7 Use Cases of Predictive Analytics with Machine Learning & AI in the Pharmaceutical Industry

We live in a data-driven world where the majority of industry verticals rely more on data than ever before. They leverage vast amounts of data to gain actionable insights and make strategic decisions. Among all, the pharmaceutical industry is one. Whether it is a clinical trial, medical research, manufacturing, or drug development, this industry significantly relies on data.

With vast amounts of data available, pharmaceutical companies need a tool or technique to analyze all data and convert it into foresight. This is where the role of predictive analytics comes into play.

In this article, we will walk you through some of the key use cases of pharmaceutical predictive analytics. But before that, let us first briefly understand what predictive analytics is.

What is Predictive Analytics?

Predictive analytics refers to analyzing historical and real-time data to uncover hidden patterns and using them to predict future trends and possibilities. It employs artificial intelligence, machine learning, data mining techniques, and statistical modeling to analyze large datasets and identify relationships between data variables.

Simply put, predictive analytics consolidates and converts disparate data into actionable insights, helping businesses make future decisions.

How is Predictive Analytics Used in the Pharmaceutical Industry? 7 Use Cases

Customer satisfaction has become the heart of every business. So, pharmaceutical manufacturers are introducing predictive analytics into their operations to better understand customers and improve product development and marketing strategies.

According to the report published in 2017, 93% of healthcare executives stated the importance of predictive analytics for their businesses’ future success.

Pharma predictive analytics streamlines business operations, enhances productivity and improves profit. Also, it assists companies in gaining visibility into future outcomes, monitoring anomalies, and forecasting product demands.
Whether it is developing better products or services, improving customer satisfaction, or staying ahead of the market, companies can use predictive analytics for witnessing a great impact.

Let us discuss some potential use cases of Pharmaceutical Predictive Analytics.

1. Drug Discovery and Development

Predictive analytics in drug discovery plays a crucial role in streamlining the entire process of drug development. The analysis of vast datasets and clinical trial outcomes are responsible for identifying the potential drug candidate. However, clinical trials are complex and time-consuming, with a success rate of only 14%. Predictive analytics has the potential to optimize this entire process.

Pharmaceutical companies leverage machine learning algorithms to identify drawbacks in the early stages of drug development. Classification algorithms help them forecast the likelihood of a particular compound’s success and what type of drug can be retained. Further, companies can predict the products’ market demands so that they can prioritize development.

These predictive insights considerably improve R&D productivity, mitigate risks, and enable organizations to make informed decisions about drug development and distribution.

2. Clinical Trial Optimization

As mentioned above, clinical trials are tedious, resource-intensive, and time-consuming processes. The major challenge involved in a clinical trial is the patient recruitment cost.

Predictive analytics optimizes the clinical trial process by identifying and selecting patient groups with characteristics that perfectly fit a specific type of clinical trial. It also forecasts patient recruitment costs and potential risks and challenges.

With predictive models, companies can reduce test subjects for research and ensure that only those groups that are fit for trials will be involved. This way, they can increase the chances of trial success, minimize resource wastage, accelerate time to market, and significantly reduce trial costs.

3. Drug Safety Assessment

The most critical aspect of drug development is safety. The reason is that any adverse drug reaction (ADR) can lead to legal implications.

Predictive analytics for drug safety assessment proves to be an effective method, as it leverages advanced analytics techniques to evaluate potential risks associated with drugs. Also, it uncovers specific patient groups who cannot tolerate certain drugs and are at risk of experiencing ADR.

4. Personalized Medicine

Isn’t it amazing to get tailored treatment plans and support services? Obviously, yes! Predictive analytics for personalized medicine has been a game changer. It has the potential to forecast patient outcomes and create tailored treatment plans.

Predictive analytics analyzes massive amounts of datasets comprising a patient’s medical history, lifestyle and genetic data, and treatment outcomes. It leverages sophisticated ML algorithms to discover patterns and trends and generate insights into a patient’s preferences and potential health risks. With these predictive insights, healthcare professionals can create personalized treatment plans and support services.

Moreover, pharmaceutical companies utilize predictive analytics to understand product demands. It generates models based on the product consumption density for a specific location, demographic, and health index of the patient base. These models assist them in better understanding the customer base and knowing drug composition and approximate quantities for production.

5. Sales Operations and Performance

In the pharmaceutical industry, only developing and manufacturing a product is not enough; selling it is equally crucial. However, managing sales operations is an ongoing challenge for pharmaceutical companies, as sales representatives need to stay ahead of constantly evolving digital tools and the research body. They need to be relevant and valuable to doctors and clients. This is where the role of machine learning in pharmaceuticals comes into play.
With clustering techniques, companies can group medical practitioners based on relevant parameters and create specific customer segments. This way, they can implement personalized sales strategies that seem more relevant to doctors and clients.

6. Site Health for Clinical Trials

Another potential challenge involved in drug development is the health of sites used for clinical trials. A report found that 2/3rd of investigative sites do not meet patient enrollment requirements, and 50% of Phase 3 clinical trials fail due to poor site health.

Predictive modeling comes in handy to determine the site’s health. Machine learning algorithms leverage previous pharma data, such as the number of patients treated, protocol complexity, etc., and uncover factors responsible for site health. Plus, they determine the site’s likelihood of clinical failure. This way, ML models ensure streamlined research processes, smooth clinical trials, and improved productivity.

7. Equipment Efficiency

The cost of equipment failure in the manufacturing industry is hefty and devastating. Also, faulty equipment results in delayed production, which can negatively impact revenue. As a result, pharmaceutical companies employ predictive analysis to determine plausible scenarios for equipment failure. Predictive analysis uses every equipment’s data and machine learning algorithms to understand its working pattern.

When manufacturing teams know the possible equipment malfunctions beforehand, they can take preventive actions to avoid equipment glitches, hefty expenses, and delayed production. This is referred to as predictive maintenance.
According to a report by PWC, predictive maintenance in factories improves uptime by 9%, reduces the cost by 12%, and reduces quality risks by 14%.

Read More – Ksolves Expertise in Predictive Analysis by using Machine Learning

Conclusion

The adoption of predictive analytics in the pharmaceutical industry is increasing at a greater pace. It serves as an effective strategy for pharmaceutical companies to determine market trends, product demands, equipment efficiency, potential risks and challenges, and clinical trial site health. Its benefits are manifold, including reduced costs, improved operational efficiency, reduced waste, increased yields, and maximized customer satisfaction.

Do you want to harness the power of data-driven, predictive insights to make informed decisions? At Ksolves, we offer predictive analytics services that help you make informed decisions with our data-backed insights. With our predictive analytics solutions, you can improve customer understanding, optimize operations, forecast future sales, gain valuable financial insights, and predict equipment failure.

Make Ksolves as your expert partner!