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Global content management needs are mounting among life sciences organizations, as they expand their market coverage and grapple with multiplying regulatory workloads. Monica Vytiskova asks if can intelligent automation offer a solution.
Global content management needs are mounting among life sciences organizations, as they expand their market coverage and grapple with multiplying regulatory workloads. So can intelligent automation, in the form of neural machine learning, offer a viable solution?
There are few industries where the need for transformation of translation management is more acute than life sciences. New drug development is happening at an accelerating pace, firms’ global ambitions are growing, and in parallel the industry is subject to increasingly rigorous and demanding regulatory standards internationally. Indeed, the scale of documentation now needed to bring drugs to market is immense. Medical device manufacturers now face similarly strict controls too, as governments act to improve patient safety in the wake of some high-profile public scares.
In the meantime, the profile of automation translation technology is rising sharply. As with so many digital developments today, consumer experience is shining a light on what’s possible. Instant phrase translation and real-time conversations between people from different countries, enabled by tools such as Google Translate, iTranslate and Waygo, have raised expectations of what could-and should-be possible in a business context. This is especially the case given how much budget and time is allocated to maintaining international consistency and messaging, and containing the risk of meaning being lost or skewed as content is adapted for different markets.
Traditionally, global content – for regulatory submissions and patient instructions and labeling – has been managed somewhere between local market affiliates and professional translation agencies or language service providers (LSPs), but almost always in a decentralized way, largely out of view and beyond the reach of corporate quality control teams.
Yet, the prospect of centralizing controls can seem daunting. Trying to link previously unconnected systems so that they can talk to each other is a significant and expensive undertaking and something companies can’t expect to achieve overnight.
Another approach has been to create regional capabilities – teams structured to look after the content needs of groups of countries, which share at least some of the same characteristics or requirements. But these plans place too much emphasis on people to handle all of the work and quality checks, incurring considerable expense and processing time.
In the meantime, translation technology has moved on at a phenomenal rate, particularly with the coming-of-age of neural machine translation and the ability of automated systems to recognize, learn and adapt to new vocabularies at high speed, transforming the economics of using the technology. Today, the overwhelming majority of international life sciences organizations are planning for its usage – and putting pressure on their translation agencies to adopt it.
In clinical trials, where the timelines for producing localized content are extremely tight, neural machine translation offers a very real solution and is already having an impact today. Notifications of adverse events, for example, can now be translated and understood almost immediately. Compare that to taking days or more depending on the volume to process. The potential for saving time is tremendous and can be a game changer in the clinical trials process where every day saved in the timeline counts.
Even for documents which must be flawless in their translation accuracy, like Patient Informed Consent Forms, and will therefore still require human oversight, potential savings range from 30-50 per cent. This is because initial rounds of translation can be automated, producing high enough quality that leaves just the final honing and checks to human editors or compliance and quality control teams.
The benefits become more pronounced as volumes of content rise and where language pairing is favorable (English to Spanish being more common than English to Malay, for example).
Ability to train the neural machine engines on custom vocabularies pushes the potential for higher quality and therefore savings in time and effort on the part of the human input even further.
The scope for combining neural machine translation with regional and eventually more centralized content processes is considerable. For instance, as pharmaceutical and medical device companies look to take a more holistic and efficient approach to monitoring market authorization requirements and compliance, they may opt to translate everything into English, or standardize in another corporate language, for the purpose of corporate visibility and quality control checks.
Such an approach offers those with overall responsibility the chance to verify what the equivalent document says for each market. Alternatively, in the case of pharmacovigilance, central responsible teams are able to collectively view all of the real-world feedback/adverse event reporting about a product from across geographical boundaries, enabling speedier and more precise decision-making, with a positive impact on risk control.
Although companies can’t (yet) rely entirely on machines alone to pick up everything, neural networks are already having an impact on data mining – by quickly learning the signs to look out for. As a result, these systems’ accuracy in flagging up meaningful events from the vast data depths and market ‘noise’ can quickly reach a superior level to anything that could be achieved by people alone. Human translators still have a role to play though – for instance, in validating whether red flags in translated feedback warrant further exploration and action.
Life sciences companies are already making good headway with intelligent automation of data mining in pharmacovigilance, for instance for combing the published literature and public patient forums for red flags. So their desire to extend the same capabilities to cross-market content translation is logical and rapidly becoming established.
As they have already seen, the cost, time and risk-management benefits are potentially very impressive, especially for high-volume translation services for which raw machine translation output is adequate. The processing cost per word through a neural machine engine is nominal compared to translation costs by a human. For the more exact needs of standard publishable content, the scope for savings are still substantial because of the time and labor saved in initial rounds of translation.
Where translation needs are highly specialist, companies may prefer to invest in custom-trained engines using the company’s preferred terminology and more precise vocabulary. An LSP partner can help here, advising on the best approach from across all the available options, for the given workload. Indeed, in 2019, life sciences companies are now looking less for just translation suppliers and more for complete solutions/process partners.
Demands for automation via neural machine learning are soaring, certainly, so being able to offer more intelligent options -and report on related performance metrics - is imperative for any language service provider now.
Other industries may be further along with neural machine translation currently, but necessity is the mother of innovation and the mounting pressures on life sciences companies to streamline critical content management processes globally are creating a perfect storm to trigger change.
Monika Vytiskova is a Global Solutions Architect within the Global Content Solutions business at AMPLEXOR Life Sciences.