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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Automating Access to Real-World Evidence.

Marie-Pier Gauthier1, Jennifer H Law1, Lisa W Le2

  • 1Department of Medical Oncology, Princess Margaret Cancer Centre, Toronto, Ontario, Canada.

JTO Clinical and Research Reports
|June 20, 2022
PubMed
Summary

Automated data extraction from electronic health records (EHRs) using artificial intelligence is highly accurate and faster than manual methods. This approach can significantly enhance the scale of real-world evidence studies in advanced lung cancer research.

Keywords:
Artificial intelligenceHealth recordsNatural language processingReal-world dataReal-world evidenceValidation

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Area of Science:

  • Oncology
  • Health Informatics
  • Artificial Intelligence

Background:

  • Real-world evidence (RWE) is crucial for regulatory and funding decisions.
  • Manual data extraction from electronic health records (EHRs) is labor-intensive and difficult to sustain.
  • Automated extraction methods, particularly natural language processing (NLP), offer potential for faster data retrieval but raise questions about validity.

Purpose of the Study:

  • To compare the accuracy and efficiency of automated (NLP/AI) versus manual data extraction from EHRs.
  • To evaluate the performance of automated extraction for various data elements in patients with advanced lung cancer.
  • To assess the feasibility of using AI for large-scale RWE studies.

Main Methods:

  • EHR data from 1209 advanced lung cancer patients were analyzed.
  • Automated extraction was performed using the AI engine DARWEN.
  • A subset of 100 patients had data manually extracted by two abstractors for comparison, with expert adjudication.

Main Results:

  • Automated extraction was significantly faster (<1 day vs. ~225 person-hours).
  • High accuracy and concordance were observed for demographic, comorbidity, and treatment data (96%-100%).
  • Lower accuracy and concordance were noted for unstructured data like performance status and smoking status, and for specific metastatic sites.

Conclusions:

  • Automated EHR data abstraction using NLP/AI is highly accurate and efficient.
  • Challenges remain with poorly structured EHR data and variations in clinical terminology.
  • NLP facilitates large-scale RWE studies, overcoming limitations of manual extraction.