AI Solving Pharma Challenges
CHALLENGES FACED BY THE PHARMA INDUSTRY
For Big Data transformation in the pharmaceutical industry to succeed, organizations must transform
to overcome the challenges they face in an ever-changing technological environment.
Healthcare’s biggest data challenge is its historical siloed structure resulting from decade-old legacy
systems. These disparate data silos prevent the access to complete patient profiles in real-time. It
is essential going forward that organizations develop a data-centric approach allowing for the
complete sharing of data across all channels – the right information at the right time in the right
Pharmaceutical companies are further challenged with developing new drugs with greater efficacy
and minimal adverse effects. And to do so in an environment that results in greater development
success rates, lower discovery costs and more direct access to patients in need.
In Pharma 2020, PWC identified three major challenges pharmaceutical companies are faced
Rising customer expectations: The commercial environment is getting harsher, as healthcare
payers impose new cost constraints on healthcare providers and scrutinize the value medicines offer much more carefully. They want new therapies that are clinically and economically better than the existing alternatives, together with hard, real-world outcomes data to back any claims about a medicine’s superiority.
Poor scientific productivity: Pharma’s output has remained at a stable level for the past decade.
Using the same discovering and developing processes, there’s little reason to think its
productivity will suddenly soar.
Cultural sclerosis: The prevailing management culture, mental models, and strategies on which
the industry relies on are the same ones it’s traditionally relied on, even though they’ve been
eclipsed by new ways of doing business.
These challenges are offset by promising advances in the collection and analysis of healthcare data
coupled with Machine Learning capabilities. In the healthcare and pharmaceutical industry, data
continues to grow exponentially as a result of the R&D process itself, patients EHRs, and feedback
provided by caregivers. Effectively utilizing these data points will help pharmaceutical companies
better identify new potential drug candidates, develop them effectively, and ultimately getting
new medications approved and reimbursed more quickly.
Furthermore, AI will offer new insights when pharmaceutical companies are reviewing their
portfolios supporting drug repurposing, rescue, and repositioning.
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