error Correction and Data Validation

Raw text extraction may contain errors due to handwriting inconsistencies or image quality issues. To refine the output, NLP models are employed for the context-based text validation to identify anomalies in the extracted data and spelling and grammar correction. This stage confirms that the converted digital data are as accurate.

Results

The pilot study successfully demonstrated the feasibility of implementing an automated image-based data capture system in a simulated trauma ED environment. The AI-powered OCR algorithm effectively processed and extracted data from scanned handwritten ED notes of trauma patients.

Data Accuracy

The AI-powered OCR algorithm achieved a high level of accuracy in extracting structured clinical information. The comparison between the algorithm's output and manually transcribed ground-truth data revealed a significant concordance rate. Specifically, the algorithm demonstrated an accuracy of 97% for handwritten notes and 99.5% for typed data in recognizing and categorizing key clinical data points.

Processing Time

The automated data extraction process significantly reduced the time required for documentation compared with manual data entry. On average, the algorithm processed each scanned note within seconds, while manual data entry for the same information took approximately 10 to 15 minutes. This represents a substantial reduction in documentation time, allowing healthcare professionals to focus more on patient care.

Structured Data Output

The algorithm successfully converted unstructured handwritten notes into structured data, facilitating seamless integration with EMRs and AI-based analytics. The extracted data were accurately categorized into predefined fields, enabling efficient retrieval and analysis of patient information.

Discussion

This pilot study successfully demonstrated the feasibility of using an automated system to convert handwritten doctor's notes into digital data within a simulated emergency room setting. The results indicate that the AI-powered system, utilizing OCR, effectively and accurately extracted crucial patient information from scanned documents. This automation significantly reduced the time required for data entry compared with manual methods, suggesting a potential for alleviating the administrative burden on emergency physicians. By streamlining the documentation process, clinicians could dedicate more time to direct patient care, potentially leading to improved patient outcomes and reduced burnout.

Moreover, the high accuracy achieved by the system in converting handwritten notes into structured digital data highlights its reliability in providing timely and precise information, which is essential for informed clinical decision-making and patient safety.[9] The system's ability to integrate seamlessly with existing EMRs further enhances its utility, enabling real-time access to patient data and facilitating AI-driven analytics for improved clinical practices.[10] However, it is important to acknowledge that the simulated environment may not fully replicate the complexities of a real-world ED, including variations in handwriting, document quality, and clinical workflows. Therefore, further validation in actual clinical settings is necessary to assess the system's performance under diverse conditions. Future research should focus on large-scale implementation to evaluate the system's impact on patient outcomes, workflow efficiency, and healthcare costs, as well as on improving the algorithm's robustness to handle variations in handwriting and exploring the integration of additional AI technologies to enhance its capabilities.

Looking ahead, the advancement of fully automated data extraction systems holds transformative potential for healthcare documentation. Future iterations of such technology could eliminate the need for clinicians to manually input data altogether. Instead, patient information, whether from handwritten notes, prescription slips, or diagnostic reports, could be captured passively and in real-time through advanced OCR and NLP algorithms. In this envisioned model, any missing or unrecognized data would be flagged and stored securely within the patient's digital file for review, ensuring completeness while maintaining clinician oversight. Such automation would allow healthcare facilities to collect comprehensive datasets effortlessly, enabling seamless integration into electronic health records and supporting large-scale health analytics, predictive modeling, and real-time surveillance. Ultimately, this could revolutionize data-driven clinical decision-making, reduce administrative overhead, and enhance the overall efficiency and quality of patient care.

EDs across India have inconsistent quality of care, even within top hospitals, due to variations in training, facilities, and procedure adherence. This is compounded by unreliable, manual methods of tracking performance, which prevent accurate comparisons and improvement. Traditional performance metrics may not account for patients India's healthcare system struggles to provide consistent and equitable emergency care due to significant variations in service quality across institutions. EDs, even within premier hospitals, face problems, leading to inconsistent patient outcomes. A major challenge is the lack of a standardized, objective system for measuring performance, with current manual data collection methods being fragmented, biased, and unreliable. The absence of standardized key performance indicators, inadequate risk adjustment for patient severity, and limited transparency hinder accurate benchmarking and data-driven decision-making. Additionally, manual tracking methods are not scalable, restricting quality improvement initiatives, particularly in resource-limited LMIC settings. This results in suboptimal patient outcomes and inequities in emergency care access, requiring an urgent need for an AI-driven solution to provide standardized, high-quality emergency services.

The limitations of manual data acquisition and inadequate risk-adjustment models impede the generation of precise output. Current manual data metrics also fail to account for patient complexity and resource limitations, hindering fair comparisons. Fragmented, non-scalable benchmarking prevents data-driven improvements, a critical issue in resource-constrained LMICs. Consequently, suboptimal patient outcomes persist. This study is based on the implementation of an automated data extraction, developing comprehensive risk-adjustment models, and improving the outcome and patient care analysis, to accurately benchmark ED performance and implement effective quality improvement initiatives.

To overcome this, utilize AI tools to automate the data collection and analysis process. This will improve the ED's performance, enabling fair and equitable comparisons. By standardizing the measurement of key performance indicators, we can drive significant improvements in care quality across all hospitals, particularly in regions with limited resources where the need for efficient and effective emergency services is crucial.

Conclusion

This pilot study shows that using an AI-based system to read and convert handwritten doctor's notes into digital data is both possible and effective. In a simulated emergency room setting, the system worked accurately and quickly, helping reduce the time doctors spend on paperwork. This means doctors could have more time for patient care, which may improve patient outcomes and reduce stress on healthcare staff. The system also works well with existing EMRs, allowing real-time access to patient information and supporting better clinical decisions.

Importantly, this study highlights a bigger issue in India's emergency care, there is no consistent or fair way to measure and compare the performance of EDs across hospitals. Manual methods are slow, often inaccurate, and do not consider how sick or complex patients are. This makes it hard to improve care and leads to unequal treatment across different hospitals, especially in areas with fewer resources.

By using AI tools to automatically collect and analyze data, hospitals can track performance more accurately and fairly. This can help set clear standards, identify areas for improvement, and ensure better emergency care for everyone. Future studies should test this system in real hospitals and improve it to handle the challenges of real-life settings. In the long run, such technology can help build a more reliable, efficient, and equal emergency healthcare system in India.

Conflict of Interest

None declared.

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Publication History

Article published online:
27 June 2025