Advancing Healthcare Through Standardised Electronic Medical Records

Every day, health services and hospitals generate an immense wealth of data. This includes electronic medical records containing information about patients’ medical history, prescription medications, imaging reports, immunisations, and more. This data can be extremely useful for research, monitoring and auditing. 

However, despite its huge potential, the resource remains largely untapped. This is partly because of the diverse nature of electronic medical record data, which often differs in quality, terminology, and vocabulary used, making the data difficult to compare. Additionally, concerns about patient privacy and confidentiality significantly hinder the reuse of electronic medical record data. Therefore, it is important to have an integrated national system that standardises electronic medical record data in a safe and secure way to maximise its utility and advance healthcare. 

Enhancing the Reusability of Electronic Medical Record Data

The ARDC co-invested in a project led by the University of Melbourne that helped standardise electronic medical record data from Australian hospitals and healthcare centres. The Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM) is the international ‘gold standard’, transforming complex and diverse electronic medical record data into a consistent, structured data model. This enables the data to be readily compared between hospitals and reused for secondary purposes provided ethical permission is obtained. 

Roger Ward, Data Solutions Architect at the ARDC, was involved in the project, having previously worked in the Department of General Practice and Primary Care at the University of Melbourne’s Medical School. After using the OMOP-CDM model to convert electronic medical record data from 140 general practices across Victoria and New South Wales, Roger and his team expanded the use of the model into hospitals. He highlighted the importance of this project in addressing the diverse and fragmented nature of electronic medical record data.  

“Different electronic medical health record systems store information in similar ways, but not quite the same. So, if you’re a researcher trying to use the data, there needs to be a common denominator to be able to compare the data. The common data model is what brings it into that common format.”

“We converted data from Austin Health, Western Health, Queensland Health and the Sydney Local Health District to the common data model, making those datasets directly comparable,” said Roger.

Ensuring Data Consistency and Privacy

By using standard vocabularies, terminology and coding schemes, the OMOP-CDM helps convert data into a common format according to the FAIR principles (Findable, Accessible, Interoperable and Reusable). The model can be implemented and used in existing database infrastructure without any change or loss of data. 

“There are two major vocabularies used for electronic medical record data, and depending on which vocabulary is used, it’s coded differently. By standardising the data to the same vocabulary, we can make it more comparable,” said Roger.

To minimise risks associated with privacy and confidentiality, the OMOP-CDM replaces all personal identifiers with a generic number, preventing the data from being linked to an individual. This allows the personal data to be protected while still allowing for analysis and processing. 

“The key thing about this project is the data never leaves the hospital. The researcher designs the analysis and sends the code to the hospital, then the hospital runs the code on the standardised data and sends back the aggregated results, rather than data on individual patients. So that addresses many of the security concerns,” said Roger.

The OMOP-CDM allows researchers to access electronic medical record data more easily, enabling them to undertake advanced analytics while ensuring that patient data remains securely firewalled within its host institution. By standardising electronic medical record data using the OMOP-CDM, hospitals, health departments, auditors, regulators and universities can gain valuable insights tailored to their operational and research needs. 

Global Adoption of the OMOP-CDM

Globally, the adoption of the OMOP-CDM has been steadily increasing, with around 12% of electronic medical records converted by 2022. This includes data from over 450 databases, covering over 928 million unique patient records across 41 countries^[1]. 

“The OMOP-CDM is increasingly being recommended and adopted by other organisations around the world as a way of increasing transparency. In the European WorldFAIR project, one of the key recommendations was that the OMOP-CDM was adopted as the standard,” said Roger.

Professor Nicole Pratt, President of the Observational Health Data Sciences and Informatics (OHDSI) Asia-Pacific chapter, also noted an increase in global adoption of the OMOP-CDM.

“The OMOP-CDM is fast becoming the world standard approach for harmonising EMR data for research. The OMOP-CDM makes it incredibly efficient to run rapid, large-scale research across multiple countries and many millions of patient records.”

“The support provided by the ARDC to accelerate the translation of Australian EMR data into the OMOP-CDM has been incredibly valuable, as it will enable more local data to align with international standards. This will allow Australian EMR data to be included in global collaborative studies, so that we can better understand the risks and benefits of the medical treatments we use,” said Nicole. 

A suite of training resources supports the OMOP-CDM, including workshops provided by the OHDSI Asia-Pacific collaboration, providing researchers with hands-on training in preparing their data for the common data model. 

The project aims to expand the use of the model into more hospitals across Australia. Work on the OMOP-CDM is ongoing through the ARDC’s People Research Data Commons’ focus area on data integration. 

The transformation of electronic medical record data using the OMOP-CDM represents a significant step in unlocking the full potential of health data to improve research, auditing, and clinical practice. Learn more about the new program.

Read journal articles about OMOP-CDM published in PLOS One and BMJ Health & Care Informatics. 

Learn more about our new Data Integration activity, which is continuing work on the OMOP-CDM as part our People Research Data Commons for health research and translation.

The ARDC is enabled by the National Collaborative Research Infrastructure Strategy (NCRIS) to support national digital research infrastructure for Australian researchers.

Reference

1. Hallinan CM, Ward R, Hart GK, Sullivan C, Pratt N, Ng AP, Capurro D, Van Der Vegt A, Liaw ST, Daly O, Luxan BG, Bunker D, Boyle D. Seamless EMR data access: Integrated governance, digital health and the OMOP-CDM. BMJ Health Care Inform. 2024 Feb 21;31(1):e100953. doi: 10.1136/bmjhci-2023-100953. Jump back