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The Problem Statement

The large majority of clinical data is stored as text. This problem of up to 80% of hospital and patient medical data being scattered across a number of document types and locations and inaccessible to Big Data technology has been recognised for years, with no viable solution.

One of the biggest challenges in healthcare is enabling automated classification, coding and indexing of those clinical documents, so allowing access to a wealth of knowledge.

What does Health Language Analytics (HLA) do?

HLA enables health organisations to turn unstructured textual data from clinical documents into structured data using its award-winning Clinical Natural Language Processing (CNLP) platform – Horizon.

Horizon analyses free-text clinical documents to process, categorise, and code clinical records to SNOMED-CT, ICD-O3, or other taxonomies, for scalable and more efficient search, extraction, and supply for big data analysis. Horizon works at a speed and accuracy that exceeds manual coding and does this 24×7.

HLA’s US subsidiary (HLA-Global) is already providing subject matter expertise to the Centers for Disease Control (Atlanta), and AI categorisation tools to major Californian cancer registries via Horizon.

What applications and use cases can this technology be used for?

There are a range of areas and services Horizon is utilised for – some examples:

  • Automated coding of pathology reports for cancer registries.
  • Decision support – delivery of extracted cancer pathology for Oncology MDT meetings.
  • Business process automation – automated classification and categorisation of clinical reports by disease type or organ for coders.
  • Research & epidemiology – cohort identification for research projects and clinical trials.

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