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Artificial intelligence in Pharma
May 28, 2021

Here at Talking Medicines we have always strived to stay at the forefront of technology when it comes to improving the connection between Pharma and Patients. One technology that we utilise in our services is Artificial Intelligence. This blog breaks down how AI is being used throughout Pharma as well as how we are using this technology at Talking Medicines.  

As in the wider digital universe, the most striking features of the emerging landscape for artificial intelligence are speed of technological evolution and the range of potential applications. Those applications relate directly to industry processes and activities (particularly in R&D), or indirectly to the broader healthcare environment, where AI has inescapable implications for the way pharma operates.

It is debatable whether that pace of change is matched by uptake that recognises AI’s full potential to facilitate or accelerate innovation, efficiency or access to care. Many healthcare systems and pharmaceutical companies, though are already forging ahead with AI initiatives.

As the Boston Consulting Group points out, there are three main drivers for AI in healthcare:

  • Mounting pressure to contain or reduce costs and improve outcomes

  • The explosion in available health data, including genomic analyses, electronic medical records, and information from monitoring devices such as wearables

  • Advances in digital software and hardware that enable those data to be leveraged in new, powerful ways.

BCG predicts that expenditure on AI-related tools will exceed US$8 billion annually by 2022 across seven segments of the healthcare chain: remote prevention and care; diagnostics support; treatment pathways and support; drug discovery and development; operations; sales and marketing; and support functions. That forecast includes a projected US$1.3 billion spend on AI-related drug discovery and development tools by 2022.

BCG envisages pharma gaining significantly from AI-driven efficiency improvements in R&D, sales, marketing, and manufacturing. It also sees ancillary benefits for technology companies active in pharmaceuticals, as algorithms for target identification, lead optimisation or clinical-trial recruitment become more ingrained R&D processes.

At the same time, concerns persist about issues such as data privacy/security and upfront costs. In a KPMG study of AI adoption in healthcare, 37% of healthcare-industry executives felt the sector was implementing AI too slowly. Yet 54% believed AI had so far increased rather than reduced the cost of healthcare, while 75% were concerned that it could compromise the security and privacy of patient data.

This suggests AI in healthcare has some bedding-in to do, for all its recognised utility across functions ranging from earlier diagnosis of disease to process automation in areas such as patient screening and records management. In the KPMG study, 89% of healthcare-industry respondents said AI was already improving system efficiencies and 91% felt it is was increasing patient access to care.

AI adoption in the broader healthcare environment presents both opportunities and challenges for pharma. Earlier and more precise diagnosis of disease could lead to more timely therapeutic intervention with better targeted medicines.

Machine learning insights into patient pathways should help identify where and how pharma can offer products and services that deliver precision medicine and patient/system-oriented value through improved outcomes. Data-mining of patient records promises to enhance understanding of diseases, rapidly identify suitable patients and investigators for clinical trials, and inform more viable, innovative study designs.

So, how are we utilising AI at talking medicines? Our product PatientMetRx provides trusted social intelligence insights for the world’s leading drug brands. Our AI enabled data collection is driven directly by Patients and results in more effective marketing for the Pharmaceutical sector.

Find out more at https://www.PatientMetRx.com 









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