Real World Data can benefit multiple stakeholders within the value chain in order to support the commercialisation of strategies and provide insightful analysis for differentiation, to assist with regulatory submissions and to influence the market access strategies. However, it is not without its limitations and there a number of issues related to the consistency and repeatability of real world data in the pharma industry.
We spoke to two industry experts in order to better understand the role that RWD can play in drug development, improving patient outcomes and the speed at which products come to market.
Artificial Intelligence, Machine Learning and other technologies are expected to make the discovery and development of new pharmaceuticals quicker, cheaper and more effective. Download this article to hear experts insights from Leonardo Rodrigues, Senior Director, AI & Machine Learning at BERG Heath about how these tools will revolutionise drug development over the next decade and what the future of a drug discovery market looks like when driven by AI.
Sample attendee list for Data Analytics for Pharma R&D 2018.
While most industries are already benefiting from better use of dataanalytics, Pharma has been slow to innovate their business mode and adopt new technologies. Pharma R&D is stalling, there is a cap onproductivity and companies need to leverage data insights to move forwards. With insights from Larry Pickett, CEO, RxDataScience, this infographic provides a step-by-step guide to overcoming the key challenges faced in establishing and implementing a coherent data analytics strategy.
Data usage could lead to accelerated drug discovery, better help patients and predict emergency rush hours. To keep reading, download the PDF file.
With large scale initiatives coming into practice, such as the European Medical Information Framework (EMIF), Hughes shares his top tips for implementing a project like this. Read this interview to find out the key opportunities and threats of utilising real world data and how the rise of personalised/ precision medicine will impact the use of data.
Pharmaceutical companies have always relied on empirical data in order to identify patterns, test theories and understand the efficacy of treatments. Big data is just another evolution in a trend that has been continuing for hundreds of years: that of human beings having ever greater access to information and data. But to really garner the benefits requires a different way of looking at data. Here are 6 ways that pharmaceutical companies can use Big Data and analytics to generate business value and drive innovation.
The project in question is focused on exploiting genome sequencing efforts to discover new targets for cancer treatment. A huge opportunity for positive change. Download the Case Study to find out more.
The biggest challenge life science companies now face is to learn how to use data, source of an enormous amount of information, and step out of the silos. Ahead of the third annual Data Analytics for Pharma Development, PharmaIQ had an exclusive interview Marie Montoya, CEO and Founder or M Montoya Consultancy, to understand how data visualisation, once incorporated into a business strategy, can help life science companies turn their business around into a cost efficient model.