An Agile Approach To Regulatory Information Management System Transformation
By Adnan Jamil, Iperion — a Deloitte business
Life sciences industry regulators have become increasingly focused on data-driven processes as a means of managing marketing authorization submissions. Potential associated benefits to companies include improved decision-making; optimized health authority interactions; increased regulatory intelligence; and streamlined data exchange between business functions.
But this requires that regulatory information management system (RIMS) upgrades are in a state of continuous evolution and are approached holistically. Common issues, and appropriate recommendations, can be broken down into system challenges, process challenges, and matters of data governance.
Although the transition from document-centric information delivery and management to a data-first environment is not entirely a technology issue, technology will form the foundation through which everyday order, consistent format and structure, data quality, and status visibility are maintained and improved.
Many companies have realized that a more agile methodology is appropriate to enable flexibility. Yet this can bring its own challenges as teams adapt to the new way of working, including:
- Develop an agile mindset around the RIMS upgrade. Adopting a mindset of refining requirements in subsequent system/project iterations can be difficult, as companies have been used to implementations with predetermined parameters.
- Capture gaps in existing data 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.
- Establish 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.
- 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 are essential to ensure that all data in the updated RIMS system are reliable, correct, and compliant.
- Establish and embed 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.
- Assess existing reports 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.
- Review existing systems integrations. 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 appropriate precautions will need to be taken to ensure that the upgrade doesn’t impact business continuity.
- Make decisions 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.
Optimizing Business Processes
However powerful the new system, the scope of the transformation will be limited unless associated business processes are optimized 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.
- Integrate business and system processes.
With increased dependence on and requirements for regulated data, RIMS is finally gaining traction as the facilitator and enabler of the regulatory function. But, in reality, business processes will need to be (re-)modelled, as it is better not to pursue customization in the interests of maintaining simplicity through standardization.
Setting up a process framework using a (business) process architecture methodology allows a clear overview of all the process steps, increasing everyone’s understanding of relationships and dependencies between specific processes, while also making it possible to drill down into more detailed process steps as needed.
- Adapt and harmonize system functionality.
Inevitably, the addition of new data-based capabilities, e.g., in preparation for EU IDMP compliance, will require the adaptation of business and system processes. Numerous updates and changes to documentation can be a laborious and inefficient process if done manually. Considerable duplication of documentation can exist across functional subdivisions, too. Having one harmonized master can substantially reduce the update effort and associated user training.
Data Governance
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 optimization. Key steps include:
- Clearly define data roles (including data owner, data stewards) to date.
- Establish data definitions, business rules, and data quality requirements and metrics. A unit dedicated to this will speed all of this up.
- Set up central coordination of controlled vocabularies, organization records, substance information, and specific product identifiers.
Overall Best Practices For When You Get “Stuck”
With so many considerations, life sciences organizations can become stuck in knowing how best to proceed, especially when they want to achieve key milestones within an acceptable timeframe. Emerging leading practices suggest:
- Communicate regulatory expectations and a detailed understanding of the intended data model, as well as the target system and business processes to stakeholders across the business.
- Provide business reporting, using data in the RIM system (both for regulatory compliance and process checks/efficiency).
- Remediate existing data within the RIMS to ensure it is reliable, complies with upcoming regulations, and can be trusted as a definitive source of truth.
Foster A Culture That Values Data
An organizational culture that recognizes the value of good data and fosters the right environment to drive this is critical. Then, technology initiatives should be incremental with clearly defined goals that go beyond compliance. Supported by effective change management, a data-first approach to RIM system evolution will transform product life cycles and business processes, ensuring maximum returns.
About The Author:
Adnan Jamil is a manager at Iperion, a Deloitte business. He has 10 years of experience within the life sciences industry, focusing mainly on regulatory information management. His expertise spans the assessment, definition, and implementation of processes, technology, organizational, and data changes in small, medium, and large pharmaceutical companies. Jamil can be reached by email at adjamil@deloitte.nl.