Evolving RIM systems to deliver data-first ambitions

Life Sciences industry regulators have become increasingly focused on data-driven processes as a means of managing marketing authorisation submissions. Potential associated benefits to companies themselves include improved decision-making; optimised health authority interactions; increased regulatory intelligence; and streamlined data exchange between business functions.

But this requires that regulatory information management system (RIMS) upgrades are approached holistically. Common issues, and appropriate recommendations, can be broken down into system challenges, process challenges, and matters of data governance, as discussed below.

1. The need to develop an agile mindset around the RIMS upgrade.

Adopting a mindset of refining requirements in subsequent system/project iterations can be difficult to adapt as companies have been used to implementations with pre-known parameters, even though regulators themselves are embracing an agile approach to development and implementation of new requirements and ways for the industry to interact with their systems.

1. Gaps in existing data captured within the central RIM system.

Up to now, companies have had the flexibility to define the data and processes based on their own requirements. Yet, where such efforts have happened without specific response to regulatory requirements, there are likely to be gaps in that data. Technology teams will need to work with the relevant business stakeholders to work out how best to address these.

1. Data collection efforts required for upcoming regulations.

In addition to getting existing data in order, technology and functional teams will need to establish new/additional data fields that must now be populated, under EU IDMP for instance. This has implications not just for the upgrade of the RIM system, but also for the collection and entry of the data within the applicable data model field/object, with appropriate links to existing records.

1. The need to transform existing data to the new standard fields as the data model is updated.

Although software vendors may provide out-of-the-box tools to support data loading and transformation efforts, these won’t automatically deliver the data transformation. Appropriate knowledge and preparatory work is essential to ensure that all data in the updated RIMS system is reliable, correct and compliant.

1. Establishing and embedding the culture and approach around data quality management.

To ensure that the data referred to in future for all aspects of regulatory exchanges, operational checks, and strategic decision-making is dependable, companies must establish formal parameters for reviewing all of this. These should include validation rules within the RIMS, and frequent data quality audits.

1. Existing reports will need to be assessed based on the system upgrade.

Since the existing fields might be modified during the RIMS upgrade, the current reports used by business users will need to be assessed for any impact to business continuity. Review of existing report specifications should be treated as part of the upgrade project to facilitate business continuity post go-live.  

1. Existing systems integrations will need to be reviewed.

Any existing system integrations will need to be assessed based on the RIM system upgrade, for the potential impact on upstream/downstream systems. The upgrade might mean that existing data is not available in the same location going forward, so an assessment and advanced precautions will need to be taken to ensure that upgrade doesn’t impact business continuity.

1. Decisions will be required about legacy data/product information management.

In populating upgraded RIM systems, teams will need to make decisions about where to draw the line with the data being transformed and managed on an ongoing basis. For inactive registrations, archiving may suffice.

However powerful the new system, the scope for transformation will be limited unless associated business processes are optimised to take advantage of a continuous flow of good-quality, standardized product and registration data. This starts with ensuring that the new/updated system works better for everyone.

To date, Life Sciences companies have struggled with setting up proper data governance measures. Unless these are addressed, they risk compromising the potential of their RIMS and process optimisation. Challenges include data roles, data definitions and the need for central coordination of controlled vocabularies, organisation records, substance information, and specific product identifiers.

Best practice recommendations for action include:

1. Multiple business stakeholders being involved and have a detailed understanding of the intended data model, as well as the target system and business processes.
2. Establishing a data governance body with defined roles and remits.
3. Business reporting, using data in the RIM system (both for regulatory compliance and process checks/efficiency), being provided for in its own right - by region, timeline, etc.
4. Remediation of existing data within the RIMS to ensure it is reliable, complies with upcoming regulations, and can be trusted as a definitive source of truth.
5. Clear and proactive communication about regulatory expectations to end users. This also requires appropriate training.

The rewards of  RIM transformation

Many companies have realised that there will never be a defining moment when all regulatory and business requirements are set in stone, and that a more agile methodology is needed. The transition to a data-first environment is not entirely a technology issue, but incremental technology enhancements will form the foundation for consistent format and structure, data quality, and status visibility. Appreciating data’s wider business value rather than focusing on compliance for its own sake is key to underpin ongoing process and technology improvements.