A Guide to the OMOP Common Data Model

The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) is a standardized framework designed by the Observational Health Data Sciences and Informatics (OHDSI) community. This open-science community aims to improve the quality of healthcare by providing guidelines for a more harmonized approach to data science.
Personalized healthcare is characterized by tailoring treatments to each individual patient’s needs. Despite evident benefits, implementing such a system is not straightforward. The ATHENA project consortium has successfully devised a set of building blocks to address the challenges associated. Both technological and governance tools were developed that can now be used on a larger scale to catalyze the transition towards personalized medicine and care.
The emergence of artificial intelligence and machine learning accelerates research and innovation across various sectors, particularly in healthcare. The potential for the development of innovative diagnostic tools and therapies based on insights from health data is limitless. However, progress should never compromise the privacy of patients. It’s a delicate balance that is essential to maintain.
The patient’s journey in a healthcare setting is influenced by factors beyond the mere medical aspects of the case. Local variations in care procedures among hospitals and caregivers enable benchmarking and adaptation of practices to optimize outcomes. For effective process modeling, it’s essential to collect data and extract insights. To optimize this process efficiently, collaborative efforts should explore alternative data sources and implement novel tools.
After a therapeutic drug hits the market, it is crucial to continuously gather, analyze, and report data regarding its safety and potential side effects, a practice known as pharmacovigilance. Unlike clinical trials, this involves real-world data (RWD), presenting unique challenges in terms of both quality and quantity. The Belgian BELpREG project seeks to employ RWD for monitoring drug utilization during pregnancy and investigating potential safety implications for both maternal and child health. This initiative holds great promise, although it faces substantial hurdles on its path to success.
BioWin and MEDVIA are two Belgian organizations which support and promote health innovation in Wallonia and Flanders. This year, these two clusters have come together to jointly organize the event Science for health on the topic of ‘biology meets technology’. In this dual interview, Ann Van Gysel (MEDVIA) and Sylvie Ponchaut (BioWin) discuss the differences between their regions and how they are working to improve cross-border collaboration in Belgium.
Patient data is a treasure trove of information, vital to tailoring individual care pathways and generating profound insights to enhance healthcare more broadly. However, achieving interoperability among the diverse systems employed by various healthcare providers remains a critical challenge. Data standards like FHIR can help to streamline the secure exchange and seamless integration of healthcare data across these different systems. This article will take you through the FHIR framework and how it could be used by your organization.
This year, Science for health will explore the inspiring innovations at the interface of biology and technology. The event will bring together academics, industry leaders, and policymakers to explore the newest treatment platforms and factories of the future. BioVox spoke to Werner Verbiest, member of the event’s Scientific Committee, about the topic and why collaboration is so key right now.
Studies of a drug's effectiveness and safety don’t end with clinical trials – they extend beyond market access when the true value of a drug is demonstrated in a real-world setting. Based on this information, factors such as availability, pricing, and reimbursement are adjusted. To study the actual worth of a treatment, we require real-world data (RWD) from a large and diverse patient population. By actively sharing this information with stakeholders, we can fuel further research and innovation, and even help to inform decision-making on a population level. But in order to unlock the full potential of our patient data, all members of the ecosystem have to work together.
Belgian Prof. Jan Rabaey has spent the past 35 years conducting pioneering tech research at the University of California at Berkeley. His groundbreaking electronics work has been used in a range of modern devices, including the iPad, brain-computer interfaces, and wireless sensor nodes used for the Internet of Things. The ongoing theme of his work has been miniaturization and connection, which he is now using to link technology and people like never before.
Rapidly spreading diseases and unpredictable global shifts cause increased strain to the pharmaceutical supply chain, jeopardizing the secure delivery of drugs to those who are in need. Artificial intelligence can maximize efficacy of the value chain and allow optimal responses to changing market demands. Two experts share insights into the possibilities and hurdles associated with integration of machine learning tools into the pharmaceutical supply chain.
Patient data can be extremely helpful for improving health. If properly utilized, valuable insights can be generated, leading to better care for all. To do this, we need a way to overcome the incompatibility of information systems used by different healthcare providers, so that we are able to conduct large-scale research projects using data from multiple sources. The key to this lies in implementing data standards, such as OMOP and FHIR – specific methods developed for the storage, sharing, and interpretation of healthcare data.
Many drugs fail clinical trials, often because preclinical animal models fall short of replicating human physiology. To improve animal welfare, speed up drug development, and reduce costs, we need to rely less on animal models, while also minimizing the number of failures early in the drug development process. Artificial intelligence and machine learning are powerful tools that can help us achieve these goals by predicting a drug's efficacy, safety, and uptake in preclinical studies. These technologies can help researchers to make informed decisions and optimize testing strategies, improving drug development for both animals and people.
Clinical drug development is a challenging endeavor, but help is at hand! From trial site selection, to patient recruitment, to endpoint characterization – data science integration can help to overcome bottlenecks and improve efficiency in clinical development by generating unique insights to help guide study design and operations. For this strategy to be used to its full potential in Belgium, local partners should optimize data governance and quality, and improve collaboration. This would help the country maintain its status as a leading location for clinical trials.
Unprecedented collaborations between different pharmaceutical companies have resulted in extraordinary progress for HIV patients over the past four decades. From the first ever treatments, to single pills and now even long-acting injections, treatment options have come a long way. In this interview, Dr. Theresa Pattery (Head of Disease Management Programs at Janssen Pharmaceutica) tells us of this long journey and talks about the role of drones and phones in the world-wide fight against HIV.