Labelling and Data Management: it’s all about the bigger picture

Labelling and packaging content is subject to manual processes, high error risk and inefficiency despite integration and increased automation in life sciences.

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For some firms in life sciences, product labelling is treated as a distinct, manually-driven process. This is a legacy of an era when technology was trusted and reliable than it is now, and it’s also a major error, as it can leave an organisation vulnerable to risk, such as costly product recalls, and inefficiency. To reduce this risk, associated processes need to be systematically managed, using automation - so that the latest compliant wording, symbols and other criteria are used consistently and reliably, and that checks and controls aren’t reliant on human proofing alone.


Time to revisit content processes

While many organisations still rely too much on manual processes, intensifying regulatory requirements and the evolution towards IDMP means that they are looking again at their processes and systems for managing content.

The most effective way to do so is via a centralised (or virtualised) ‘master data’ approach, meaning those responsible for labelling and related content preparation, have a definitive database of approved assets to work from.

In the context of labelling management, the goal should be to enable systematic, structured content altering as regulatory requirements are updated in given markets, or as products evolve over time.  Yet without master data management, cost, risk and inefficiency remain and in the context of labelling, companies’ exposure could be significant.


Master data management step one

Any master data management initiative should start with a product master data object model, of which regulatory intelligence is a part. The regulatory factors may not fit generic system data fields, being the proprietary IP of each company. But if it the information is structured, it can still be reflected in the main product information system, contributing to that holistic, 360-degre  e resource which caters for all information needs.

Combining product master data with regulatory intelligence makes it possible to automate more processes – including labelling management - and the need for heavy manual work is reduced each time there is a new content-related requirement.


Accurate and compliant labelling

Taking a master data/complete product profile approach means all of the correct content for accurate, compliant labelling can be called up quickly and easily for the given use. In addition to ingredients and manufacture information, it should be possible to call up detail for all authorised medicinal products alongside all the respective countries’ procedures, health authority organisation information and marketing authorisation programmes and processes.

Labelling processes, change requests, sequences and templates should all be possible to manage in a clear and structured way. Proper provision for labelling, to reduce risk and improve efficiency, should include the ability to select approved content elements as self-contained label ‘objects’ or assets.

By seeing the bigger picture around labelling and data management, life sciences firms open themselves to a range of new possibilities – to reduce complexity, cost and risk, while improving productivity, accuracy and speed.

For further information on AMPLEXOR Life Sciences, and how we assist firms with master data management, please visit here.

Published on 20/09/17    Last updated on 03/10/19

#Life Sciences, #Content Management, #Labeling

About the author

Romuald has devoted his 25-year career to-date to various roles related to compliance, document management, and content management in the Life Sciences industry. He has held leadership roles both on the client side and in consulting, including delivery, sales, and project and line manager. His experiences bridge on-premise and cloud environments in Europe and the US. Romuald holds a Master’s Degree in Drug Regulatory Affairs from the University of Bonn, Germany, and a diploma in data technology from the Technical University Darmstadt, Germany.

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