The clinical research review process is a lengthy, time-intensive procedure that can often lead to significant bottlenecks in an organization’s time-to-market timeline.
When the data submitted in a report fails to accurately support a study’s findings due to deficiencies in quality and integrity, it can set an organization back months in terms of work and analysis progress.
Over the past 15 years, the FDA and clinical trial industry have worked closely to make significant progress in promoting, adopting, and developing data standards. Believed to help align clinical research with the FDA’s goal to accelerate the regulatory review process, standards are key in creating an efficient review process and ensuring traceable, accurate findings throughout the clinical trial process.
What is data standardization?
Data standards are documented guidelines on the collection, use, and management of data collected in clinical trials enforced by regulatory bodies that review clinical study reports. Developed by subject matter experts, these standards allow efficient information sharing and provide quality assurance, preserving and improving data integrity within a clinical trial from start to finish.
Organizations collecting and analyzing clinical trial data must include a set of data quality standards within their current processes. Failing to do so could result in enormous roadblocks to submission approval, particularly if the data being submitted does not align with industry and regulatory standards.
What are the benefits of using data standards?
Originally intended to accelerate the review process, data standards enable all scientific disciplines efficient comprehension of a product’s safety and efficacy. This is particularly beneficial during the product’s regulatory review process in which multiple organizations must interpret study findings to determine if the product can and should be approved.
Of course, there are a number of additional benefits that come from using data standards industry-wide, including:
- Improved transparency in data sharing
- Enhanced data quality throughout
- Refined data integration and usability
- Enhanced clarity across third-party organizations and in-house teams
- Increased opportunity for automation and advanced software use
- Data and result consistency
- Data and findings accuracy
- Comprehensible interpretations of data
- Clear-cut processes for documentation
- Efficient resource utilization during the data collection and analysis
- Reduced data redundancy
How does technology help with data standards?
The emergence of standards technology and data exchange standards enforcement has become prevalent in both clinical and non-clinical data submissions. However, while data standards efforts continue, limitations involving traceability, interoperability, and linkage of data from collection through analysis remain.
Data transformation and management is fraught with issues. Creating lengthy SDTMs can put a significant strain on labor, time, and financial resources. Without the proper tools and subject matter expertise monitoring the ever-evolving CDISC standards, many organizations may encounter difficulties when producing required supporting documentation.
Who is responsible for data standards?
Standards organizations such as CDISC, Health Level 7 (HL7), and others have realized that legacy data standards cannot support the future operational needs of the industry and regulatory review process. This inability to adapt inhibits major steps forward in improving review process timelines and data transparency.
In response to these obstacles, HL7 has engaged in a new approach to exchange standards over the last 3 years by implementing FHIR, an innovative exchange framework focused on flexibility and rapid implementation.
Similarly, CDISC has initiated an ambitious pilot project, CDISC360, to reinvent standards implementation from beginning to end. The goal of this project is to provide standards that are linked to enable automation, and is positioned as the key to increasing return on investment in standard implementation while also providing improved efficiency, consistency, and re-usability.
Creating data standards is not an easy task. The development of standards requires collaborative expert input, analysis, and consensus from subject matter experts, industry leads, and ambitious pioneers driven by quality and results. Despite the amount of effort it takes to develop and implement these standards nationwide, the benefits of doing so vastly outweigh the other option - remaining scattered and disconnected, resulting in unusable data and wasted resources.
In order to reach the full potential of utilizing data standards, the FDA and industry must work together to understand and support a new approach to data standard implementation and consumption. This approach must also be balanced with the need to execute, operationalize, and iterate as necessary.
Pharmaceutical research organizations must play their part in implementing data standards throughout their businesses processes. Implementing industry standards at the base of their day-to-day operations will allow organizations to avoid unnecessary work when it comes time to submit studies, and will assist in creating and collecting quality data and results.
Experienced in combining extensive standard implementation with deep clinical data technology, d-wise is uniquely positioned to support your organization's diverse data standards needs. The Business Process Optimization and Submission Readiness Review offerings provide clients with the industry and technology expertise needed to ensure that your organization is prepared for the submission process from beginning to end.
To learn more about how your organization can benefit from the guidance of experts during the submission process or business optimization, check out our Submission Readiness Review webinar or reach out to us today!