Language Technology Hub

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A large majority of medical data is stored as text, usually across a number of document types and storage locations. This unstructured content embedded in patient records can be tapped for a wealth of greater knowledge with HLA’s deep-dive search and analytics engines.

HLA technology has been developed to utilise unstructured clinical text through a range of functions including:

Report Classifying
Content Extraction
Keyword & Concept Search
Hot Key Abstracting & Coding

HLA’s technology uses semantics (i.e. the meaning of a word, phrase, sentence) and context (document structure and surrounding text) to identify a broad range of medical concepts within medical documents.

Our unique “Knowledge Discovery – Knowledge Reuse” methodology ensures that all knowledge captured in the creation of a client’s solution is integrated immediately into their technology. This enables rapid iteration to an optimal solution for their needs.

The HLA Wheel diagram shows the structure of our technology hub and the Language Technology Hub Applications table expands on the clinical and business uses of the various segments of the wheel.

Service Type



Report Classifier

Document Separation

Separating documents based on defined classes (e.g. separating cancer from non-cancer reports).

Work Streaming

Filter documents and route to correct staff or user-groups for processing (e.g. filter breast cancer reports from other tumour streams).

Case Identification

Identifying a report that is needed for a particular task, e.g. identifying a reportable cancer case to send it to the cancer registry.


Cancer Staging

Convert tumour descriptions to stage.

Data Integration

Identify risks and automatically raise alarms.

Content Extraction

Risk Analysis

Automate extraction of content required to compute risk analysis profiles for patients.

Convert Unstructured text to Structured data

Supply categorised data for BIG Data analytics, and quality audit databases.


Deliver and codify data for storage in population-based databases.

Patient Safety Notifications

Daily extraction from pathology, imaging and other lab reports of diagnoses requiring clinical attention.

Concept Search

Cohort Identification

Find records with certain characteristics (e.g. All patients with prostate cancer).

Case Studies

Identify patients who are current problematic cases so as to make comparisons.

Case Review

Find specific cases without having to know their  demographics (e.g. Clinician retrieving a certain case with a
known medical history but no recollection of the patient’s identity).

Content Retrieval

Ad-hoc general-purpose search for text-based records with given content.

Multi-Disciplinary Team Meetings

Search for radiology, pathology, and other text notes in preparation for Oncology Multi-Disciplinary Team

Report Completion Validation

Medical Record Coding

Ensure that all content that forms a complete record is included and flag missing content (e.g. flagging that
“Plan” has not been included in a particular discharge

Report Consistency Validation

Billing Errors & Omissions

Automatic computation of billing codes lowers errors and increases the number of chargeable items.

Unexpected Results

Ensure that content is consistent across a report (e.g. alerting a radiology report that has a diagnosis that is unexpected in the context of the requested investigation).

Hot Key Coding & Classification

Medical Records Coding

Add-on application that automatically suggests clinical codes for any on-screen content (e.g. suggesting codes in a pop up for a Discharge Summary in an EMR system).

GP Coding

Code specific types of content (e.g. codifying reason for attendance at a General Practice or Emergency Department).