OBJECTIVES: Electronic medical records (EMR) are increasingly utilized in clinical practice and research, allowing for more efficient availability of rich patient records. However, most use of EMR is limited to coded, structured, and administrative data, while the vast majority of patient information (e.g. disease subtype, severity, medical device usage, etc.) is tied up in narrative clinical notes. The challenge remains in accessing the information in these patient notes. Historically this has been done via timely and costly manual chart review, but as the amount of EHR data increases exponentially, manual chart review becomes impractical and impossible. Advancements in Natural Language Processing (NLP) have demonstrated promising results in combining the capture of additional clinical note information with the efficiency of modern informatics.

The objective of this study is to demonstrate the relevancy and utility of NLP to extract health data from EMR in real-world observational studies. METHODS: We conducted a systematic review and meta analysis of performance metrics for five (5) NLP-driven projects involving oncology, inflammation and medical devices ,which had similar protocols and objectives. We assessed and validated the accuracy of NLP algorithms, as well as heterogeneity of accuracy between studies using random effects meta-analysis (represented by I2 value). RESULTS: A total of 382,523 patients were identified using NLP among the 5 studies. Accuracy among the studies ranged from 95.2% to 100% (95% CI: 95.1%, 100%), with an I2 value of 95.9% (95% CI: 92.9%, 97.7%). CONCLUSIONS: NLP provide a unique opportunity to extract meaningful information from patient-level narrative clinical notes in EMR data sources with high degree of accuracy. This provides additional rich sources of data from narrative clinical notes, that are otherwise not easily available, to support epidemiology and other real-world observational studies.