According to a survey of 1,200 global companies sponsored by Dataiku in September 2020, 66% of life sciences or pharmaceutical organizations believe AI is either considerably or very important to the future of their business. This goes to show the immense potential that AI and machine learning have in the pharmaceutical industry.
Amongst other things, pharmaceutical organizations can leverage these technologies to: expedite the time-consuming task of medical information collection and processing, enhance availability of data and medical records, streamline the drug discovery and R&D process so it takes less time and is more cost effective, and more. In this blog post, we will explore different use cases that will help companies thrive in the developing AI-powered pharmaceutical industry.
Through collaborative, transparent, and explainable AI efforts, there is significant potential to increase efficiencies and improve patient outcomes. One of the main uses of these AI efforts is predictive analytics. Predictive analytics refers to micro-level predictions — that is down to a specific individual — rather than macro-level predictions based on averages or generalities. Let’s explore two examples of predictive analytics used in the pharmaceutical industry.
- Early Disease Identification: With predictive analytics and pattern recognition, doctors can better understand and diagnose symptoms, run tests and analyze those results, monitor the outcomes from any previous treatments, and move on to the next treatment plan. Analytics speed up the process of disease identification, thus moving to the treatment phase faster and bringing in pharmaceuticals sooner. This means patients can receive the correct care or treatment earlier in their patient journey, possibly saving them from later stage risks.
- Potential Patient Identification: Another example of how predictive analytics can be used in the pharmaceutical industries is that of clinical trial potential patient identification. With advanced analysis of patient history and medical records through natural language processing (NLP) or by exploring geographically and symptom-distinct patients at scale, AI platforms can help identify patients who would be a good fit for a particular trial more quickly and precisely. Going a step further, such techniques can also examine the interactions of potential trial members’ specific biomarkers and current medication to predict the drug’s interactions and side effects, avoiding potential complications.
Manufacturing and Supply Chain Applications
AI can have a major impact on the pharmaceutical industry in much the same way that it can bolster any other business: by helping companies better understand who their customers are, where they are, and how to reach them.
- Optimizing and Automating Production: As drugs are increasingly customized to small numbers of patients with certain genetic profiles, identifying the most efficient supply system by optimizing and automating steps of production will become even more critical.
- Demand Forecasting: Through collaborative data science tools, pharmaceutical organizations can better forecast demand and distribute products more efficiently. This is important for both general availability (what you can get at the pharmacy) as well as managing clinical trials.
- Predictive Maintenance: Predicting when or if necessary recruitment will fail so that maintenance and repair can be scheduled in advance of the failure is especially sensitive in the pharmaceutical sector, given the rapid time-to-market required for pharmaceutical products and the vast amount of pharmaceutical equipment required in a production run. One machine failure in a pharmaceutical manufacturing plant can cost the company hundreds of thousands of dollars in scrapped batches.
Sales and Marketing Use Cases
Last, but not least, let’s go through a couple examples of common pharmaceutical industry sales and marketing use cases.
- Geographical Sales Optimization: Pharmaceutical organizations often send sales representatives to hospitals and physician offices. It’s important to be able to identify which geos are underperforming so they can increase the amount of visits.
- Sales Visit Optimization: The visits mentioned above need to be efficient, so organizations can use analytics to avoid too many representatives from visiting the same hospitals (sales reps from the same company selling different drugs).
- Adhering to Do-Not-Contact Lists: Data privacy guidelines have become increasingly critical across many industries, and pharmaceuticals is no exception. Some physicians do not want to be contacted by pharmaceutical organizations at all, while others only want to be through specified channels (i.e., only email).
- Personalized Marketing: Teams can leverage advanced analytics for marketing automation and personalization, resulting in innovative campaigns that will drive customer engagement (i.e., create a predictive model that scores target customers for their propensity to open an email).
Get Ready for the Ride
The 2020 global health crisis has accelerated this newly AI-powered pharmaceutical industry, so organizations need to be able to pivot their operations effectively to cope with the ongoing changes and make the most of what AI can offer. The use cases mentioned above represent only some of the many ways AI can benefit the pharmaceutical industry and with a continuous devotion to innovation, organizations will certainly keep on discovering and implementing new AI strategies.