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We are living in the information age and the flux of information has become the driving force of social evolution. Can you imagine how our understanding of COVID-19 would be without the sharing of information between academic, industry and public health entities? During the ongoing public health crisis, it has become clear that open data sharing at international levels has helped us to improve in many ways. In that sense, the key purpose of using data standards is to enable the exchange of electronic health information from one system with other systems without special effort. But effective data sharing requires considerable efforts to get all stakeholders involved in a dialogue to fully understand each other’s assumptions and expectations. Mainly defining ‘what to collect’ and deciding ‘how to represent what is collected’ are the top two questions to address.
Nowadays, there are vast amounts of electronic health data available for use in clinical research. In the last decade, implementation of electronic health records (EHRs) has become widespread across healthcare systems and regulators have been encouraging the creation of disease registries. This proliferation has come along with a health data collection landscape that shows a group of systems that are often duplicative and uncoordinated, most of the times with very different formats and standards, or even without. On top of that, the list of data sources continues to enlarge in the entire healthcare ecosystem (clinical trails, biorepositories, imaging databases, or medical and consumer devices). For that reason, deciding which data to use in data standardization projects is critical and this decision will clearly depend on the purpose we want to give the standardized data. Retrospective and prospective research studies, clinical decision making, manage population health, or conduct proactive safety surveillance are good examples where data standards can make the difference.
How to represent what is collected is not an easy question to answer either. Among all standardization methodologies that have been developed to date for clinical purposes, most are organizing data in a common format called ‘common data model’ (CDM) and translating native terminology to a standardized vocabulary. Common data model explicitly determines and organizes the structure of data elements and determine the relationships among them. Once the chosen data is migrated from native formats to universal standard CDMs, information can be exchanged, pooled, shared, or stored from multiple sources.
An example of the success of data standardization is the Sentinel Initiative launched in 2007 by the United States Food and Drug Administration (FDA). Their goal was the automated monitoring of product safety information from multiple health data sources (EHRs, registries and administrative claims databases). Sentinel uses a distributed data approach in which the participating data partners (medical centres, hospitals, health insurance companies) transform their data from native formats to the Sentinel CDM. This initiative protects public health and helps inform healthcare providers about decision-making for patients. Did you know that Sentinel initiative discovered that medical doctors were confusing two drugs with similar name but for distinct conditions? They recommended the name change for one of the medications, and this was only possible thanks to the analysis of standardized data from multiple sources.
Another more recent successful initiative is the European Health Data & Evidence Network (EHDEN), which aims to collaborate with diverse institutions and data partners to enable large-scale analysis of health data in Europe. The goal of EHDEN is to build a federated data network where source data is locally harmonised to the OMOP CMD. This partnership allows access to the standardised data of circa 100 million EU citizens and is helping to generate medical evidence that would not otherwise be possible without large-scale collaboration, such as in rare diseases or in the current COVID-19 pandemic.
Undoubtedly, dialogue between regulators, companies, and data holders to understand barriers and opportunities can facilitate the development of new business models through collaboration. Furthermore, the use of data standards inside the health ecosystem and its intrinsic interoperability capacity is helping to improve our medicines and treatments today. Digital health transformation is necessarily becoming more collaborative and standardized and has the potential to evolve from disease-centred models to more realistic patient-centred models.
Our expert knowledge at Modis enables us to build an impactful and strong relationship with our clients and their stakeholders in the healthcare ecosystem. Our experts can act as interlocutors offering customized solutions for project management.
– Javier Cordoba, Project Manager