Extracting laboratory test information from paper-based reports

• Published in: BMC medical informatics and decision making 2023-11, Vol.23 (1), p.1-251, Article 251
• Main Authors: Ma, Ming-Wei, Gao, Xian-Shu, Zhang, Ze-Yu, Shang, Shi-Yu, Jin, Ling, Liu, Pei-Lin, Lv, Feng, Ni, Wei, Han, Yu-Chen, Zong, Hui
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Automation; Computational linguistics; Conditional random fields; Deep learning; Digitization; Electronic health records; Format; Hospitals; Human error; Information extraction; Information processing; Information retrieval; Information systems; Laboratories; Laboratory test; Laboratory tests; Language processing; Layouts; Lightweight; Medical care; Medical informatics; Medical tests; Modules; Natural language interfaces; Natural language processing; Optical character recognition; Paper based medical reports; Performance evaluation; Physicians; Pipelining (computers); Quality management; Structured data
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-023-02346-6

Background In the healthcare domain today, despite the substantial adoption of electronic health information systems, a significant proportion of medical reports still exist in paper-based formats. As a result, there is a significant demand for the digitization of information from these paper-based reports. However, the digitization of paper-based laboratory reports into a structured data format can be challenging due to their non-standard layouts, which includes various data types such as text, numeric values, reference ranges, and units. Therefore, it is crucial to develop a highly scalable and lightweight technique that can effectively identify and extract information from laboratory test reports and convert them into a structured data format for downstream tasks. Methods We developed an end-to-end Natural Language Processing (NLP)-based pipeline for extracting information from paper-based laboratory test reports. Our pipeline consists of two main modules: an optical character recognition (OCR) module and an information extraction (IE) module. The OCR module is applied to locate and identify text from scanned laboratory test reports using state-of-the-art OCR algorithms. The IE module is then used to extract meaningful information from the OCR results to form digitalized tables of the test reports. The IE module consists of five sub-modules, which are time detection, headline position, line normalization, Named Entity Recognition (NER) with a Conditional Random Fields (CRF)-based method, and step detection for multi-column. Finally, we evaluated the performance of the proposed pipeline on 153 laboratory test reports collected from Peking University First Hospital (PKU1). Results In the OCR module, we evaluate the accuracy of text detection and recognition results at three different levels and achieved an averaged accuracy of 0.93. In the IE module, we extracted four laboratory test entities, including test item name, test result, test unit, and reference value range. The overall F1 score is 0.86 on the 153 laboratory test reports collected from PKU1. With a single CPU, the average inference time of each report is only 0.78 s. Conclusion In this study, we developed a practical lightweight pipeline to digitalize and extract information from paper-based laboratory test reports in diverse types and with different layouts that can be adopted in real clinical environments with the lowest possible computing resources requirements. The high evaluation performan