Aggregating and Integrating Data on Health Outcomes Associated with Bushfires at a National Scale
Exploreabout Aggregating and Integrating Data on Health Outcomes Associated with Bushfires at a National Scale
Electronic Medical Records as a National Data Asset
A national, research-ready hospital electronic medical record data asset for greater accessibility, rapid interrogation, and evidence generation
Who will benefit
Peak bodies, research organisations, commercial providers, governments (state and federal), pharmaceutical research
Program
The Challenge
Electronic medical record (EMR) data warehouses for audit, research and surveillance exist in Australia. However, they have variable standards. As the need to utilise health data across organisational boundaries grows, we face challenges regarding data standards, linkage, and governance. Converting EMRs to one common data model (CDM) will open up health data for collaborative use.
The Response
To enhance data accessibility for rapid interrogation and evidence generation, this project has established a national, research-ready hospital EMR data asset.
The project transforms data from hospital EMRs to an international gold standard Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). Tools, mappings and experience gained will be made openly available, and a community of practice and a roadmap for continued national implementation will be built.
By streamlining data governance, consent, and ethics, the data asset will significantly improve the quality, accessibility and feasibility of EMR data warehouses. We have converted 3 existing data warehouses from the following centres so that they can be accessed through a single interface:
- Queensland Health
- Austin Health
- Western Health.
Learnings from these conversions will guide the conversion of other data warehouse platforms, including the EPIC EMR system, which will be investigated in collaboration with the Peter MacCallum Cancer Centre.
The Outcomes
The project has delivered or contributed to:
Health information data is currently siloed in individual EMR systems. The OMOP Common Data Model will harmonise this data and allow direct comparisons. A common data layer will allow standard queries to be run on the data and results shared among researchers.
When a researcher wants to run standard analysis on a number of OMOP-compliant datasets, they will contact the data custodian at each centre to obtain permission. Once permission has been given, a standard analysis script can be provided by the researcher and run on each data set. Results are generated and shared for research, but the actual underlying health information data doesn’t leave the host institution.
The OMOP Common Data Model is supported by high quality training resources. Extract, transform and load (ETL) workshops are provided by OHDSI Asia Pacific. Researchers are provided hands-on training on preparing their health data for the common data model. The EHDEN Academy provides online training and assessment in the common data model via the learning management system Moodle. The annual OHDSI International Symposium provides training and collaboration opportunities for researchers.
The project has:
- delivered a research-ready hospital electronic medical record (EMR) data asset that enhances national accessibility
- allowed rapid interrogation of the data asset and help generate evidence-based population health insights
- created a health dataset that aligns with the Findable, Accessible, Interoperable, and Reusable (FAIR) principles
- enabled collaboration among national and international research groups with the FAIR EMR asset.
A paper has been published by the project team on the OMOP Common Data Model and how its adoption locally and internationally facilitates more efficient and secure access to EMR data. Read the paper.
Find training materials, including guides, workshop recordings and assessments through:
Who Will Benefit
This project will benefit:
- peak bodies
- research organisations
- commercial providers
- government (state and federal)
- pharmaceutical research.
The Partners
- The University of Melbourne
- MACH
- Health Translation Queensland
- Health Translation SA
- Maridulu Budyari Gumal NSW
- AIHW
- CSIRO
- Queensland Health
- UNSW
- The University of Queensland
- Sydney Local Health Area District Health Informatics Unit
- The Austin Hospital
- Victoria Office of the Chief Medical Information Officer
- Victorian Comprehensive Cancer Centre (VCCC) Alliance
- Western Health
- Peter MacCallum Cancer Centre
Further Resources
- For more information, please visit the project website.
- Read more about the Observational Health Data Sciences and Informatics (OHDSI) Australia and the Observational Health Data Sciences and Informatics.
Contact the ARDC
Timeframe
January 2021 to November 2023
Current Phase
Complete
ARDC Co-investment
$345,000
Project lead
The University of Melbourne
Categories
Research Topic
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