Automating Access to Real-World Evidence.

ABSTRACT:

Introduction

Real-world evidence is important in regulatory and funding decisions. Manual data extraction from electronic health records (EHRs) is time-consuming and challenging to maintain. Automated extraction using natural language processing (NLP) and artificial intelligence may facilitate this process. Whereas NLP offers a faster solution than manual methods of extraction, the validity of extracted data remains in question. The current study compared manual and automated data extraction from the EHR of patients with advanced lung cancer.

Methods

Previously, we extracted EHRs from 1209 patients diagnosed with advanced lung cancer (stage IIIB or IV) between January 2015 and December 2017 at Princess Margaret Cancer Centre (Toronto, Canada) using the commercially available

SUBMITTER: Gauthier MP 

PROVIDER: S-EPMC9201015 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature