Speakers
Description
The increasing volume and heterogeneity of patient care data present significant challenges for
comprehensive analysis and the generation of insights, particularly in specific disease areas
such as respiratory diseases. Standardising diverse health data is crucial for enabling large-
scale observational research and ensuring data readiness. The Observational Medical Outcomes
Partnership (OMOP) Common Data Model (CDM) provides a widely adopted standard for
harmonising such data. However, evaluating the quality of data transformed into the OMOP
CDM format is a critical step before its use in research or clinical decision support.
This study evaluates the impact of the OMOP CDM standardisation process on generating data
quality insights for a respiratory disease dataset. The source dataset, initially in a paper-based
format, was first converted to an electronic format. This historical dataset covers the years 2009
to 2024, containing 64 variables and 2,153 records.
The data underwent the standard Extract, Transform, and Load (ETL) process to convert it into
the OMOP CDM format. Following this transformation, the quality of the resulting OMOP
CDM instance was rigorously assessed. We utilised the Achilles tool, part of the OHDSI suite,
designed for evaluating the quality of OMOP CDM databases. Achilles performs validation
checks on the data based on key data quality dimensions, including conformance (adherence
to standards), completeness (presence of values), and plausibility (believability of values).
The application of the OMOP CDM transformation and the subsequent quality assessment
using Achilles successfully generated detailed insights into the dataset's quality. This
systematic evaluation facilitated the identification of specific data quality issues across the
conformance, completeness, and plausibility dimensions. Overall, the assessment conducted a
total of 2,344 checks, of which 2,269 passed and 75 failed, resulting in a 97% overall pass rate
for the Respiratory Diseases Inpatients data. It's also noted that 1439 out of 2269 passed checks
were deemed 'Not Applicable' due to empty tables or fields, and 39 of the 75 failed checks were
caused by SQL errors.
The standardisation of respiratory disease data using the OMOP CDM enabled a structured and
transparent evaluation of data quality. Through the application of the Achilles tool, this study
demonstrated the utility of OMOP CDM in generating meaningful data quality insights across
multiple dimensions. These findings highlight the model’s potential to enhance data readiness
and support evidence-based decision-making in respiratory disease management.
