Page 132 - Special Topic Session (STS) - Volume 2

STS466 Md. Khadzir S.A. et al.
more result compare to Raw data due to SNOMED CT relationship structure,
thus capturing all the subtypes of IHD and its synonyms or ways of writing.
The registry, however, only captures three (3) diagnosis due to its
structured format, which are ST Elevation Myocardial Infarction, Non-ST
Elevation Myocardial Infarction, and Unstable Angina. This trend and pattern
comparison allow validation by the Clinicians and gains their buy-in in using
MyHarmony.
The team also tried to generate more queries required by the NCVD
registry. However, it was limited by the documentation in the discharge
summary. Registry queries requires more detail information that may often
not documented in a discharge summary, such as information on smoking
status and complications of procedures.
After that, the team was tasked to generate National Cardiology Key
Performance Indicators (KPIs). MyHarmony are able to generate 7 out of the 8
KPIs (KPI 2 to 8). The first KPI was excluded because the data are available at
the clinic and not documented in inpatient discharge summaries. The Health
Information Framework (HIF) was developed for the 7 KPIs, which detailed out
the inclusion and exclusion criteria, the target, the formula, the terms used by
MyHarmony, and query, and lastly a section for additional notes.
Preliminary manual validation on the completeness and accuracy of
codified data shows 90% precision and 70% recall. The content of those
records is complex as it does not follow grammar rules, and contains a large
number of short forms, abbreviations, acronyms and analogous terms (e.g.,
synd, ACS, CCS IV, NYHA 2). One example of record is “2VD with RCA culprit
lesion - Ad hoc PCI DES to RCA and LAD” which is challenging to codify using
approaches based on strict grammar. The revised version of MyHarmony uses
a different approach based on shallow parsing and the consideration of
multiple suitable combinations of words in a sentence. With further iterations
and improvement in the mapping, these challenges were overcome[3].
From the SNOMED CT codified database, the system was able to show a
more accurate result during analysis . This is because MyHarmony capitalises
on the existing SNOMED CT relationships structure between concepts. In this
case, when querying “Number of Ischaemic Heart Disease cases per year”,
MyHarmony search the code and term for “Ischaemic heart disease”, its
synonyms and accepted abbreviations, and all the subtypes of Ischaemic heart
disease such as all subtypes of “Myocardial infarction” and “Angina”.
MyHarmony aggregates these records resulting in a more accurate analysis.
Usually, the result where MyHarmony utilise SNOMED CT’s relationship
structure would show more records. This is because Mi-Harmony was able to
aggregate data not just through String match, but also utilize the IS-A
hierarchy structure in SNOMED CT. For example, querying “Ischemic heart
disease” will gather clinical records with synonymous terms like “Ischaemic

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