Validating transformers for redaction of text from electronic health
  records in real-world healthcare
Zeljko Kraljevic Anthony Shek Joshua Au Yeung Ewart Jonathan Sheldon Mohammad Al-Agil Haris Shuaib Xi Bai Kawsar Noor Anoop D. Shah Richard Dobson James Teo
Protecting patient privacy in healthcare records is a top priority, and
redaction is a commonly used method for obscuring directly identifiable
information in text. Rule-based methods have been widely used, but their
precision is often low causing over-redaction of text and frequently not being
adaptable enough for non-standardised or unconventional structures of personal
health information. Deep learning techniques have emerged as a promising
solution, but implementing them in real-world environments poses challenges due
to the differences in patient record structure and language across different
departments, hospitals, and countries.
  In this study, we present AnonCAT, a transformer-based model and a blueprint
on how deidentification models can be deployed in real-world healthcare.
AnonCAT was trained through a process involving manually annotated redactions
of real-world documents from three UK hospitals with different electronic
health record systems and 3116 documents. The model achieved high performance
in all three hospitals with a Recall of 0.99, 0.99 and 0.96.
  Our findings demonstrate the potential of deep learning techniques for
improving the efficiency and accuracy of redaction in global healthcare data
and highlight the importance of building workflows which not just use these
models but are also able to continually fine-tune and audit the performance of
these algorithms to ensure continuing effectiveness in real-world settings.
This approach provides a blueprint for the real-world use of de-identifying
algorithms through fine-tuning and localisation, the code together with
tutorials is available on GitHub (https://github.com/CogStack/MedCAT).