Practical use case of natural language processing for observational clinical research data retrieval from electronic health records: AssistMED project.

Abstract

Electronic health records (EHRs) contain data valuable for clinical research. However, they are in textual format and require manual encoding to databases, which is a lengthy and costly process. Natural language processing (NLP) is a computational technique that allows for text analysis. Our study aimed to demonstrate a practical use case of NLP for a large retrospective study cohort characterization and comparison with human retrieval. Anonymized discharge documentation of 10 314 patients from a cardiology tertiary care department was analyzed for inclusion in the CRAFT registry (Multicenter Experience in Atrial Fibrillation Patients Treated with Oral Anticoagulants; NCT02987062). Extensive clinical characteristics regarding concomitant diseases, medications, daily drug dosages, and echocardiography were collected manually and through NLP. There were 3030 and 3029 patients identified by human and NLP‑based approaches, respectively, reflecting 99.93% accuracy of NLP in detecting AF. Comprehensive baseline patient characteristics by NLP was faster than human analysis (3 h and 15 min vs 71 h and 12 min). The calculated CHA2DS2VASc and HAS‑BLED scores based on both methods did not differ (human vs NLP; median [interquartile range], 3 [2-5] vs 3 [2-5]; P = 0.74 and 1 [1-2] vs 1 [1-2]; P = 0.63, respectively). For most data, an almost perfect agreement between NLP- and human-retrieved characteristics was found; daily dosage identification was the least accurate NLP feature. Similar conclusions on cohort characteristics would be made; however, daily dosage detection for some drug groups would require additional human validation in the NLP‑based cohort. NLP utilization in EHRs may accelerate data acquisition and provide accurate information for retrospective studies.