OMOP Common Data Model Course

Overview

This one-day intensive course delivers a hands-on introduction to data standards and interoperability in healthcare, with a special emphasis on the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). This course is the ideal gateway for anyone interested in mastering health data transformation and interoperability, particularly using OMOP CDM as the foundation for modern, data-driven healthcare research and analysis.

Participants will gain practical skills and foundational knowledge to design, transform, and analyse health data using the OMOP CDM framework.

Led by Adjunct Professor Ngiam Kee Yuan (Head, Dept of Biomedical Informatics), this programme showcases real-world examples, tools, and the best practices for harmonizing disparate clinical datasets to support analytics and research collaborations. The curriculum is highly interactive, featuring live demonstrations and discussions relevant to digital health transformation and integrated data environments.

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Course Details

Fee: S$1,500 per person (inclusive of course resources). Prevailing GST applicable.
Certification: All participants will receive a Certificate of Participation.

Topic	Description
EHR Data Systems	Explores the relationship between EHR/EMR systems and secondary data use, focusing on how clinical data generated in healthcare settings is extracted and prepared for transformation into the OMOP CDM. Discusses key drivers and examples (e.g., EPIC, NEHR), illustrating pathways from operational health records to harmonized, research-ready datasets. Highlights the importance of data provenance, source data quality, and the technical challenges of moving real-world clinical data into a standardized analytic model.
Data De-identification and Anonymisation to Local and International Standards	Centres on privacy-preserving processes integral to OMOP ETL (Extract, Transform, Load) pipelines. Examines methods for de-identifying and anonymizing patient-level data as required for OMOP-compliant databases, referencing local (PDPA) and international (GDPR, HIPAA) requirements. Reviews practical strategies - data masking, tokenization, and advanced de-identification - for both structured fields and unstructured sources (notes, images), ensuring resulting OMOP datasets can be shared and analysed securely and ethically.
Data, Standards & Interoperability	Examination of the principles and technical frameworks underpinning seamless healthcare data exchange. Explores the different types of interoperability (physical, syntactic, and semantic); foundational clinical vocabularies and messaging standards (SNOMED CT, ICD, LOINC, RxNorm); and the practical integration of these standards within information systems for end-to-end data flows and analytics.
Data Transformation and Processing	Introduction to Common Data Models (CDMs) such as OMOP, covering the transformation, mapping, and harmonisation of heterogeneous healthcare data into standardized, research-ready formats. Includes practical instruction on data cleaning, quality assessment (e.g., OMOP DQD, Kahn framework), domain mapping (diagnoses, drugs, demographics), and workflow automation using state-of-the-art tools (USAGI, Athena).

Facilitator

Designation

Biography

Adjunct Professor Ngiam Kee Yuan

Head, Division of Biomedical Informatics, NUS Medicine

Head, AI Office, NUHS

Adjunct Professor, Department of Surgery, NUS Medicine

Adj Prof Ngiam is a Senior Consultant at the Division of General Surgery (Thyroid & Endocrine Surgery) at the National University Hospital, specialising in Thyroid and Endocrine surgical disorders. Following the completion of his Advanced Specialist Training in General Surgery, he was awarded a fellowship from the Royal College of Surgeons of Edinburgh and was accredited as a surgical specialist by the Specialist Accreditation Board, Singapore in 2012. He received the Higher Manpower Development Program Award in 2012 to complete a Fellowship in Metabolic and Endocrine Surgery in Taiwan. He received further training in robotic thyroid surgery at the Seoul National University Hospital, South Korea.

Adj Prof Ngiam’s special interests are in thyroid and endocrine surgery, robotic thyroid surgery, bariatric and metabolic surgery, advanced endoscopy and laparoscopic surgery. He was awarded the ExxonMobil-NUS Research Fellowship for Clinicians in 2007 and his research interests include parathyroid surgery, robotic thyroid surgery, obesity surgery for type 2 diabetes and thyroid cancer surgery.

Target Audience

Healthcare Professionals: Clinicians, nurses, and administrators interested in healthcare analytics or quality improvement.
Healthcare IT & Informatics Specialists: System architects, data managers, and IT professionals implementing or supporting OMOP.
Researchers & Data Scientists: Those using real-world data for epidemiological studies, outcomes research, or population health analytics.
Policy Makers & Regulators: Staff responsible for health data governance, compliance, or interoperability initiatives.
Graduate Students: Individuals in medicine, public health, bioinformatics, or related fields aiming to build OMOP CDM expertise.

Prior Knowledge Required

Audience Profile	Beneficial Background
Clinicians, Admins	Basics of healthcare systems
IT Professionals/Informatics	Databases, health data concepts
Researchers/Data Scientists	Data handling, CDM familiarity
Policy Makers/Regulators	Regulatory/health IT workflows
Graduate Students	Healthcare or data science background

*The course is designed to welcome learners from clinical and technical backgrounds. While general familiarity with healthcare or IT systems is helpful, all essential concepts are covered in the programme.

For more information or to enquire about the OMOP Course, please email: nusdbmi@nus.edu.sg.