DATA & AI PROJECTS IN THE PHARMACEUTICAL INDUSTRY
Stay competitive with the use of Data Science and AI in your business. You already know what project you would like to implement or want to learn what options are available? Our experts will help you from idea generation to implementation in your operations.
EXPERTISE IN INDUSTRIAL SOLUTION
We have used our experience from over 1.000 projects in the last 6 years to develop a holistic system for data & AI projects – our [at] Data Journey. A consistent data strategy forms the basis and the framework for the efficient use of data in your company. The goal is to test Use Cases as quickly as possible in order to develop a prototype with real data from the concept in a timely manner. In the Data Factor the Use Cases are industrialized into finished products. The absolute main focus is on scaling and the sustainable generation of added value – as such the user is just as much the focus here as well. In our DataOps, we operate and maintain your platforms and machine learning algorithms.
Since our founding in 2015, we have become a leading provider for Artificial Intelligence, Data Science and Big Data in the UK. Together with our customers, we generate real added value from data. To this end, we develop and implement data-driven innovations and new business models. We empower our customers to develop their own strengths and accompany them on their journey from a data strategy to the development of algorithms and the construction of IT architectures to maintenance and operation.
OPPORTUNITIES OF AI FOR THE PHARMACEUTICAL INDUSTRY
DEVELOPMENT & EXAMPLES OF AI PHARMA
IQVIA research highlights ten potential areas, including the use of digital applications in healthcare, artificial intelligence (AI) and machine learning (ML), next-generation biotherapeutics and insights from healthcare practice.
According to this research, the use of artificial intelligence and machine learning will soon become the norm for life science companies. Currently, the most advanced method is to use intelligent algorithms to analyze large and complex data sets, especially in clinical and preclinical research. The algorithm is used to screen preclinical drug candidates for new drugs and identify potential targets based on supply data. Overall, they have been found to improve the efficiency of clinical development. However, to better subdivide patient groups or to better identify undiagnosed patients, predictive analysis supported by ML can be used.
According to IQVIA, the U.S. Food and Drug Administration (FDA) is receiving more and more requests to approve mobile apps for therapeutic purposes. These digital therapies (DTx) require prescriptions and the use of digital technology for treatment. They are expected to make significant progress, particularly in the areas of health behavior and cognition. On the other hand, the evaluation of new treatments by other stakeholders is more rigorous because their benefits have not been demonstrated in practice.
More examples
• Information about competitors can be collected, analyzed and prioritized more quickly
• Chatbots provide automated feedback to patients and healthcare providers
• Patient cohorts can be identified and thus recruited for research studies
• Advanced analytics helps identify suitable drugs more quickly for alternative applications