In the upcoming decade, there will be a big trend within the pharmaceuticals industry to adopt RPA. Drug discovery is shifting. The ability to develop and deliver pharmaceuticals from the lab to the pharmacy shelf at a faster rate has become more possible with the introduction of RPA to the clinical trials industry.
The execution of life sciences trials has not changed greatly in the past decades.
Typically, ever-increasing costs and vast failure rates, and low rates of drug discovery has created an inefficient approach to experimentation. Improving the efficacy of clinical trials and executing life sciences experiments will give greater confidence about the safety of new pharmaceuticals with smarter methods of accelerating the journey from laboratory to market, and to healthcare patients.
However, the use of data has changed greatly.
Traditionally, the first step of drug discovery involves consistency, precision, and cost-efficacy. With RPA, better decisions can be made earlier on in the process when more valuable, reliable data is collected. This way, better results can give way to improved pharmaceutical research. 75% of life sciences research takes place in five countries (typically the wealthiest), due to the expenses of R&D. AI is becoming more and more important in healthcare, and as “ramping up” the effects of diverse data accelerates the need for AI and related technologies, modern data science and 21st approaches will step in to fill in the spaces where the pharmaceutical industry is lagging.
Leveraging AI within the pharmaceutical industry will help to arrange those amounts of data into important patterns. Observational data, clinical results, imaging biomarkers, and a number of other data methods will help to bring machine learning to the forefront of the pharmaceuticals industry. As new medicines enter clinical development, smarter use of reliable, diverse data can power analytics to improve the likelihood of new and effective medicine.
Moving away from legacy approaches
Specifically, Robotic Process Automation (RPA) can develop and validate automated solutions for repetitive tasks, and help to ease the process of adoption as the legacy approaches are retired. Personalized medicine and individualized therapies are a trend within the drug discovery community, rather than the “one size fits all” approach—attempting to match one drug with a million patients.
RPA provides these opportunities for greater acceleration with machine learning.
Overall, technology within the pharmaceutical industry that works to unpack the complexity of data will be transformational. Cross-functional teams diverse in both thought and experience will progress into the future with discipline and vision.
Confiance can help with leveraging technology to make profound changes within the drugs industry. Get in touch if you’d like to learn more about how you can standardize and derive excellence in business processes in the Life Sciences and Biotech space.