Use of the OMOP data model
We want to be a part of this development, and the Cancer Registry has initiated and received funding to standardize the registry's data to the OMOP - Observational Medical Outcomes Partnership data model.
OMOP has been developed through an international project that works to standardize data into a common format. This makes it easier for data to be used for collaboration, large-scale analyses, and sharing of sophisticated tools and methods. Variations in the format of health data make it challenging to collaborate across organizations and nations. With a common data model, the basis for sharing is improved. The model is based on established code systems and assigns variables and structure to the OMOP model. Such a generic data model supports standardized analyses and sharing of analysis scripts across research networks. A key concept is that the data remains locally stored, and only statistics/aggregated data leave the Cancer Registry.
In the future, we hope to make data available based on this data model.
The figure provides an overview of which of the Cancer Registry's data has been mapped or is planned to be mapped to OMOP. All core variables have been mapped to the data model, and selected variables from the quality registries will be mapped in specific projects (SACT = Systemic anti-cancer treatment, PROs = Patient reported outcomes, dx = Diagnosis, Clinical registry = quality registries).
Ongoing OMOP-prosjects
FLORENCE: Federated Learning on Cancer Data
FLORENCE (Federated learning using OMOP modelling of health data for elevating colorectal cancer care in the Nordic countries) is an interregional collaboration that, through the use of hospitals and registry data harmonised to the OMOP data model, will provide clinicians in colorectal cancer with a better basis for their assessments.
Background
Colorectal cancer is the second most common cancer in Norway when looking at both sexes. In 2021, 4550 men and women were diagnosed with colorectal cancer. 1 in 4 colorectal cancer patients experience complications after surgery, and the same proportion experience a recurrence of the cancer within 3 years.
Better utilisation of registry and hospital data can give clinicians and patients a better basis for treatment and prevention of complications after surgery. Mapping data to the OMOP data model makes it possible to analyze data across countries without moving the data itself across borders.
Purpose
In this project, we want to lay the foundation for an IT tool (decision support tool) that can be used by specialists to help with treatment choices for colorectal cancer patients.
The project is a collaboration with the Center for Surgical Science (CSS) at Zealand University Hospital in Denmark, and by using hospital and registry data from both countries, we will form the basis for a new decision support tool. In addition, it collaborates with Lund University in Sweden, Computerome at DTU and the unit for research projects at Zealand University Hospital.
In order to create a decision support tool that uses basic data from several countries, it is important that the data on which the analyses are based are comparable. As a result, data must be standardised and harmonised across countries before it can be compared. To achieve this, we will use a generic data model called OMOP-CDM from Observational Health Data Sciences and Informatics (OHDSI). The project will also use synthetic data for testing and development of algorithms that can be regularly improved via federated learning.
We have received funding for the project together with the project partners from Interreg Øresund-Kattegat-Skagerak (ØKS). Interreg ØKS is a European regional development fund that supports projects that focus on innovation, a green transition, transport or a borderless labour market.
Privacy
Privacy is safeguarded, among other things, by using a common data model so that algorithms can be shared instead of health information. The local data on a hospital/registry will be used to train each version of the model, or decision support tool in this case. Changes in the model between each site can be shared, and in this way all treatment centres will have an equally good decision-making tool.
See informational video about cooperation in the FLORENCE project.
About the project
Project leader: Jan F. Nygård
Project staff: Espen Enerly, Kristin Oterholt Knudsen, Tina Sture, Siri Larønningen and Marie Gulla.
Partners:
Vegar Johansen Dagenborg at OUS
Ismail Gögenur, project manager, etc. at the Center for Surgical Science, Zealand University Hospital, Denmark
Intelligent Systems Laboratory at Lund University, Sweden
Enheden for Research Projects and Clinical Optimization (RePCO), Zealand University Hospital, Denmark
Computerome, Technical University of Denmark (DTU), Denmark
Funding: The study is financed by Interreg Øresund-Kattegat-Skagerak (ØKS).
Project period: 2022-2025
Blueberry
How can we facilitate the sharing of information about rare cancer diagnoses across Europe while still safeguarding privacy effectively? This is the issue that the BlueBerry project aims to address.
BlueBerry - Co-creating a Blueprint for Building a sustainable, effective, and scalable Euracan Rare Cancer RegistrY - is an infrastructure project with participants from 9 institutions in 7 European countries.
The main goal of the project is to utilize new infrastructure and methods to make relevant information on rare cancer types available and shareable across Europe in a secure and privacy-friendly manner.
The European network for rare adult cancer diseases - EURACAN - laid the foundation for this work through its STARTER project. BlueBerry takes this work further and aims to establish a technical, semantic, and legal infrastructure for a federated registry where only anonymous, aggregated data will be shared in the network, with privacy-sensitive information stored in secure areas within the responsible institutions in each country.
