Soapbox

Smart IDMP: How to Ensure Preparations for ‘Data-Driven’ Future Are a One-Time Investment

By Romuald Braun
October 7, 2021


• Categories: 01-Digital Health, 02-MedTech Economics, 04-Product Design & Development, 05-Quality/Regulatory, Soapbox

If ever there was a case for doing things differently, and in a smarter and more coordinated way, it is in preparation for the data-driven regulatory future. EMA’s IDMP—due to be mandatory from February 2023—may be the flagbearer for this new reality, but it is not an end in itself. It is simply the first in what will be a long line of digital requirements to emerge globally, across all facets of life sciences.

The FDA has long fostered similar goals linked specifically to labelling and CMC information management in pharma, with plans to extend its implementation of the ISO data standards to other areas in due course, while other regulators across the world are also looking at something similar to IDMP. At the same time, medical device regulations such as the EU’s MDR and identification standards (eg., UDI) are progressing for medical device manufacturers—and who knows what else lies over the horizon.

The point is that we have a good idea where everything is heading, even if many of the details are still evolving. What all of these emerging standards have in common is support for new digitally enabled process efficiencies—across functional/company and even country boundaries—and for greater global transparency and accessibility of product information.

The mistake, indeed the great missed opportunity, would be to invest in each set of regulatory requirements as a distinct project. This would risk incurring great expense—not to mention investment of time and resources each time—only to create another system silo, a scenario that is at odds with the data-driven movement.

The smart money will be spent on preparing not just for a given set of external requirements, then, but for a data-driven future that will better serve internal operations and ultimately -produce better outcomes for patients.

But what does that look like, and what’s the best route forward?

Reducing Manual Repetition

One way to look at this is to consider how many companies still approach regulatory applications and reporting. Typically, this happens sequentially: Documents are built, typically from scratch each time, through a painstaking process involving each department gathering its information before throwing it over to the next team to add their contribution.

EMA’s target operating model for IDMP, now made clear, challenges this process head on. It requires that the originating data behind each dossier is submitted/made available at the same time, requiring much tighter synchronization and alignment of document/data management activity. This in turn suggests more continuous collaboration between teams in different departments to maintain a single, comprehensive and current representation of the truth from which all submissions, knowledge and insights flow.

IDMP Shows the Way, but Isn’t the Magic Formula

Although there has been a temptation to wait until all of the details of a particular regulatory mandate—most notably, but not exclusively EMA’s IDMP—are set in stone before making serious headway with data-based process transformation, this is a flawed strategy.

For one thing, IDMP’s main purpose is as a transfer protocol for exchanging information between regulatory agencies and the life sciences industry, with most immediate application in the EU, and initially only for Centralized Procedures. In other words, its primary use case was never intended to be as a model for an internal data repository to help companies function more optimally.

So, life science organizations can free themselves from their perceived reliance on external decisions to progress their own process transformations. They can take the principles and templates from EMA’s implementation of IDMP and other regulatory initiatives as helpful blueprints. But, given how diverse the global regulatory landscape continues to be, and how regularly requirements are updated, a more sustainable strategy will be to keep internal data models independent from regulator-specific parameters. Ideally, a company’s own data model will be built with the scope and flexibility to address a broad and growing range of requirements through simple configuration. The key is that this new, improved organization-wide capability will support the maintenance of up-to-the-minute, high-quality data right across the product lifecycle across entire portfolios, and all product variants, from now until eternity.

Investing for the Long Haul

Most Regulatory teams have grasped the need to think laterally about compliance projects, but up to now this hasn’t necessarily translated into a more holistic and forward-looking approach to their preparations. More often than not, companies continue to put off the inevitable until deadlines feel within reach and the specifics of each set of new Regulatory requirements are known. At that point, speed is of the essence and the temptation is to do something ‘fast and dirty’, which does the job within the time left—no more, no less. Then, when requirements are updated, or another region goes its own way, teams are forced to return to the drawing board.

The smart approach is different. It prioritizes up-front preparation, so that the company invests once and well for all future eventualities. Instead of looking for a single, mandate-specific system that delivers what’s needed for one region’s latest requirements, it focuses on creating a comprehensive internal capability that captures and maintains everything in a standard way for the global organization. This master data and any linked, approved content can then be repurposed/reconfigured in any number of different ways for each new set of needs.

Through collaboration across multiple departments, and with backing from the top of the organization, this emerging approach establishes a comprehensive internal data model with a high level of granularity, which can be continuously developed and adapted with input from across the enterprise.

Planning for a Sustainable Future

There is still time to build a future-proof data-driven regulatory information management (RIM) capability, if companies start now. The better the preparation, the more sustainable, reusable and valuable the resulting platform will be over time.

This is how we recommend building such a capability:

1. Start with an understanding of the benefits of a ‘Smart IDMP’ approach, or multi-purpose platform/capability, and an appreciation/acceptance that this will require more investment and planning now than a single-purpose compliance system.
2. Establish a baseline. Identify the relevant data the company has, and any gaps in it, and the steps and processes that will be needed to improve and maintain it—collaboratively and in the context of the desired data model. This should be thought of both from an internal perspective, and in the context of evolving regulator expectations—eg., the EU IDMP target operating model (which specifies the simultaneous delivery of dossiers and underlying data, implying reduced reliance on manual cutting and pasting or cross-checking between distinct, parallel activities).
3. Put a project in place, defining roles and responsibilities, and securing support from the top management to drive any necessary transformation to business processes, and even the organizational structure, to allow the new data-driven vision for RIM—and more—to be realized.
4. Implement the desired data model, accommodating the requirements of IDMP and any other relevant regulatory demands, along with the supporting tools or platform to enable these to be combined with associated processes. Consider scope for extending this capability to encompass functions beyond the Regulatory remit—such as Quality/Safety/PV and labelling management. A configurable platform will ideally support a wide range of use cases, based on the same core data set.
5. Establish appropriate data governance, reflecting interdependencies between roles and teams across the value chain, so that the quality of data can be built across departments. This may require change management to support a revised/optimized organizational structure.
6. Go live, ensuring that a data governance and associated process body is in place to maintain this valuable utility in a sustainable way, supporting continuous improvement to data quality and to processes

The sooner companies get going, the less chance that they will have to compromise their vision.