The road to better medicine is paved with data

Until recently, compliance in a life sciences context has primarily been centered around documents and files. Regulatory submission dossiers. Quality batch and CAPA records. Clinical repositories. PSUR packages with pharmacovigilance documentation. Often in the form of spreadsheets and narratives saved as PDFs.

Some of these are still required, but regulators increasingly expect product information to be exchanged as structured master data instead of documents. In the EU, EMA is implementing Identification of Medicinal Products (IDMP) while the American FDA is pushing for formats like Structured Product Labeling (SPL).

This shift requires life sciences companies to adopt a new approach and mindset, as Cristina Ghiurcuta, Principal Consultant at NNIT, explains:

– Regulators are moving away from documents and toward data. So having a strong foundation of clean and consistent data that adheres to standards like IDMP is essential if you want to remain competitive in this industry.

A focus shift from output to input

With the old compliance mindset, the focus is to prove quality in the output, such as the finished dossier or submission document. Often, this involves manually reconciling and checking data from several different sources before collecting the finalized documentation in packages ready for submission.

The problem is that fragmented data and poor interoperability increase the risk of inefficiencies, potential errors and possibly delayed treatment. A product’s dosage form or strength might be listed in the incorrect format. Duplicate cases in safety reporting go undetected. Missing timestamps on a batch record delays release.

– When the data flow is fragmented across systems, processes are inefficient and ultimately patient safety can be at risk, says Cristina Ghiurcuta.

Under the new compliance mindset, the focus shifts to ensuring quality from the inputs throughout the process, so that quality and consistency are built in at the source.

In other words, the most important task is not to compile and quality-check a document narrative, but to ensure that the underlying information is available in a structured form that can be verified and aligned with standards before being delivered directly to Health Authorities.

Why master data management is often a struggle

While many life sciences organizations have already started to make this shift, they often realize that managing their master data is more of a struggle than they anticipated. The answer lies in siloed systems and old habits that are hard to change.

– Master data management becomes harder if you lack a strong governance and have focused on optimizing individual systems instead of the entire ecosystem. To fix this issue, you must align the underlying processes and integrate them accordingly, says Cristina Ghiurcuta.

This can be accomplished through strict data governance and by using standards like FHIR (Fast Health Interoperability Resources) to ensure interoperability, she explains:

– The lack of a consensus data vocabulary across an organization, leads to different systems defining the same data differently. NNIT can help with this, by using standards like FHIR to harmonize data, streamline systems’ interoperability and remove the need for manual reconciliation.

Build a strong data culture

To break down the silos and create better interoperability, you must work on multiple fronts: Integrating digital tools (e.g. via a FHIR API), agreeing on data definitions and implementing governance to ensure that everyone follows the new standards.

– Data affects the entire business, not just the IT department. So, the entire organization must be involved in the process, and it requires strong leadership focus. You need to create a data culture, says Cristina Ghiurcuta.

Making the shift from an output-based compliance focus to a strong data foundation has multiple benefits beyond just compliance.

– Building APIs to directly connect with health authorities can save hundreds of hours of manual work. Automating complaints handling and regulatory intelligence will require less time spent keeping up with changing regulatory requirements. Ultimately, implementing a strong data foundation and automations leads to increased patient safety, faster development of new treatments and shorter time to market for medicines.