This year’s event is once again being organised in collaboration with Health Data Research UK.

Please save the date !

The Observational Health Data Sciences and Informatics (OHDSI, pronounced “Odyssey”) is an international community of stakeholders dedicated to unlocking the value of health data through large-scale analytics. OHDSI promotes open science and collaboration in health data research with key focus on adoption of the OMOP Common Data Model, a global standard for harmonising data and facilitating federated analytics across institutions. Find out more about OHDSI.

Sponsorship

The organisers are grateful for sponsorship from InterSystems OMOP.

InterSystems OMOP, powered by InterSystems IRIS for Health, helps healthcare organisations unlock data for large-scale research and innovation. It enables seamless EHR access with daily updates, rapid deployment of OHDSI tools, and faster trial recruitment through timely insights. With a no-code pipeline and built-in data quality monitoring, it transforms EHR data into research-ready information quickly and reliably.

OHDSI UK Event

On Friday, 26th September 2025, the OHDSI UK National Node held its 3rd Annual Meeting at the Wellcome Building, London, with support from InterSystems.

The event featured a dynamic programme including lightning talks, poster sessions, and software demonstrations, showcasing national and international initiatives leveraging the OMOP CDM.

All event highlights — including abstracts from the lightning talks, posters, and software demos — are listed below.

Download the Agenda here

Agenda Highlights

Invited Presentations:

Rhoswyn Walker (Director of Strategy Health Data Research UK): Welcome

[YouTube video link]

Dani Prieto-Alhambra (NDORMS-University of Oxford) & Alex Knight (HDR UK): An update on OMOP CDM based activity in the UK and Europe

[YouTube Video Link]

Anna Ostropolets (Columbia University): OHDSI Standardized Vocabularies: a community effort 

[YouTube Video Link]

Gustav Klingstedt (Finnish Institute for Health and Welfare): FinOMOP The Finnish OHDSI National Node

[YouTube Video Link]

Shaun Rowark(NICE): NICE perspective: RWE and OMOP

[YouTube Video Link]

David Amadi ( Data Science Without Borders): Strengthening Data Science Capabilities in African Health Institutions

[YouTube Video Link]

Lightning Talks 1- Clinical Applications

YouTube Video Link

Hiba Junaid(Queen Mary University of London): Use of high-cost medicines in secondary care in the English NHS

Amani Al Balushi (HDR UK): Data-Driven Insights into the Lancashire Teaching Hospital Oral Antibiotic Prescribing and IV to Oral Switch: An analysis of Electronic Health Records using the OMOP Common Data Model to Inform Best Practice

Magali Ruffier (East of England Sub-National Secure Data Environment for R&D): Linking data across multiple hospitals using OMOP: Heart failure proof of concept in the East of England Secure Data Environment

David Akwuru( University of Limerick): Building a Federated, FAIR-Compliant Clinico Genomic Data Framework for Acute Myeloid Leukaemia in Ireland: Enabling Precision Oncology and Clonal Evolution Research.

Gianluca Fabiano (NDORMS: University of Oxford): Two Paths, One Goal: Validating Primary Care Resource Use and Costs in OMOP-Mapped vs Source CPRD Data in the UK

Lightning Talks 2- Data Standardisation and Open-Source Software

[YouTube Video Link]

Marta Pasikowska( DataLoch): Development of OMOP CDM for the Lothian population

Madina Hasan (University of Sheffield):Standardising National-NHS APC Data with OMOP CDM A Pilot for Secure Federated Research

Polina Talapova (SciForce): Bridging UK Drug Terminology and OMOP: NHS dm+d Refresh via OHDSI Community Contribution

Martí Català (NDORMS: University of Oxford): OmopConstructor: An R Package for Customising Observation Periods in OMOP CDM Analyses

Niko Möller-Grell(OHDSI): OMCP-A2A: LLM Agent collaboration on OMOP CDM question answering

Abstracts 

Find below the  accepted abstracts showcasing innovative research, methodological advances, and applications of the OMOP Common Data Model.

