Nitayavardhana Prompak, Liu Keibun, Fukaguchi Kiyomitsu, Fujisawa Mineto, Koike Itaru, Tominaga Aina, Iwamoto Yuta, Goto Tadahiro, Suen Jacky Y, Fraser John F, Ng Pauline Yeung
Division of Cardiothoracic Surgery, Department of Surgery, Faculty of Medicine, Siriraj Hospital, Bangkok, Thailand.
Critical Care Research Group, The Prince Charles Hospital, Brisbane, Australia.
Crit Care. 2025 Mar 18;29(1):117. doi: 10.1186/s13054-025-05347-1.
The manual entry of data into large patient databases requires significant resources and time. It is possible that a system that is enhanced with the technology of optical character recognition (OCR) can facilitate data entry, reduce data entry errors, and decrease the burden on healthcare personnel.
This was a prospective multi-center observational study across intensive care units (ICU) in 3 countries. Subjects were critically-ill and required invasive mechanical ventilation and extracorporeal life support. Clinical photos from various medical devices were uploaded using an OCR-enhanced case record form. The degree of data completeness, data accuracy, and time saved in entering data were compared with conventional manual data entry.
The OCR-based system was developed with 868 photos and validated with 469 photos. In independent validation by 8 untrained personnel involving 1018 data points, the overall data completeness was 98.5% (range 98.2-100%), while the overall data accuracy was 96.9% (range 95.3-100%). It significantly reduced data entry time compared to manual entry (mean reduction 43.9% [range 27.0-1.1%]). The average data entry time needed per patient were 3.4 (range 1.2-5.9) minutes with the OCR-based system, compared with 6.0 (range 2.2-8.1) minutes with manual data entry. Users reported high satisfaction with the tool, with an overall recommendation rate of 4.25 ± 1.04 (maximum of 5).
An OCR-based data entry system can effectively and efficiently facilitate data entry into clinical databases, making it a promising tool for future clinical data management. Wider uptake of these systems should be encouraged to better understand their strengths and limitations in both clinical and research settings.
将数据手动录入大型患者数据库需要大量资源和时间。利用光学字符识别（OCR）技术增强的系统有可能促进数据录入、减少数据录入错误并减轻医护人员的负担。
这是一项在3个国家的重症监护病房（ICU）进行的前瞻性多中心观察性研究。研究对象为危重症患者，需要有创机械通气和体外生命支持。使用OCR增强的病例记录表上传来自各种医疗设备的临床照片。将数据完整性、数据准确性以及数据录入节省的时间与传统手动数据录入进行比较。
基于OCR的系统使用868张照片开发，并通过469张照片进行验证。在由8名未经培训的人员进行的涉及1018个数据点的独立验证中，总体数据完整性为98.5%（范围98.2 - 100%），而总体数据准确性为96.9%（范围95.3 - 100%）。与手动录入相比，它显著减少了数据录入时间（平均减少43.9% [范围27.0 - 1.1%]）。基于OCR的系统每位患者平均需要的数据录入时间为3.4（范围1.2 - 5.9）分钟，而手动数据录入为6.0（范围2.2 - 8.1）分钟。用户对该工具满意度很高，总体推荐率为4.25 ± 1.04（满分5分）。
基于OCR的数据录入系统可以有效且高效地促进临床数据库的数据录入，使其成为未来临床数据管理的一个有前景的工具。应鼓励更广泛地采用这些系统，以更好地了解它们在临床和研究环境中的优势和局限性。
