Open AccessImproving the Efficacy of the Data Entry Process for Clinical Research With a Natural Language Processing–Driven Medical Information Extraction System: Quantitative Field Research
Author(s) -
Hua Jiang,
Ken Chen,
Lei Fang,
Shaodian Zhang,
Fei Wang,
Handong Ma,
Liebin Zhao,
Shijian Liu
Publication year - 2019
Publication title -
jmir medical informatics
Language(s) - English
Resource type - Journals
ISSN - 2291-9694
DOI - 10.2196/13331
Subject(s) - computer science , data extraction , observational study , electronic data capture , field (mathematics) , process (computing) , data science , data quality , data collection , information extraction , chart , quality (philosophy) , electronic medical record , clinical trial , data mining , information retrieval , medicine , medline , pathology , engineering , operations management , statistics , mathematics , political science , law , metric (unit) , philosophy , internet privacy , epistemology , pure mathematics , operating system
Background The growing interest in observational trials using patient data from electronic medical records poses challenges to both efficiency and quality of clinical data collection and management. Even with the help of electronic data capture systems and electronic case report forms (eCRFs), the manual data entry process followed by chart review is still time consuming. Objective To facilitate the data entry process, we developed a natural language processing–driven medical information extraction system (NLP-MIES) based on the i2b2 reference standard. We aimed to evaluate whether the NLP-MIES–based eCRF application could improve the accuracy and efficiency of the data entry process. Methods We conducted a randomized and controlled field experiment, and 24 eligible participants were recruited (12 for the manual group and 12 for NLP-MIES–supported group). We simulated the real-world eCRF completion process using our system and compared the performance of data entry on two research topics, pediatric congenital heart disease and pneumonia. Results For the congenital heart disease condition, the NLP-MIES–supported group increased accuracy by 15% (95% CI 4%-120%, P =.03) and reduced elapsed time by 33% (95% CI 22%-42%, P <.001) compared with the manual group. For the pneumonia condition, the NLP-MIES–supported group increased accuracy by 18% (95% CI 6%-32%, P =.008) and reduced elapsed time by 31% (95% CI 19%-41%, P <.001). Conclusions Our system could improve both the accuracy and efficiency of the data entry process.