Development of novel optical character recognition system to reduce recording time for vital signs and prescriptions: A simulation-based study

Abstract

Digital advancements can reduce the burden of recording clinical information. This intra-subject experimental study compared the time and error rates for recording vital signs and prescriptions between an optical character reader (OCR) and manual typing. This study was conducted at three community hospitals and two fire departments in Japan. Thirty-eight volunteers (15 paramedics, 10 nurses, and 13 physicians) participated in the study. We prepared six sample pictures: three ambulance monitors for vital signs (normal, abnormal, and shock) and three pharmacy notebooks that provided prescriptions (two, four, or six medications). The participants recorded the data for each picture using an OCR or by manually typing on a smartphone. The outcomes were recording time and error rate defined as the number of characters with omissions or misrecognitions/misspellings of the total number of characters. Data were analyzed using paired Wilcoxon signed-rank sum and McNemar’s tests. The recording times for vital signs were similar between groups (normal state, 21 s [interquartile range (IQR), 17–26 s] for OCR vs. 23 s [IQR, 18–31 s] for manual typing). In contrast, prescription recording was faster with the OCR (e.g., six-medication list, 18 s [IQR, 14–21 s] for OCR vs. 144 s [IQR, 112–187 s] for manual typing). The OCR had fewer errors than manual typing for both vital signs and prescriptions (0/1056 [0%] vs. 14/1056 [1.32%]; p<0.001 and 30/4814 [0.62%] vs. 53/4814 [1.10%], respectively). In conclusion, the developed OCR reduced the recording time for prescriptions but not vital signs. The OCR showed lower error rates than manual typing for both vital signs and prescription data.

A machine learning framework for extracting information from biological pathway images in the literature

“…A seeming limitation of OChemR is its unability to handle reaction reversibility (i.e., recognition of only unidirectional arrows, not bidirectional ones) and its ability to detect only molecular reactions presented in a very specific format. Another technology available for extracting information from images is optical character recognition (OCR), which detects texts within images, and transforms them into editable and searchable texts (Hom et al, 2022; Soeno et al, 2024).…”