The project utilizes OMOP for data model and semantic standardization across different nodes and Vantage6 as the federated network structure.
Blueberry is now really taking off
Other OMOP projects
As part of the EHDEN collaboration, the Cancer Registry is invited to participate in OMOP projects that are based on collecting cancer statistics based on the OMOP model from data partners. For example, we have accepted to participate in such a project focusing on cancer survival, coordinated by Oxford University and NICE (National Institute for Health and Care Excellence). OMOP EHDEN-prosjektet
Collaborators
The Cancer Registry collaborates with both national and international entities in relation to the work on the OMOP data model.
International collaborators
The Observational Health Data Sciences and Informatics program (OHDSI)
The international organization OHDSI (The Observational Health Data Sciences and Informatics) leads the development of the OMOP data model and related software. The Cancer Registry participates in working groups related to oncology and registries. Observational health data sciences and informatics
European Health Data & Evidence Network
In Europe, the EHDEN consortium (European Health Data & Evidence Network) is a major driver for OMOP. The Cancer Registry is a data partner in EHDEN and is findable in their catalog of European databases harmonized to OMOP. EHDEN has also contributed funding to the Cancer Registry's OMOP work (OMOP-EHDEN). European Health Data and Evidence Network
Other international collaborators
In addition to formal project-based research collaborations with organizations such as the Netherlands Cancer Registry at IKNL (Blueberry) and the Center for Surgical Sciences at Zealand University Hospital (FLORENCE), we have informal collaborations with other European cancer registries, including the Cancer Registry of Luxembourg, Geneva Cancer Registry, Vaud Cancer Registry, and Geneva Cancer Registry (Switzerland). There is also a formal collaboration with several European institutions through the EU Horizon-project IDEA4RC.
National collaborators
In the spring of 2021, together with Norwegian partners, we established an informal OMOP forum for sharing experiences, insights and discussing aspects of implementing and carrying out research using OMOP in Norway. Participants from the clinical data warehouse at Oslo University Hospital, the Department of Pharmacy at the University of Oslo, the Directorate for e-Health and the Cancer Registry regularly take part in the forum.
In autumn of 2023, we formalized this collaboration as a national node in the European chapter of OHDSI. The national node is open to participation from others who are interested in OMOP in Norway.
How to become a member of the national node
To become a member of the national node, contact Espen Enerly or Siri Larønningen.
To access Teams channels for the national node and working groups in the OHDSI network, you must register for an OHDSI Teams user via this form. After you have been assigned a user, you must use this form to register for the Norwegian node (select "Europe" in the chapter menu) and for international working groups you wish to join.
Dissemination
OHDSI Europe 2023
Lessons learned from four population-based cancer registries: Mapping of ICD-O-3 codes to standard concepts. Peter Prinsen, Maaike van Swieten, Chiara Attanasio, Espen Enerly, Siri Larønningen, Elisabetta Rapiti, David Marcic, Evelyne Fournier, Pierre Künzli, Michael Schnell, Sophie Couffignal, Claudine Backes.
Standardizing European sarcoma registry data to the OMOP Common Data Model. Maaike van Swieten, Vittoria Ramella, Anna Alloni, Matteo Gabetta, Peter Prinsen, Chiara Attanasio, Espen Enerly, Siri Larønningen, Roberto Lillini, Paolo Lasalvia, Joanna Szkandera, Stefan Janisch, Andreas Muth, Emelie Styring, Julien Bollard, Annalisa Trama, Gijs Geleijnse.
ENCR-IARC 2023
Lessons learned from five cancer registries: mapping of icd-o-3 codes to omop-common data model concepts. Peter Prinsen, Maaike van Swieten, Chiara Attanasio, Espen Enerly, Siri Larønningen, Elisabetta Rapiti, Evelyne Fournier, David Marcic, Pierre Künzli, Michael Schnell, Sophie Couffignal, Tapio Niemi, Eloise Martin, Jean-Luc Bulliard, Claudine Backes.
ANCR 2022
Standardizing Norwegian Cancer Registry data to the OMOP common data model. Siri Larønningen, Kristin Oterholt Knudsen, Gintaras Pikelis, Marie Gulla, Petter Topp, Vlatko Duric, Björn Eklund, Jan Nygård, Espen Enerly.
More info:
The organization OHDSI (The Observational Health Data Sciences and Informatics) is behind the international OMOP collaboration. Observational Health Data Sciences and Informatics.
In Europe, the consortium EHDEN (European Health Data & Evidence Network) is a major driver for OMOP. European Health Data and Evicence Network.
Several employees at Kreftregisteret are part of projects based on the OMOP model, including researchers, advisors, and IT experts.
Contact person: Espen Enerly.
