Page 131 - Special Topic Session (STS) - Volume 2

STS466 Md. Khadzir S.A. et al.
1. Terminology Management– to allow user to upload the SNOMED CT
International Release content into MyHarmony, and upload SNOMED
CT Refset in reference to the SNOMED CT International Release.
2. Data Management– to allow user to upload the data that will be
harmonized and codified. This function also allows user to view the
content of the data.
3. Codification Management– to allow user to codify the dataset
according to the selected SNOMED CT Refset and view the codified
data for validation purpose.
4. Query Management– to allow user to explore the data by generating
queries using Structured Query Language (SQL).

The functions were arranged according to the work process. First, the
SNOMED CT International Release, SNOMED CT Cardiology Refset, and the
dataset needs to be uploaded. Then, the dataset is codified and saved. Using
the Query Management, the codified data can then be explored via data
profiling and query generation.
For initial analysis, SNOMED CT International release version 20160731 was
used as the Cardiology Reference Set was developed using this version of
SNOMED CT.
The team received a set of database from a hospital with cardiology service
which consists of 16224 discharge summaries from year 2017. The database
was then uploaded into MyHarmony. The personally identified information
(patient names, ID, and street address) were anonymised prior to codification
and analysis. The output is a codified dataset, which enable information
processing and analysis by machines.
Using the Query Management, the codified data was then be explored via
data profiling and query generation.

3. Result
The team conducted several data profiling queries to ensure that
MyHarmony were able to capture the data correctly. For example, the number
of records by month between Raw data (MyHarmony without SNOMED CT)
and Harmonized data (MyHarmony with SNOMED CT) should return the same
result. Other examples of data profiling queries are the number of records by
gender, by specialty, and by ethnicity.
Next, the team developed queries required by the National Cardiovascular
Disease (NCVD) registries and compare the results with published registry
reports. For example, querying the number of Ischaemic Heart Disease (IHD)
by gender shows 1:4 female to male ratio, which is a similar ratio in the registry
reports. Furthermore, the query also shows that Harmonized data captures

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