In a recent webinar, we presented pharmaceutical industry use cases of artificial intelligence (AI) and machine learning (ML), stressing how the techniques have progressed to the point that they can be of use at every stage of the product lifecycle…
Currently, the richness of available data, new algorithms, and advances in technology are fueling novel solutions in health sciences that are powered by AI and ML. This is allowing companies to optimize their commercial activities, providers to make better informed treatment decisions, and patients to address risks and engage in appropriate disease management in a timely fashion.
As not everyone shares the same definition of AI, it’s important to clarify how we use the term and those related to it. We regard AI as any technique that enables computers to mimic human intelligence. Machine learning (ML) is a subset of AI that uses statistical methods and iterative processes to enable machines to gain experience and improve a result. Two forms of ML are deep learning and natural language processing (NLP). In deep learning, machines learn via a supervised or unsupervised process using data that is unstructured or unlabeled. With NLP, programs allow computers to make sense of human language when it is spoken or written.
For life sciences companies, the greatest benefits of AI are realized when the techniques are used to develop an integrated solution that delivers a holistic view of the situation. In one example, a manufacturer had launched a brand with great success, only to see sales begin to flatten after several months. Although the brand team had some hypotheses as to the cause, they asked IQVIA to determine the performance drivers for the brand, to recommend how the brand could be optimized, and to estimate its potential.
IQVIA tapped a number of databases (the client’s own promotional data and target lists, IQVIA Channel Dynamics, prescription data, longitudinal patient data, and primary market research. Using IQVIA’s analytics platform, they ran a range of econometric and ML models to quantify brand growth from various pockets of value.
As a result, IQVIA was able to quantify the optimal marketing mix on brand performance in terms of spend level, channel mix, geographic alignment, and message content. We also recommended the best patient segmentation, identified the drivers of patient adherence, and estimated the lifetime value of a patient. The company learned that overall, its multi-channel marketing program was effective, and that increasing the frequency of face-to-face sales efforts would deliver only limited incremental gains. The real improvement was to be found in making targeting changes.
To learn more about the applications of AI and ML in solving your commercial challenges and to view the slides of the webinar, contact Patrick Van Dooren, Sr Director Commercial & Technology Services IQVIA Belgium and Luxembourg.