Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Jan 18, 2019
Open Peer Review Period: Jan 18, 2019 - Mar 15, 2019
Date Accepted: May 29, 2019
(closed for review but you can still tweet)
Improving the efficacy of data entry process for clinical research with an NLP-driven medical information extraction system: a quantitative field research
ABSTRACT
Background:
The growing interest in observational trials using patient historical data from electric medical records (EMRs) poses challenges to both efficiency and quality of clinical data collection and management. However, even using electronic data capture (EDC) system and electronic case report form (eCRF), a manual data entry process followed by chart review is still inevitable and becomes an efficiency bottleneck.
Objective:
To facilitate the data entry process, we developed a natural language processing (NLP)-driven medical information extraction system (NLP-MIES) based on the i2b2 reference standard. We aimed to evaluate whether our NLP-MIES based eCRF application could improve both the accuracy and speed of the data entry process.
Methods:
We conducted a randomized and controlled field experiment and recruited a total of 24 eligible participants, 12 for manual group and 12 for NLP-MIES support group. We simulated the real-world eCRF filling process using our system and compared the performance of data entry on two research topics, pediatric congenital heart disease and pneumonia.
Results:
For congenital heart disease eCRF, NLP-MIES support was associated with an increasing of 15% (95% CI 4% to 120%, P=0.0269) in geometric mean accuracy and a reduction of 33% (95% CI 22% to 42%, P <0.001) in geometric mean of the time consumed. For pneumonia eCRF, NLP-MIES support was associated with an increasing of 18% (95% CI 6% to 32%, P =0.0075) in geometric mean accuracy and a reduction of 31% (95% CI 19% to 41%, P =0.001) in geometric mean of the time consumed.
Conclusions:
Our system could improve both the accuracy and speed of the data entry process.
Citation
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Copyright
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