Background: Efficient and accurate clinical data management is crucial for stroke care and research; however, for complex stroke cohorts, manual data entry is often complicated by high human error rates and significant inefficiency. This study addressed this gap by developing and evaluating an automated system for constructing high-quality stroke cohorts from electronic health records (EHRs). Methods: This retrospective cohort study was conducted at a single specialty hospital, comparing manual data entry (January–June 2022) with an automated system (January–June 2024). The system uses C# and secure SQL protocols for direct EHR integration. We developed an automated system using the C# programming language to extract 133 items covering the full hospitalization period (from admission to discharge) from EHRs, aligning with the Korean Stroke Registry, the Registry of Stroke Care Quality, and national quality assessment programs. The system’s effectiveness was evaluated by assessing the data entry time, data error rate, and medical record completion rate and comparing the automated method’s performance against conventional data entry. Results: The automated system significantly reduced the data entry time from 35 min to 19 s per patient. Furthermore, the data error rate decreased from 2.32% to 0.15% (p < 0.001), and the rate of missing medical records decreased from 28.9% to 16.2% (p < 0.001). Conclusions: The proposed clinical data collection and cohort construction system effectively improved data quality and efficiency compared to the manual method. This system provides a reliable and scalable data infrastructure that could facilitate research on stroke and quality improvement initiatives.

The growing volume of clinical data necessitates efficient management of Electronic Health Records (EHR) data. Traditional labor-intensive EHR management methods have led to significant risks, including high error rates and data omissions. This study modified and developed EHR templates focusing on key cohort-specific variables and developed a C#-based automated system for extracting and managing data from patients with acute stroke. The system reduced data entry time per patient from 35 minutes to 19 seconds. The data error rate decreased from 2.32% to 0.15%. This system enhances data management efficiency and is expected to support the construction of various cohorts through further expansion.

This study developed and validated a Python-based program for analyzing the region of interest mean values using red–green–blue (RGB)–reconstructed perfusion-weighted imaging (PWI) data. The program addressed the limitations of traditional source-image-based analysis, including manufacturer dependency, high storage costs, and limited accessibility. High intraclass correlation coefficient (ICC) values for cerebral blood volume and time to peak confirmed the feasibility of this approach, whereas lower ICC values for mean transit time suggested discrepancies between reconstructed RGB images and conventional dynamic susceptibility contrast–based data. Designed with a user-friendly graphical user interface and executable distribution, the program enhances clinical applicability. Future studies should focus on improving the algorithms and validating the approach with multicenter datasets.