Striking a Balance Between Centralized and Decentralized RIM Models

Jan 10, 2024 | Pratyusha Pallavi

Biopharmas that implement new regulatory solutions face pressure to demonstrate how the new system will support data-driven decision-making and enhance processes. Accurate, real-time data can help, but requires affiliates, subsidiaries, and regulatory strategy teams across therapeutic areas to adopt a global regulatory information management (RIM) platform.. An important consideration with a global platform is deciding between centralized and decentralized data management models.

Comparing the benefits of centralized and decentralized data entry

A decentralized data entry model allows infrequent RIM platform users, such as regulatory strategists or country regulatory managers, to track and enter data in real-time as regulatory processes and events occur. But, this approach requires extensive user training and expensive system controls to ensure consistent data quality standards.

A centralized data entry model traditionally involves a small team trained on RIM data standards within the regulatory operations organization. The team records progress of regulatory processes by gathering information from strategists and affiliates. However, considerable back-and-forth communication for completeness checks and data verification workflows can create long turnaround times to complete tasks.

So, what is the best balance between a centralized and decentralized regulatory data management model? Regulatory leaders from the top 30 biopharmas debated this roundtable topic at this year’s Veeva Summit in Boston. Many organizations started with one model, experienced challenges, and then pivoted to the other model, only to realize that neither one sufficiently met their needs. Many biopharmas are now experimenting with different levers, including:

Accountability versus responsibility
Selective decentralization
Hybrid organizational structures
Technology to enable greater automation

Read on to learn how regulatory leaders are approaching this industry challenge.

Defining accountability versus responsibility

The first step in determining the best data management model, according to regulatory leaders, is to identify the roles associated with each critical data point in the product lifecycle. Consider questions such as:

Who generates and owns each data point?
Who tracks it?
Which is the system of record?
And, who consumes it?

For example, when the FDA approves a new drug application (NDA), the country regulatory manager owns the approval status and date. However, the regulatory strategist of the therapeutic area owns the associated post-approval commitments. Further downstream, manufacturing uses the approval information to trigger batch release, while clinical operations initiates planning for post-marketing studies. As you can see, there are many different owners and consumers of data generated from a drug approval.

This data mapping exercise is valuable for clarifying roles and responsibilities, hand-offs, and quality metrics related to data accountability and documentation ownership. It enables hybrid data entry processes, where, for example, regulatory strategists are accountable for the data set, while submission managers within regulatory operations are held responsible for data entry. And, in cases where data quality metrics are trending downward, regulatory strategists could be appropriately questioned instead of the submission manager. This makes regulatory strategists feel more accountable for ensuring accurate data documentation by submission managers.

Employing selective decentralization

Process and data mapping exercises also help companies identify operational aspects better suited for decentralized or centralized approaches.

For example, one top 10 biopharma adopted a decentralized approach solely for their globally marketed and established core products that experience a high volume of global submissions. The repetitive maintenance of such products resulted in familiarity with the RIM system — even for infrequent users like affiliates. For specialty or emerging products, the data entry responsibility remained with central teams at headquarters.

Another top 10 biopharma decentralized only simple processes that involved few data points, sequential steps, and clear hand-offs. The team continued with centralized data entry for more complex process phases that included multiple parallel paths and conditional decision-making.

Implementing a hybrid organizational structure

Regulatory leaders also discussed organizational structures to support selective decentralization.

One top 10 biopharma created a new role, the “submission strategy lead,” that is responsible for operating the RIM system on behalf of the regulatory strategists. A dedicated role that is part of each therapeutic area’s regulatory strategy team, instead of rolling the responsibilities into the regulatory operations team, means no more throwing data over the fence and forgetting about it.

Another top 20 biopharma started with a centralized regulatory data entry team but has since opted for a hybrid approach by leveraging remote working models. In this new organizational structure, data entry individuals are part of therapeutic areas and affiliate organizations worldwide and perform the role of RIM data stewards. Their new job is to advise and coach the rest of the organization to own the data they generate by more actively using the RIM platform.

A different top 20 biopharma achieved similar results by transitioning the core implementation project team, which had amassed deep knowledge of the system, to superusers across the global regulatory organization.

Investing in automation technology

Automating simple process steps is valuable in decentralization. Regulatory leaders recommended creating basic system rules to automate data quality checks and use wizards or guided user workflows to help infrequent users navigate the RIM platform.

The promise of advanced AI and digitization for repeatable, high-volume regulatory processes, e.g., intake of correspondence documents from a health authority, strengthens the value of automation. Additionally, systems can leverage historical data and knowledge of regulatory procedures to provide recommendations on more complex steps that require regulatory experience and intuition, such as suggesting potential responses to health authority questions.

Technological advancements can aid companies in finding the right data entry operating model and make the processes more sustainable for the future.

Building stakeholder commitment for change

To find the best balance between centralized and decentralized models, buy-in from the broader user base is critical, affirmed many of the regulatory leaders. The “why” for selective decentralization must go beyond the risk of non-compliance.

Some companies shared that a top-down executive mandate was essential to champion such changes. Others offered the carrot approach to change — highlighting the value of real-time and high-quality data summarized via readily available dashboards.

Regardless of the change management approach, relying solely on a centralized or decentralized data entry approach is not viable long term — the time to evolve and adjust is now.

Watch our recent webinar with GSK, Jazz Pharmaceuticals, and Veeva R&D Business Consulting to hear more about efficient regulatory organizational operating models.