The process of researching, developing, and ultimately commercializing a treatment for patients, regardless of the therapy area, is a long and costly one with slim chances for success. 

For example, the average R&D investment in developing a new drug treatment is $1.3 billion, the median development time is 5.9 to 7.2 years for non-oncology and 13.1 years for oncology, and the probability of success in clinical trials is 13.8%. A litany of inefficiencies, such as an inability to identify the right patient cohorts to enroll in clinical trials, or low optimization of promotional marketing channel mixes, can lead to long and resource-intensive processes and delays with patient care.  

Recent advancements in artificial intelligence (AI) and machine learning (ML) are now giving pharmaceutical and biotech companies the ability to drive efficiencies in developing and providing new treatments to patients who need them. However, to take advantage of advanced AI and ML, the life science industry must move past their traditional approaches and move towards data consumption and analysis. 

Moving Beyond “The Way We’ve Always Done It”

Historically, the biopharma industry approached (and some continue to approach) their analytics and insight needs by using third-party traditional platforms or relying heavily on teams of consultants. 

While these solutions do provide insights, there are intrinsic factors that limit the capabilities and effectiveness of the two approaches.

With third-party traditional platforms, limitations include:

• Use of only one data source, which isn’t enough to provide insights into any therapy area
• Data and business rules that are created for the platform and not the treatment opportunity
• Little to no customization of key performance indicators for the target market
• Little to no flexibility to augment the platform or add disparate data to allow for a deeper dive or provide a broader view for further insights
• A need for multiple platforms and service providers

With the consultant-heavy approach, limitations include: 

• A process that, while fully customizable, is slow, not repeatable, and inefficient
• Solutions that are difficult to operationalize
• Reliance on specific resources and expertise 

As the amount of data in the world increases, so do the challenges associated with using the methods described above. In the biomedical field alone, on average, more than 1.6 million scientific papers are published each year, according to data from Definitive Healthcare’s Monocl ExpertInsight product – about three papers per minute. Robust, reliable data is essential for providing insights that guide decision-making within drug-treatment development.  

One main inefficiency of using traditional platforms and consultants is their inability to easily combine multiple large disparate data sets into one version of the truth.   

The ability to efficiently sift through an overwhelming amount of clinical research, claims data, electronic health records, marketing performance indicators, and sales data can provide the valuable insights drug manufacturers need. To do this “sifting,” advanced AI and ML capabilities must be incorporated into biopharma’s drug treatment development and commercialization processes.

Outcomes of Adopting Advanced AI and ML

Biopharma companies using advanced AI and ML for data analysis will help develop new tests, treatments, and procedures in the future – as well as find opportunities to better identify ideal patients for clinical trials, understand the demographics of patients’ market share, and improve promotional marketing of treatments to patients and their healthcare providers.  

Pharma and biotech organizations can benefit from using AI and ML by: 

• Identifying eligible patients with rare indications/conditions
• Dynamically targeting physicians who treat a patient demographic
• Accelerating clinical trial enrollment by identifying eligible patients
• Optimizing patient compliance and patient support activity

Embracing advanced AI and ML provides a scalable way to harness data to inform decision-making and act on market opportunities.  

Looking Ahead to 2023 

AI/ML-generated health care commercial intelligence is critical in helping biopharma companies create new and more efficient paths to get the right drug to the right patient at the right time. 

Using advanced AI/ML technology to interpret, analyze, and integrate the vast amount of ever-growing data will drive the next wave of innovation in therapy development and commercialization. 

With expectations for biopharma companies and the FDA to expeditiously and effectively develop treatments for new diseases (e.g., COVID vaccines) and an increased focus on improving patient health care, the need to use advanced AI and ML couldn’t be higher.