  1. Jasmine Handford (Kings College London) :Developing a strategy for the standardisation of Cancer Research UK funded data
  2. Gordon Milligan (University of Dundee): Reaching the full potential of applying a common data model to Scottish clinical data to support research feasibility queries
  3. Elin Rowlands (University of Oxford): Characterisation of use of antibiotics commonly associated with antimicrobial resistance in UK primary and hospital care.
  4. Elizaveta Gribaleva (Kings College London): Implementation of the OMOP Common Data Model in the UK-Irish Atopic Eczema Systemic Eczema Register (A-STAR UK) within a pan-European DREAM TO TREAT Atopic Dermatitis Framework
  5. Christian Cole (Health Informatics Centre, University of Dundee): Alleviate Pain Data Hub: Making Chronic Pain Data Discoverable and Reusable
  6. Cecilia Campanile ( University of Oxford): Characterisation of Data Sources in the Health data Real World Evidence OMOP Network (HERON UK)
  7. Qingze GU ( University of Oxford): Ethnicity‑resolved prevalence and disparity in 406 rare diseases across 62 million individuals in England, UK
  8. Jamil Foridi( Health Data Research UK): Cohort Discovery and the OMOP Common Data Model in the United Kingdom
  9. Ping Wu (The Institute of Cancer Research): Natural History and Outcomes in Early- and Late-Onset Colorectal Cancer: A Comparative Analysis
  10. Jeremiah Akintomide (University of Limerick): Standardisation of Irish Prostate Cancer Data Using OMOP CDM for ML[1]Based PIRADS3 Biopsy Decision Sup
  11. Arya Soman ( University of Limerick): Scoping Reviewing of R Packages for Statistical Analysis and Machine Learning in the OMOP CDM Research Setting
  12. Joe Zhang(Guy’s and St. Thomas’ Hospital): Full Stack Multi-Modal Data into a Federated Secure Data Environment for London
  13. Robert Goudie(University of Cambridge): Mapping Cambridge University Hospitals clinical data to OMOP
  14. David Akwuru (University of Limerick): Building a Federated, FAIR-Compliant Clinico-Genomic Data Framework for Acute Myeloid Leukaemia in Ireland: Enabling Precision Oncology and Clonal Evolution Research
  15. Amani Al Balushi (The University of Manchester): Data-Driven Insights into the Lancashire Teaching Hospital Oral Antibiotic Prescribing and IV to Oral Switch: An analysis of Electronic Health Records using the OMOP Common Data Model to Inform Best Practice
  16. Isobel Weinberg (Artificial Intelligence Centre for Value-Based Healthcare & King’s College London): Phenolab: A Streamlit application to integrate existing clinical definitions and phenotypes with a London Data Platform
  17. Shihao Shenzhang (King’s College London): OMCP: Model Context Protocol Servers for the OMOP Common Data Model
  18. Gareth Whiteley(Answer Digital on Behalf of Oxford University Hospital): Bridging NHS Data and OMOP CDM: Transforming Data Quality and Accessibility with the Oxford OMOP Data Mapper
  19. Marta Pasikowska (DataLoch, University of Edinburgh): Development of OMOP CDM for the Lothian population
  20. Andy South (University College London Hospitals): How UCLH moved to using OMOP as the first choice for providing hospital data to researchers.
  21. Rabia Khan (University of Oxford): Breast Cancer Inequities Using Real World Data Mapped to the OMOP CDM from Pakistan and the United Kingdom
  22. Niko Möller-Grell (King’s College London): OMCP-A2A: LLM Agent collaboration on OMOP CDM question answering
  23. Polina Talapova (SciForce): Bridging UK Drug Terminology and OMOP: NHS dm+d Refresh via OHDSI Community Contribution
  24. Gianluca Fabiano (University of Oxford): Two Paths, One Goal: Validating Primary Care Resource Use and Costs in OMOP[1]Mapped vs Source CPRD Data in the UK
  25. Alex Knight( Health Data Research UK): Establishing a Health Data Research UK OMOP Network with hospital and primary care data: HERON-UK
  26. Milou Brand(IQVIA): Characterizing Acute STEMI Patients Across Multi-Country Real-World Data Sources: A Comparative Analysis
  27. David Morrison (TwinsUK (King’s College London): Using OMOP CDM within TwinsUK Phenotype Management System
  28. Sarah Seager(EPAM): Enabling quicker insights from RWD with the Prometheus Federated Data Network Platform
  29. Esmond Urwin(University of Nottingham): Carrot Top – A tool for OMOP Transforma3on Reliability Support
  30. Michael Ochola (African Population and Health Research Center): Standardizing Longitudinal Mental Health Data to OMOP CDM Using a Staging Database Approach centered on DDI-Lifecyle standard
  31. Melanie Philofsky (EPAM Systems): OHDSI in Action: A Global Collaboration to Develop Sustainable Solutions for Representing Race and Ethnicity Data
  32. Peter Hoffmann(D4L data4life Asia Limited): Identifying High Data-completeness Patients in Data2Evidence Platform
  33. Renata Silva(MTG Research and Development Lab): Bridging the Gaps in Clinical Timelines: Unlocking OMOP’s Relationship Potential with FACT_RELATIONSHIP
  34. Elin Rowlands (University of Oxford): Characterisation of the Presentation of Lung Cancer: a Multinational European Cohort Study
  35. Tri Thien Nguyen (University of Nottingam): Carrot and Lettuce: A Modular Architecture for AI-Assisted OMOP Mapping
  36. Marti Catala (University of Oxford): OmopConstructor: An R Package for Customising Observation Periods in OMOP CDM Analyses
  37. Francesca Chiara Bladt (Barts Health NHS): Maternal and foetal health outcomes in pregnant trauma patients
  38. Magali Ruffier (Health Innovation East): East of England Secure Data Environment Heart Failure Data OMOP mapping
  39. Litong Jiang (Chinese Academy of Sciences) :OMOP Navigator: An Open-Source Natural Language Interface for Federated Queries Across OMOP CDM Database
  40. Madina Hasan( The University of Sheffield): Standardising national-NHS APC Data with OMOP CDM: A Pilot for Secure Federated Research
  41. Marta Alcalde-Herraiz (University of Oxford): Software demonstration: PhenotypeR – an R package for evaluating computable phenotypes applied to the OMOP CDM

