Clinical trials are essential to the development and implementation of safer and more effective devices and medications, but clinical trial success rates have been shockingly low. The likelihood of a new drug advancing to the next trial stage or regulatory approval is less than 14% across all therapeutic areas. These high failure rates are often attributed to the tremendous amount of complex data in today’s clinical trials.
Now, artificial intelligence (AI) is transforming the way clinical trials are conducted, which will likely lead to more accelerated, accurate and efficient trials. AI’s enhanced automation is better able to:
- Gather, manage and analyze massive amounts of complex data.
- Streamline resources and boost efficiencies.
- Improve clinical trial design and outcomes.
- Leverage key learnings from legacy materials.
- Provide more accurate results.
- Elevate the clinical trial process and drive more successes.
Managing Data in Today’s Clinical Trials
Today, we have more data—and more tools available to collect it—than ever before. The healthcare industry, in particular, is experiencing an exponential increase in data. The doubling time in 1950 was 50 years, in 1980 was 7 years, and today, medical data doubles every 70 days. Currently, a Phase 3 trial generates an average of 3.6 million data points, triple the amount of data collected in clinical trials 10 years ago.
This is a positive development for clinical trials. The more data that is available for a clinical trial, the stronger the case an organization can make to regulatory bodies. However, it does increase the burden on regulatory professionals and clinical trial investigators who must organize and sift through huge amounts of data.
One of the challenges of analyzing large amounts of data is that data has traditionally been siloed. The paper records of the past made it difficult to cross silos, but even with the invention of electronic health records (EHRs), there weren’t always standards to help dictate how outside bodies could access this information. Now, with Fast Healthcare Interoperability Resources (FHIR) and Health Level 7 (HL7) standards for health information, data is much more accessible. And AI-powered tools can expertly manage this data, providing richer data analysis that can uncover deeper insights and recognize essential patterns.
AI Benefits Beyond Data Analysis
Today, AI not only improves data collection and analysis, it impacts which products are engaged in clinical trials, determines necessary medical criteria, helps design the trials and can even choose participating facilities.
For instance, AI-powered tools can help decide the properties of a clinical trial, such as how many people are needed for the trial to be meaningful. It can learn about past clinical trials’ successes and failures and help investigators learn from previous mistakes and best practices. AI can also help determine inclusion/exclusion criteria, such as participants’ minimum age and required medical conditions.
Additionally, AI can help researchers evaluate institutions’ electronic health records and select sites for clinical trials based on their specialties and whether they have enough patients that meet a trial’s specific criteria.
AI can also be valuable in doing the “pre-work” prior to the clinical trial, using computer models and simulations to determine which clinical trials have a higher probability of success. AI-powered tools can screen out the drugs and devices that likely won’t be viable, saving significant time, effort and money by accurately eliminating potential failures.
The Advantages of AI-powered Tools
The clinical trial process has typically been long, slow and expensive. Now, AI is helping to change that. If we can make the development and approval process for medications and medical devices better, faster and less expensive, we can, ideally, pass along the savings to the people who need them.
In a nutshell: organizations that are not using AI will be left behind; and organizations that leverage AI will be more successful and will go to market faster than those that don’t.
The current ways of collecting data will be around for many years. However, moving forward, AI tools will help sponsors collect their data more accurately—through more automated processes. By combining the power of AI with the knowledge and experience of healthcare providers and researchers, developers will be able to get their innovative products to the people who need them faster and more efficiently.