Streamlining data recording through optical character recognition: a prospective multi-center study in intensive care units

• Published in: Critical care (London, England) Vol. 29; no. 1; p. 117
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: London BioMed Central 18.03.2025; BioMed Central Ltd
• Subjects: Accuracy; Aged; Computational linguistics; Critical Care Medicine; Data entry; Digital technology; Emergency Medicine; Female; Hemodynamics; Hospitals; Human error; Humans; Information management; Intensive; Intensive care; Intensive Care Units - organization & administration; Intensive Care Units - statistics & numerical data; Language processing; Male; Medical equipment; Medicine; Medicine & Public Health; Middle Aged; Mobile applications; Natural language interfaces; Observational studies; Physiological apparatus; Physiology; Prospective Studies; Research methodology; Software upgrading; User surveys; Variables; Ventilators; Hong Kong China; China; Mobile applications; Optical character recognition; Intensive care unit; Data entry; Data registry
• ISSN: 1364-8535; 1466-609X; 1364-8535; 1466-609X; 1366-609X
• DOI: 10.1186/s13054-025-05347-1

Background 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. Methods 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. Results 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). Conclusion 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.