Life sciences executives know labeling is an essential component for bringing products to market and keeping them there. They are also well aware that the labeling process is highly complex and constantly in flux, due in part to frequently changing requirements across more than 150 regulatory bodies globally. Despite this knowledge, the labeling function has been largely overlooked as an area for digital transformation.
Automation technology can help aggregate and organize information quickly, which enables the acceleration of these otherwise laborious processes. However, since emerging artificial intelligence and machine learning tools are not yet being utilized for label management, administrative input has not been completely done away with. This leaves gaps for human error that could negatively impact or delay labeling approvals. The integration of AI and ML technologies will elevate traditional automation to make it truly intelligent, but it cannot be implemented overnight. Decision-makers must consider where their organizations stand today on label management and how they can ready their digital transformation strategy in the present for the next generation of capabilities.
Addressing Present Challenges
The industry is facing unprecedented pressure to expedite its regulatory processes, which includes label management. Life sciences executives are tasked with ensuring that all manufacturing facilities strictly adhere to a plethora of standards that typically vary across countries, regions, product categories and more. What’s more, this expectation is maintained in high-pressure situations, such as with label change requirements, which can be unanticipated and may need to be addressed at a moment’s notice.
Product labels can undergo multiple changes due to factors ranging from safety and efficacy concerns to simple label graphic changes. This challenge is compounded by the fact that drugs and medical devices marketed in multiple countries must continuously adhere to labeling and regulatory standards that are often completely different. Any level of noncompliance in labeling could be met with serious consequences, including losing market authorization or, worse, jeopardizing patient safety.
Subsectors of the industry face their own unique challenges, as well. Emerging biopharmaceutical companies do not have the years of regulatory experience or the headcount that many large pharmaceutical companies have to manage processes effectively and efficiently. As a result, those seeking to retain control of their own products as opposed to partnering with more established players will experience difficulties navigating the labyrinth of varied regulatory environments.
Medical technology companies are at a similar disadvantage as a result of the explosion of digital devices and “software as a medical device,” as well as sharply increasing regulatory mandates, like the European Union Medical Device Regulation. These shifts are only the beginning of regulatory adherence challenges for medical technology, which has previously not been treated with the same stringency as traditional pharmaceutical companies.
For these players, adopting automation will increase the speed and dexterity with which they overcome these disadvantages. Though the pharmaceutical industry has by far the most regulatory experience and resources to digitally transform, it is encumbered with legacy systems and processes, as well as significantly more people to retrain. As such, while emerging biopharmaceutical and medical technology will have an easier time quickly adopting intelligent processes, traditional pharma will be challenged with time and resource-consuming change management. This reality should compel pharmaceutical leaders to walk before they run on intelligent label automation and regulatory intelligence at-large. Integrating automation into a digital strategy will prime the organization for what is to come next while minimizing the burden on business operations.
The Present and Future of Labeling
Though there is much in store for intelligent labeling automation, current iterations of automation technology do hold significant immediate benefits. Most notably, labeling automation makes it possible to process massive amounts of compounding regulatory information and expedite regulatory processes.
The U.S. Food and Drug Administration alone has implemented more than 2,000 new or modified regulations since 1998. In tandem, health information has expanded far beyond what is possible for humans alone to manage. The labeling function has been no stranger to these realities. Currently, a department’s key priorities to manage label information and execute operational activities necessary to deliver the label to market are fraught with rote, redundant tasks and administrative burden. Automation is an ideal solution for these headaches.
Pharma companies have long seen compliance functions like labeling as cost centers. However, placing focus on making these processes happen more quickly through digital transformation ultimately frees up time to be reinvested into the business and driving activities that propel marketplace advantage.
In the future, more advanced intelligence will make this speed even greater while adding a much higher degree of accuracy, as well as proactivity. Labelers will be able to compare any number of countries’ labels simultaneously, react to global regulatory insights proactively and make adjustments before noncompliance becomes a possibility. This, in turn, will ensure resilience against the unpredictability of the regulatory compliance landscape.
Beyond the business, intelligence in labeling will act as an added safeguard and enabler for pharmaceutical companies to protect their patients and advance the industry-wide initiative toward patient-centric business practices. With intelligence enabling critical information to be processed and relayed in near-real-time, pharmaceutical companies will eventually be able to relay label updates to patients taking those drugs almost immediately, creating an unprecedented level of transparency between the pharma company and its end users.
Planning for the Future
One can never start planning too early. Adopting labeling automation as part of a comprehensive digital transformation strategy today will inevitably help chart the course for long-term success when future innovations like intelligent label management come to fruition tomorrow.
