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 decades-old legacy systems. These disparate data silos prevent 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 place.
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 with –
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 clinically and economically better therapies 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 new ways of doing business have eclipsed them.
These challenges are offset by promising advances in collecting and analyzing healthcare data, coupled with Machine Learning capabilities. In the healthcare and pharmaceutical industry, data continues to grow exponentially due to 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 review their portfolios, supporting drug repurposing, rescue, and repositioning.
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