Connected data gives you a clearer picture
At Evaluate we collect, calibrate and connect the pharmaceutical world's data.
These days we are not short on buzzwords around artificial intelligence. While experts can wax lyrical about the differences between AI, machine learning, and natural language processing, mere mortals are often left scratching their heads. For many people, it’s all a black box that’s exciting, confusing, and scary in equal measure.
However, for those of us in the life sciences market, it doesn’t pay to bury our heads in the sand on this topic. Why? Because inaccurate risk assessments and poor strategic decision making cost the industry billions of dollars in wasted research and development (R&D) spend every year. In short, decision making on clinical development is compromised.
When it comes to strategic decision making around pipeline prioritisation, there are four key areas that require evidence-based insights to inform strategic decision making:
None of these is straightforward, and all of them have multiple data points, some of which are constantly shifting.
Which aspects of the pharmaceutical value chain challenges does Intelligent Forecasting address?
There are various challenges across the pharma value chain that companies struggle with while forecasting the pipeline. The challenges are both internal and external and staying on top of these and incorporating those in forecasting decisions are critical. Intelligent Forecasting focusses on addressing some of these key challenges.
Unlike traditional forecasting methodologies, it considers the past, present and future drivers of commercial and clinical success in real time. It is a forecasting methodology that allows complex real-world clinical development and commercial questions to be addressed dynamically and with up-to-date information.
How does it really help?
At a high level, intelligent forecasting applies machine learning technologies to lever millions of historical, clinical, and commercial events. This means it can provide real world decision support that businesses can use to inform decision making across the entire lifecycle of a therapeutic product, including preclinical discovery, overcoming historical and emerging forecasting challenges.
Intelligent forecasting can also identify key risks and highlight return correlations that uncover insights into asset development and commercial opportunity. It does this by tracking millions of data points curated from thousands of products across the entire clinical pipeline and commercial outcomes.
Deploying intelligent forecasting to analyse historical datasets can identify signals of potential success, or failure, for products at all stages of the pipeline. Then, when combined with historical commercial datapoints, translate these insights into commercial value it can accurately predict the future clinical and commercial outcomes.
Here’s a quick summary of the key differences between intelligent forecasting and conventional, or bottom-up, forecasting.
The big takeaway here is that intelligent forecasting is adaptive and can be retrained to reflect recent changes more accurately, based on new evidence and data sets. Not quite a crystal ball, but definitely the next best thing.