How IDMP as a business-driven initiative can advance robust data flow

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by Jared Kimble

Now that we are entering into a new phase of discovery, the Identification of Medicinal Products (IDMP) has once again become a priority for the European Medicines Agency (EMA). After many delays and other scheduling problems, the industry must turn its attention to the standard but, rather than view it as just a regulatory compliance requirement, life sciences companies should embrace it as an opportunity to create a more robust, standardized data-driven environment.

At its core, IDMP is a collection of five standards developed within the International Organization for Standardization (ISO) to uniquely identify medicinal products. Use and information exchange of the standard may take on many forms, including a way to communicate with the health authority submission requirements as well as across industry functions, and ultimately across the life sciences value chain.

Having a common data model — IDMP — will facilitate that cross-functional communication by enabling different systems to be connected. To achieve this objective, companies will need to establish a master data management system that will become the central source, or golden record, with which all systems can communicate. By building a connection from each system to a master data management system, companies are assured that data across all those networks is up to date with the master.  And the way this data will be exchanged is by leveraging Health Level Seven International’s (HL7) Fast Healthcare Interoperability Resources (FHIR) standard for data exchange, the same way as the data is exchanged with the regulatory agencies. This would allow data harmonization and communication across separate functional areas — regulatory, pharmacovigilance, manufacturing, clinical and commercial.

Making the leap

To achieve this transformational environment, companies will first need to recognize the benefit of leveraging IDMP and FHIR for better data sharing and embrace a connected cross-functional data model. Next, they will need to assess the systems currently in place, or in use, if any, and determine how many of these could be used in a common data model such as FHIR.

The task of building a connected environment may be daunting at first, but one approach is to draft a minimum viable product plan — a technique that allows those leading the project to demonstrate proof of concept through the development of core functionalities. This approach allows the company to phase adoption, adding various systems over time into the data-driven environment.

The longer-term objectives are to get to a fully compliant IDMP data model, to a point where all needed systems are fully connected and making use of standards to their fullest extent. If companies have a connected environment where clinical, regulatory, manufacturing and safety are all connected, the workflows can be significantly improved. Consider, for example, the process of managing product variations. This may require updates in manufacturing or addressing a safety concern, which would then need to be reviewed and submitted to a regulatory agency. Having a common data model allows all the information in a variation to be better assessed and makes it easier for the health authorities to understand the impact of changes to a particular product, which speeds up the submission process.

Having that broader oversight, therefore, helps both the company and the regulatory authorities, and, ultimately, the patient who is taking the drug, because it helps to improve data accuracy.

A health authority objective

Data quality, discoverability and accessibility are key priorities for regulatory authorities, which are keenly focused on advancing FAIR principles (findable, accessible, interoperable, reusable [data]), and using standards such as FHIR to establish a framework that describes and improves data quality. For example, the European Medicines Agency recently announced plans to create an EU platform to access and analyze data across the European Union, known as DARWIN (Data Analysis and Real-World Interrogation Network). The objective is to create a network of databases of verified content to assist with regulatory decision making. The goal for data acceptability is to encourage and promote the use of IDMP for regulatory submissions of big data, including real-world data (RWD).

Concerted efforts are also in place to align standards, with EMA and the U.S. Food and Drug Administration working together to incorporate IDMP standards into FHIR specifications.

Ultimately, IDMP will become a requirement adopted by many health authorities around the globe. It’s therefore in companies’ interests begin laying the foundation of building IDMP as the common layer for a robust, data-driven environment and start preparing well before it becomes mandatory.


Jared Kimble has over 14 years of experience in the life sciences industry. His expertise ranges from software design and development to solution architecture where he is currently the offering lead for Life Sciences Regulatory Transformation Services. He leads the management and development effort for many key projects and was involved in several on-site engagements. Before joining DXC, Jared worked as a software engineer developing applications dedicated to providing financial exchange services for banks and financial institutions.

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