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By Akhilesh Ayer, Head of Research & Analytics Practice and Mark Halford, SVP- Client Services, Life Sciences and Healthcare Practice at WNS
In a recent report, McKinsey emphasizes the remarkable speed at which three COVID-19 vaccines were granted Emergency Use Authorization (EUA) or other forms of approval in Europe, the UK, and the US by the end of 2020. In an industry where the average time for drug approval is ~10 years, this is unprecedented. That said, could this become a norm? With Artificial Intelligence (AI) making its presence felt, this possibility can soon become a reality.
The rise of telemedicine, amid the pandemic, is a great example of how AI is playing an instrumental role in the development of remote healthcare models. We are already witnessing the increasing application of AI in MedTech. The FDA, recently, approved the marketing of GI Genius, an AI-powered medical device to spot lesions during colonoscopy.
Pharma is taking early steps in the AI revolution that is poised to impact the value chain – from drug discovery and development, to supply chains and commercial processes.
Speeding Up Drug Development
AI can accelerate the process of clinical trials and testing by synthesizing vast amounts of unstructured data. The potential impact of AI far exceeds its application to automate processes for improved efficiencies, something that pharma companies have already begun.
For instance, countless research papers and statistics can be analyzed, evaluated and turned into practical information for researchers in hours rather than weeks. Permutations in testing and clinical trials can be managed at rapid speeds. AI also allows errors and outliers to be identified, and eliminated faster and more accurately than ever.
By shrinking the time-to-market, reducing the number of trials required and minimizing errors, AI will drive down costs for forward-thinking pharma companies. It can help prioritize R&D spend by analyzing demographic and health data, and examining other important factors, including competitor activity.
Providing the Decisive Competitive Edge
AI-driven Competitive Intelligence (CI) can enable the prioritization of R&D spend. It can streamline and accelerate the end-to-end data-to-insights process – right from knowledge collation through commercial and public data services, to curation, and dissemination of granular and actionable insights to end-users on preferred channels. In fact, significant progress has already been made in this regard, as exemplified by CI solutions such as PRECIZON that are leveraging AI and Machine Learning (ML) led pharmaceutical intelligence to accelerate decision-making and go-to-market.
Driving Insights-led Focus into Supply Chain & Marketing
AI can help streamline supply chains by mining heaps of data to identify threats and opportunities from economic, geopolitical and societal perspectives. For instance, predicting extraordinary requirements arising due to any of these perspectives. It can look at past patterns within supply chains, and make predictions and estimates. Managers can model different scenarios more rapidly and accurately.
In marketing, AI can provide essential information on underlying market trends and demographics. AI-driven CI can help identify new opportunities and gaps in the existing market.
Pharma companies have tended to use generalized, above the line advertising and marketing techniques that address entire groups. AI can enable personalized messaging through sophisticated segmentation and targeting. Different demographics across different geographies can receive messages that are relevant to them through their preferred media and on the most appropriate devices.
Where consent is obtained, AI can draw insights from data based on medical history, age, lifestyle and location to spot new marketing opportunities and tailor messages to patient needs. AI can use Natural Language Processing (NLP) to interrogate structured and unstructured data sources to identify the patient voice more accurately than any human can. This omni-channel targeted engagement creates better brand affinity and, when combined with the next best action practices that can be used to drive growth.
Brand and marketing teams will become more agile and, just like their colleagues in R&D, will be able to react more quickly and make fewer mistakes, owing to the availability of timely and accurate information.
A Word of Caution
While a majority of pharma companies are committed to investing in AI, they need to be aware of the potential barriers – data being the principal challenge. Cleansing, validating and integrating data can often take longer than expected. Intrinsic to AI’s potential is its ability to bring together previously disparate data sets or processes. Hence, AI initiatives will work best when integrated with enterprise data solutions.
The success of AI projects also depends on the right blend of technical, domain and business knowledge. No matter how smart the technology is, it still requires parameters to be set and aligned with business objectives.
There is no doubting the transformative and end-to-end impact that AI can drive for pharma businesses. But success will be an outcome of a well-thought-out and structured approach. This includes identifying the right use cases and setting strategic priorities. These could, for example, be faster price changes or increasing sales per representative. Business leaders should trial a particular area before extending the initiative. They must have the right suppliers, resources, and IT and governance standards. Most of all, they should rely on proven expertise.