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Home > All articles > The OMOP (Observational Medical Outcomes Partnership) Common Data Model (CDM) facilitates the utilization of healthcare data – Medaffcon was involved in a Finnish OMOP pilot study
The OMOP (Observational Medical Outcomes Partnership) Common Data Model (CDM) facilitates the utilization of healthcare data – Medaffcon was involved in a Finnish OMOP pilot study
In healthcare, there is a vast amount of data being generated and processed. Sometimes, its utilization is cumbersome because the data is scattered, and its analysis requires a lot of preprocessing.
The OMOP data model (Observational Medical Outcomes Partnership) seeks to address this challenge. In Finland, the utilization of OMOP was explored in a pilot study, in which Medaffcon also participated.
The Observational Health Data Sciences and Informatics (OHDSI) organization drives the utilization of the OMOP data model. OMOP standardizes healthcare data and enables the uniform processing of data from various sources.
“The use of the OMOP data model would significantly accelerate projects by streamlining the preprocessing of the data. Currently, preprocessing takes up to half of the working hours in research projects, sometimes exceeding half of the total time. When the data is in a consistent format, creating reusable analysis codes and conducting reproducible studies becomes more straightforward. Also, the open source nature of the data model simplifies leveraging artificial intelligence for querying and analyzing the data.”, says Medaffcon’s Sr. Data Scientist Juhani Aakko.
OMOP (The Observational Medical Outcomes Partnership) data model offers smoother data utilization for health care data
One of the benefits of the OMOP(The Observational Medical Outcomes Partnership)data model is that studies could be more easily conducted across different hospitals and countries. Authorities would also benefit from smoother data utilization.
In Finland, the utilization of the OMOP(Observational Medical Outcomes Partnership) data model has been tested in three university hospitals, HUS (Helsinki University Hospital), the Pirkanmaa and Varsinais-Suomi Wellbeing Services County and Fimea(The Finnish Medicines Agency) in a pilot project funded by Sitra(The Future Fund).
In the pilot, Fimea(The Finnish Medicines Agency) defined the information needs related to the use of new medications. University hospitals evaluated whether these information needs could be met by utilizing the hospital OMOP(The Observational Medical Outcomes Partnership) databases to extract the necessary information and to produce the aggregated results.
OMOP data model pilot project in Finland with Medaffcon
The pilot examined three different use cases related to the treatment of SMA (spinal muscular atrophy) and multiple myeloma, as well as the use of CART therapies (CAR T-cell therapies). The goal was to assess the suitability of the piloted model for practical use. Medaffcon conducted the technical implementation based on data from Helsinki University Hospital for the questions related to the treatment of multiple myeloma and the use of CART therapies.
“We analyzed data from the OMOP (The Observational Medical Outcomes Partnership) database and assessed whether it could answer the research questions and conducted the analysis. The analysis codes were shared with the collaborators from Pirkanmaa (PIRHA) and Varsinais-Suomi (VARHA) Wellbeing Services County who could then perform the same analysis easily from their OMOP databases.”, Medaffcon’s Sr. Data Scientist Juhani Aakko says.
Juhani Aakko considers the OMOP (The Observational Medical Outcomes Partnership) data model as an important step towards better utilization of healthcare data. Its broader usage would support healthcare organizations in research and data-driven decision-making, as well as authorities and pharmaceutical companies in utilizing health data to support patients’ better and impactful treatment.
Medaffcon´s experiences from OMOP pilot project
How does OMOP (The Observational Medical Outcomes Partnership) Common Data Model (CDM) facilitate the use of health data?
Data becomes available in a standardized format and the time spent on data preprocessing is reduced
Data is easier to utilize and share among different stakeholders
OMOP enables studies to be more easily reproducible and comparable
The data model facilitates collaboration and innovation between different stakeholders
Federated analyses can be performed without moving data between different hospital data lakes
Juhani joined Medaffcon in October 2020 as a data scientist. Prior to joining Medaffcon, Juhani has worked as a data scientist in a global IT company as well as a scientist at the University of Turku in the Medical Bioinformatics Centre (MBC) and Functional Foods Forum (FFF). Juhani holds a Doctor of Science in Technology degree (2017) and the topic of his thesis was the development of human gut microbiota in early infancy.
Juhani has experience from applying statistical and machine learning methods in medicine and due to his multidisciplinary background, he can easily communicate with people with varied expertise ranging from clinicians to IT-professionals. “Knowledge management and business intelligence have become hot topics also in the social and healthcare sectors. It is very interesting to be involved in harnessing the vast amounts of data available in the systems to actual usable information to support decision making. Both traditional statistics as well as advanced analytics and artificial intelligence will be in a key role in this job.”
Mariann joined Medaffcon’s team in 2016 after finishing her PhD. The transition to real world evidence (RWE) research was a natural continuum to her previous research career. Through RWE studies, she has had the privilege to gain a broad insight into working with different stakeholders within the healthcare field. The vast proportion of her days goes towards interacting with clients, planning and performing RWE studies, and supporting Medaffcon’s RWE team. Subjects that keep her work interesting are the vast variability of customers and projects, problem-solving, and interacting with people.
“The number of RWE studies has increased since stakeholders within the healthcare industry have an increasing demand for knowledge-based decision making tools that need to be fulfilled. The future, therefore, has an ever-increasing emphasis on RWE”.
Iiro joined Medaffcon in March 2017 as a Biostatistician. For the preceding four years, he has worked as a research assistant in an academic study group, analyzing clinical and genetic patient data. Iiro holds a Master of Science degree in Technology in Bioinformation Technology.
Iiro’s strengths include his strong expertise in statistics and data-analysis, hands-on experience in working with sensitive patient data, and strong interdisciplinary communication skills with experts from various fields. In the field, he is particularly interested in the large data amounts made available with the revolution of technology and how the information received such data can potentially be utilized to draw concrete conclusions, both in order to understand the nature of diseases and to advance the goals of the pharmaceutical industry and patient treatment.
“Machine learning and AI-based solutions will have a major impact on the healthcare sector now and in the future. However, effectively utilizing the already collected and available health-data will have a higher importance in order to improve health-care”.
