There are all sorts of valid and practical reasons for life sciences organizations to be collecting, preparing, publishing/submitting and managing increased data in 2020, and — now more than ever — to be doing this in a structured way, using structured data.
Regulatory agencies are demanding increased safety and traceability detail, requirements about what goes on packaging and labeling are becoming more stringent, and the public is calling for greater transparency about the products they use. Meanwhile, companies need to be smarter, faster and more cost-efficient in the way they capture information and create critical content – from clinical trials studies data collection/reporting to managing product labeling.
As a result of these growing data-based demands, there has been a surge in life sciences companies asking for help. They see the sizeable challenges ahead but are often unsure where to begin. So what is the best way forward?
1. Test the business case
We recommend that companies approach transformation with a clearly defined business case in one small, focused area. Once specific benefits have been demonstrated, this will help to make the case for broader change. But it’s important to keep the bigger picture in focus. A strong technical backbone is critical – a master data source that is capable of supporting current and future use of regulated product data from one end of the global organization to the other.
2. Create champions
Changing the way organizations manage content will inevitably require disruption to the way people behave and work, so identifying corporate-level champions, and creating a team to showcase successful initial use cases will be important in creating momentum.
3. Be in it for the long haul
Don’t expect to be able to effect the transformation overnight. A definitive, standardized, high-quality global data backbone is likely to take years to create. Ideally, it would be possible to analyze and transform content by tackling small sections at a time, comparing different sources to look for discrepancies or overlap. However, if respective systems and teams have captured data in different formats and with differing degrees of granularity, comparisons will require too much time. Other potential issues include data ownership. If content ownership has tended to exist at a document level rather than a source-data level, it may not be immediately obvious who should be driving any data transformation initiative.
4. Be clear about the direction of travel, then reset
Legacy formats and systems will take time to sort out, but there comes a point when teams need to stop creating and handling content in “the old way.” Establishing common structures and templates for data will help put a hard stop to continuing data complexity by imposing firmer parameters over what and how data is captured from document/report authors. By restricting data input to what is needed by regulators, companies can start to curb the spiraling of free-form content.
This will help to keep everyone focused on building consistent, high-quality data with the potential for extensive re-use — as long as the accuracy and currency of the data is maintained across its lifecycle, so that it remains trusted as a definitive information source.
Establishing a common dictionary and a content repository free of duplication will be instrumental in transforming existing content so that it can be retrofitted into the new structured templates and processes, and invested with new business value. A common dictionary sets down agreed rules for referring to products and data around them, and will define any metadata linked to that content which makes assets searchable in context.
Once historic product data has been transformed and assigned proper schema, and processes put in place to ensure that information edits or additions adhere to the new structure and sources, the positive impact should be felt in everyday activity.
5. Aim for early successes
To build momentum and secure people’s buy-in, look for some early quick wins. We advocate starting with a proof of concept in an area where documents are simpler in makeup: factual rather than descriptive, and typically defaulting to a single language. Some CMC/manufacturing documents fall into this category, detailing the composition of a drug and usually in a common language. Descriptions of formulae can be readily transferred to a table format, following standard fields describing the composition of a drug or its manufacturers. Once companies have learned what works well and proved the potential of a common, structured way to capturing and managed content, they can broach international labelling management from a structured basis, harnessing reusable master content.
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- The Benefits of Structured Content Management in Life Sciences - September 25, 2020