BlogsHow Artificial Intelligence and Machine Learning is Changing the Pharmaceutical Industry?

April 26, 2022by Shashiraja
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Digital disruption has triggered transformation across industries, bringing about revolutionary changes in business operations. The pharmaceutical industry is one such industry that has been at the forefront in adopting new technologies. Unsurprisingly, it is now equally receptive to new-age technologies powered by artificial intelligence (AI) and machine learning. Look at the progress on the healthcare front during the pandemic. The medical efforts to manage the Covid outbreak were assisted by AI at all levels – from expedited vaccine development to scheduling vaccination drives for billions and creating chatbots for contactless screening of symptoms – artificial intelligence (AI) has been the silent enabler of our pandemic triumphs.

With several pharma companies looking to leverage advanced analytics further, the demand for AI-powered applications is shooting up. The McKinsey ‘The state of AI in 2020’ survey on AI implementations across industries mentions that high-performing organizations have increased investment in AI in each major business function in response to the pandemic. Companies in healthcare services, pharmaceuticals, and medical products are most likely to have increased their investment.

Here are some ways in which AI and machine learning are changing the pharmaceutical industry

1- Research and development with greater predictability: Research and development for bringing a new drug to the market is an extensive process with very high costs and extended timelines. And this is where AI and machine learning are instrumental. AI systems can analyze enormous amounts of data and identify crucial patterns to generate critical insights.

This can be used to recognize the complex biological networks that aid the survival and communication of a pathogen. Following this, artificial intelligence (AI) and machine learning can assist with the identification of drugs that may be effective against the disease, bringing in greater predictability to research and development.

2- Clinical drug trials are quicker with less expenditure: Clinical trials are a long process dealing with an incorrect selection of patients, delay in timelines, the complexity of trials, elevating costs, and stringent compliance. A Cognizant study shared that around 80% of clinical trials fail to meet enrollment timelines. This is set to change with IoT (the internet of things).

AI-powered mobile and wearable health devices make it possible for patients to share data conveniently without compromising the quality of information shared. This enables the trials to move along quicker, eliminating discrepancies that crawl in due to prolonged trial cycles.

3- AI-assisted marketing: With artificial intelligence (AI) and machine learning, pharma companies can test marketing campaigns for their success and, based on the powerful insights, develop strategies that predict revenue and measure brand awareness. Machine learning algorithms also enable businesses to run social media analytics to identify the right influencer (doctor) to meet their campaign needs.

4- Predictive forecasting for manufacturing optimization: AI predictive models can predict what the forthcoming seasons can be like regarding infections across different geographies, helping pharma companies plan their actions well in time. This means manufacturing and distributing drugs that will be most needed and combating stockouts due to surprise infections or scale of infections.

5-Supply chain optimization: Predictive modelling and statistical analysis can also help with end-to-end supply chain optimization. This includes reducing material wastage, optimizing yield, developing procurement excellence, and optimizing network partners. With platforms that integrate with partner systems across the supply chain, AI can streamline processes from procurement to distribution and sales.

What to consider when opting for AI?

Here are the key aspects to consider while making AI and machine learning your business enabler.

1- Look for a service partner with the right set of capabilities:  Look for a service provider with a deep understanding of the pharmaceutical industry and the ability to bring all your supply chain stakeholders on a single platform. This will help seamless integration of AI with your system and create end-to-end automation. This is also essential to counter fractured digitization that creates information silos, leading to impaired insight generation.

2- Choose an area of preference and set a quantifiable KPI: Integrating AI and machine learning algorithms is not just a matter of expenditure. Initiatives that are heavy on investment too fail to deliver value or deliver partial value because businesses assume that ‘value’ is something that will occur later in the cycle. This pitfall can be avoided. When drawing up plans for AI implementation, please choose an area of preference where you would like to see a clear benefit and quantify it. The benefit need not be financial, just an improvement before and after. This serves as a reference point for the transformation process to align itself with.

3- Analytics is an innovation capability that gets better with time, proceeding gradually: It is not uncommon for businesses to expect their AI initiative to deliver results rapidly. A good way of doing this is to create an independent team and share an innovation priority or preference as laid down by the management. Once successful, expertise can be extended to the next preference from a different function. In this way, AI can grow into the entire operational framework and produce time-guided, tangible results.

AI and machine learning have significant implications in the pharmaceutical industry with benefits for both business and customers. The time to adopt advanced technologies and scale with AI is now. For more information on how you can harness the power of AI and machine learning in the pharmaceutical industry, reach out to DforD experts today.

 

 

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