Posters

  1. Renata Silva(MTG Research and Development Lab): Bridging the Gaps in Clinical Timelines: Unlocking OMOP’s Relationship Potential with FACT_RELATIONSHIP
  2. Quinze Gu (University of Oxford):Ethnicity-resolved prevalence of 406 rare diseases in 62 million individuals in England:a national cross-sectional study
  3. Alex Knight (Health Data Research UK): Establishing the Health Data Research UK OMOP Network: HERON-UK
  4. Carole Morris (University of Dundee):Reaching the full potential of applying a common data model to Scottish clinical data to support research feasibility queries
  5. Jeremiah Akintomide (University of Limerick): All Island Research Hub for Federated Analysis of Cancer Data
  6. Zhi Min(Data4life):Identifying High Data-completeness Patients in Data2Evidence Platform
  7. Rabia Khan(University of Oxford): Understanding Breast Cancer Inequities Using Real World Data Mapped to the OMOP CDM from Pakistan and the United Kingdom
  8. Man Fung Tsoi (Kings College London):Implementation of the OMOP Common Data Model in the UK-Irish Atopic Eczema Systemic Eczema Register (A-STAR UK) within a pan-European DREAM TO TREAT Atopic Dermatitis Framework
  9. Ping Wu (The Institute of Cancer Research): Natural History and Outcomes in Early- and Late-Onset Colorectal Cancer: A Comparative Analysis
  10. Denys Kaduk (SciForce): No more biscuit-vs-cookie for drug codes
  11. Jasmine Handford(Kings College London): Developing a strategy for the standardisation of Cancer Research UK funded data
  12. Jamil Foridi ( Health Data Research UK):Cohort Discovery and the OMOP Common Data Model
  13. Shihao Shenzhang (Kings College London): OMCP: Model Context Protocol servers for the OMOP Common Data Model
  14. Elin Rowlands (University of Oxford): The use of antibiotics with high resistance potential in primary and secondary care: a HERON-UK network study
  15. Elin Rowlands (University of Oxford): Incidence Trends and Characterisation of Lung Cancer: A Multinational European Cohort Study
  16. Fabiano G (University of Oxford): Two Paths, One Goal: Validating Primary Care Resource Use and Costs in OMOP-Mapped vs Source CPRD Data in the UK
  17. Anna Calvacante (University College London Hospitals NHS Trust):How UCLH moved to using OMOP as the first choice for providing hospital data to researchers
  18. Magali Ruffier (Health Innovation East): Transforming Heart Failure Data in the Secure Data Environment into the OMOP Common Data Model v5.4
  19. Cecilia Campanile (University of Oxford): Characterisation of Data Sources in the HEalthdata Real World Evidence OMOP Network (HERON UK)
  20. Robert Goudie( University of Cambridge): Mapping Cambridge University Hospitals clinical data to OMOP
  21. Madina Hassan ( University of Sheffied): Standardising National-NHS APC Data with OMOP CDM A Pilot for Secure Federated Research
  22. Litong Jiang(Chinese Academy of Sciences): OMOP Navigator: An Open-Source Natural Language Tool for Federated Queries with Text2SQL —Complementing ATLAS for OMOP CDM Exploration
  23. Melanie Philofsky (OHDSI): OHDSI in Action: A Global Collaboration to Develop Sustainable Solutions for Representing Race and Ethnicity Data
