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Metadata comes from multiple sources and can be stored in different places, systems and networks. It’s hard to track it down. When files are updated, new versions are created. So how do you know if you’ve got the latest version? How do you know if the quality is good enough?
The easiest way is to use a clinical metadata repository. By creating organisational standards that adhere to industry standards, data will be reliable and consistent. You’ll also have greater transparency.
What’s a clinical metadata repository?
A cloud-based clinical metadata repository is essentially a database that maintains metadata definitions such as forms, datasets, codelists and variables throughout the various stages in a clinical trial.
Metadata plays an essential role in allowing different people involved in a clinical trial to access, monitor, track and log data. All your teams can access information in a readable format easily and quickly. It allows for effective planning, communication and teamwork, gives total transparency to all users and ensures that data is of a high standard. Both current and historical metadata should be accurate and easily accessible.
A clinical metadata repository is key to effectively managing organisational standards. It lets you:
- Create, maintain, govern and use standards consistently
- Reuse your existing assets
- Realise the impact of changes
- Create accurate mappings
- Be fully compliant
- Create high-quality submissions
There are various features that contribute to data quality.
You can create your own organisational life cycle for studies and standards to transition through according to your company’s governance process. Aside from improving data quality, governance lets you control and fully understand the workflow and develop robust organisational standards. This means you can get your product to market safely and faster.
If metadata isn’t properly managed, it can become out of date and invalid. Good governance means your metadata is accurate and compliant.
Organisational standards are stored in one place and can be reused ‒ for example, forms, mappings, annotations, controlled terminology and data sets. A standard can then be updated to suit study-specific requirements. Outputs can also be automated. And because standards have already been approved, tested and validated, data quality is improved and remains consistent.
One of the key objectives is to analyse the impact of change. All associated standards and assets will be analysed to let you know exactly what downstream or upstream metadata will be affected. Impact analysis should also show all assets that are indirectly affected. You can see how your assets interrelate in the metadata repository.
Impact analysis lets you make informed decisions before you make changes. You know the scope of the updates. And once you have this information, you can decide whether it’s worth making a particular change or not.
Team members can set up change requests to change existing standard objects ‒ for example, updating a form. The change control process is a predefined workflow that defines the approval process as well as the tracking and handling of change requests. All changes are tracked from inception to completion.
A good clinical metadata repository allows multiple versions of the same standard that has been updated, improved or customised. You can easily identify which version of a standard is being used, and users can be confident they’re working on the correct version of an asset or standard.
Traceability is of key importance in the world of clinical trials due to the ever-changing regulatory environment.
Traceability must be built into a clinical metadata repository so that all assets can be fully tracked through their life cycle. With traceability in place, you can see who has accessed the clinical metadata repository ‒ who made changes to which studies, standards and assets, and when. And you can check the differences between them ‒ for example, the differences between versions of the same standard. You can see the full and detailed history of a standard.
Full traceability throughout the life cycle process ensures audit compliance and increases the chances of a successful submission to the Food and Drug Administration (FDA).
The real measure of data quality comes at submission time. Are many questions raised? How long does it take to resolve them? If the answer is ‘not many’ and ‘not long’, then you know that the quality of your data is high.
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