DATA & AI PROJECTS FOR pharmaceutical companies
We have used our experience from over 1,000 projects in the last 8 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 using data efficiently 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 the Data Factory, use cases are industrialised into a finished product. The absolute focus is on scaling and sustainable generation of added value - which is why the user is also the focus here. In our DataOps, we operate and maintain your platforms and machine learning algorithms.
Since our founding in 2012, we have developed into a leading provider of artificial intelligence, data science and big data in the German-speaking region. Together with our clients, we generate real added value from data. To do this, we develop and implement data-driven innovations as well as new business models. We empower our clients to develop their own strengths and accompany them on their journey with our [at] Data Journey - from data strategy to the development of Algorithms and the construction of IT architectures through to maintenance and operation.
Opportunities of AI for the pharmaceutical industry
Reduce development time
Development time of new drugs can be shortened
Access to all relevant data with a data warehouse
Make more effective and intelligent decisions
AI assists in finding a suitable dose of active ingredient
Measure patient satisfaction and treatment adherence
Reduce resources through process automation
Projects of our customers
We have proven our data science and AI expertise in the pharmaceutical sector for various projects. Read some references on AI in the pharmaceutical industry here. If you have any questions, please feel free to contact us.
AI-based visibility of the medication position in the entire "cold chain
NLP-based text analysis to reduce the time needed for drug discovery and development
Timely prediction of unacceptable quality deviations
Intelligent system for automatic adjustment of the parameters for material disposition
Drug approval delay based on centralised approval management
Biopharmaceutical performance optimisation
Industry Exchange #KIpharma
Compared to other industries, the pharmaceutical industry has so far neglected artificial intelligence. Yet it is precisely here that many opportunities present themselves: AI can predict reactions - and even suggest reactions that produce certain active ingredients. In our Industry Exchange, experts from the pharmaceutical sector came together and talked about the opportunities, challenges and also the reasons for the comparatively hesitant use of AI so far. The discussion also covered regulatory and ethical requirements. And ultimately also about encrusted leadership cultures in an established industry. Take a look at our Industry Exchange #KIPharma at any time and free of charge.
DEVELOPMENT & EXAMPLES OF KI 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 health practice insights.
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 analyse large and complex amounts of data, especially in clinical and preclinical research. The algorithm is used to test preclinical drug candidates for new drugs and identify potential targets based on utility data. Overall, they have been found to improve the efficiency of clinical development. However, to better subdivide patient groups or better identify undiagnosed patients, predictive analysis supported by ML can be used.
According to IQVIA, the US 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 advances, especially in the areas of health behaviour and cognition. On the other hand, the evaluation of new treatments by other stakeholders is more rigorous, as their benefits have not been proven in practice.
- Information about competitors can be collected, analysed and prioritised 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 medicines more quickly for alternative applications