  24. David Akwuru(University of Limerick): All Island Research Hub for Federated Analysis of Cancer Data
  25. David Morrison (TwinsUK) :Phenobase Data Management: Load, Manage & Disseminate Twins Research Data
  26. Esmond Urwin (University of Nottingham): Carrot Top –A tool for OMOP Transformation Reliability Support
  27. Al Balushi (University of Manchester): Data-Driven Insights into the Lancashire Teaching Hospital Oral Antibiotic Prescribing and IV to Oral Switch: An analysis of Electronic Health Records using the OMOP Common Data Model to Inform Best Practice
  28. Gordon Milligan (University of Dundee): Alleviate Pain Data Hub: Making Chronic Pain Data Discoverable and Reusable
  29. Melanie Philofsky (OHDSI): Enabling quicker insights from RWD with the Prometheus Federated Data Network Platform
  30. Francesca Baldt (Barts Health NHS Trust):Maternal & Foetal Health Outcomes in Pregnant Trauma Patients
  31. Arya Soman (University of Limerick): Reviewing R packages for Statistical Analysis and Machine Learning in the OMOP CDM Research Settings.
  32. Milou Brand (IQVIA):Characterizing Acute STEMI Patients Across Multi-Country Real-World Data Sources: A Comparative Analysis
  33. Joe Zhang (Guy’s and St. Thomas’ NHS Foundation Trust): Full Stack Multi-Modal NHS Trust Data into the Federated Learning & Interoperability Platform for the OneLondon Secure Data Environment
  34. Isobel Weinberg (Guy’s and St. Thomas’ Hospital): Phenolab: A Streamlit application to integrate and version clinical definitions and phenotypes within the OneLondon Data Platform
  35. Marta Pasikowska (DataLoch): Development of OMOP CDM for the Lothian population
  36. Niko Möller-Grell ( King’s College London): OMCP-A2A: LLM Agent Collaboration on OMOP CDM Question Answering

Software Demos

The software demonstrations provided attendees with hands-on insights into tools developed to facilitate OMOP CDM usage:

Marta Alcalde-Herraiz: PhenotypeR: An R Package for Evaluating Computable Phenotypes Applied to the OMOP CDM.

Tri Thien Nguyen: Carrot and Lettuce: A Modular Architecture for AI-Assisted OMOP Mapping

Jamil Shah Foridi: Cohort Discovery

Peter Hoffmann: Identifying High Data-completeness Patients in Data2Evidence Platform

Gareth Whiteley: Oxford OMOP Mapper : An Open-source ETL Platform to help you with your mappings

Litong Jiang: OMOP Navigator: An Open-Source Natural Language Tool for Federated Queries with Text2SQL—Complementing ATLAS for OMOP CDM Exploration

Martí Català: OmopConstructor: An R Package for CustomisingObservation Periods in OMOP CDM Analyses

David Morrison:TwinsUK Phenobase – Medical Research Data Management

Questions?

Links:

OHDSI UK 2024: https://ukhealthdata.org/news/ohdsi-uk-2024/

OHDSI UK: https://www.ohdsi-europe.org/index.php/national-nodes/uk

OHDSI Europe: https://www.ohdsi-europe.org/

 

 

OHDSI UK 2025 Lightning Talks 1: Innovating Clinical Applications

The OHDSI UK 2025 Lightning Talks – Clinical Applications session showcases cutting-edge research using real-world health data to drive innovation in clinical practice, drug use, and precision medicine.