Health Data Interoperability

Dr Alejandro Metke
Team Leader
Health Data Interoperability

The health data interoperability team is at the forefront of research and development in the use of standard clinical terminologies and information models for exchanging information between clinical systems.

Our Science

Our main scientific goals include improving clinical data quality (accurately capturing meaning) and effectively dealing with patient information spread across the Australian healthcare system.

Key challenges include:

  • Mechanisms to formally define clinical concepts by domain experts with no logics background.
  • Effective concept search to improve data quality during capture.
  • Automatic generation of mappings between code systems to improve semantic interoperability of coded data.

Impact on the Health System

AEHRC technologies are now being used to collect data using key healthcare standards, such as FHIR, and standard terminologies such as SNOMED CT. The aim of these technologies is to increase the value that can be gained from electronic health data, and improve patient outcomes as well as health system performance and productivity.

Our Solutions

  • Snorocket: Fast classification of clinical ontologies. Provides the foundation for using formally-defined clinical terminologies.
  • Ontoserver: Lowering the entry barriers for clinical terminology use. Allows effective search of clinical content using state-of-the-art information retrieval algorithms and simplifies the access to different clinical terminologies by providing a FHIR-based API and a syndication mechanism.
  • FHIRCap: Standardising clinical research data. Implements a rules-based engine that allows exporting clinical research data captured in REDCap into a FHIR repository.



Clinical Terminology Tools

The Australian health system is currently transitioning to a fully electronic system. The Australian health system is currently grappling with the transition to a fully electronic system which will enable health data to be accessed as...

November 19, 20170


Shrimp is HTML5, SVG, and Javascript based, and runs in most modern web browsers. Shrimp is a browser for hierarchical terminologies like SNOMED CT and AMT in a HL7 FHIR Terminology Server like Ontoserver....

November 18, 20173


Snorocket is an implementation of the Dresden algorithm that is tuned for classifying the SNOMED CT clinical terminology. Snorocket Snorocket is an implementation of the Dresden algorithm that is tuned for classifying the SNOMED CT...

November 17, 20173

Case Studies

Team Members:

Alejandro Metke

Andrew Patterson

Hugo Leroux

Hoa Ngo

Oisin Fitzgerald







Alejandro Metke
Dr Alejandro Metke leads the Health Data Interoperability team and has been heavily involved in the implementation of Ontoserver, a terminology server that has been licensed to the Australian Digital Health Agency (ADHA), as well as the development of tools to assist in the standardisation of patient phenotypic data for use in genomics.
CSIRO Profile

Andrew Patterson
Andrew Patterson is the designer and main developer for PenCAT, an award winning population health tool used in Australian general practice.

Hugo Leroux
The work on semantic enrichment and interoperability of clinical research data (2013 to date) has received worldwide attention and recognition from standards bodies (CDISC, W3C, FDA) including five citations in one Journal publication.
CSIRO Profile

Hoa Ngo
Duy-Hoa NGO is the author of YAM++: (not) Yet Another Ontology Matching tool, which achieved the best matching tool in the Ontology Alignment Evaluation Initiative – OAEI 2012, 2013 campaigns
CSIRO Profile

Oisin Fitzgerald
Oisin is part of the industry PhD program, a collaboration between CSIRO, UNSW and an industry partner, in this case NSW Health. He is currently working on patient phenotyping to support new care pathways.


  1. Metke-Jimenez A, Lawley M. Snorocket 2.0: Concrete domains and concurrent classification. In Proceedings of the 2nd International Workshop on OWL Reasoner Evaluation. 2013 (Vol. 1015, p. 32).
  2. Karimi S, Wang C, Metke-Jimenez A, et al. Text and data mining techniques in adverse drug reaction detection. ACM Computing Surveys. 2015; 47(4):56.
  3. DuyHoa Ngo, Zohra Bellahsene. Overview of YAM++—(not) Yet Another Matcher for ontology alignment task. Web Semantics: Science, Services and Agents on the World Wide Web. 2016 (Vol 41 p 30-49)