Although closely linked to the data science use cases in healthcare, data science use cases in pharma are geared toward the development of drugs, physician marketing, and treatment-related analysis. Examples of data science in pharma include the following:

• Patient journey and treatment pathways: Understanding the progression of diseases in patients and treatment or therapy outcomes is one of the prime examples of data science in pharma. Several companies have engaged in deep studies related to the development of such tools to understand not only the efficiency of drugs, but also how to best position and market their products. (Source: https://kx.com/blog/use-case-rxdatascience-patient-journey-app/).
• Sales field messaging: Using NLP, pharma companies analyse discussions between sales representatives and physicians during sales visits to improve their messaging content and better inform physicians on the potential risks and benefits of medications as needed. (Source: https://www.aktana.com/blog/field-sales/power-personalization-using-advanced-machine-learning-drive-rep-engagement/).
• Biomarker analysis: Machine learning for identifying biomarkers and their importance and/or relevance to diseases are used in clinical research such as cancer-related studies. (Source: https://www.futuremedicine.com/doi/abs/10.2217/pme.15.5?journalCode=pme).
• Research and development: The use of machine learning for identifying small and large molecules that treat diseases is another common application of data science in pharma. It is a challenging task and several large pharma companies have engaged teams to solve such use cases. (Source: https://www.kaggle.com/c/MerckActivity).