A Pragmatic Approach To Technology In Clinical Research

Gadi Saarony is Chief Executive Officer at Advarra, a market leader in regulatory review solutions and clinical research technology.

There is an explosion of innovation in drug development. In 2023, the FDA approved 55 novel drugs. There were five approved gene therapies for rare diseases, including the first-ever FDA-approved treatment to use a novel genome editing technology as well as “the first cell-based gene therapies for the treatment of sickle cell disease.”

Yet, there has been little to no progress in accelerating drug development timelines. While the Covid-19 vaccine trials moved at a rapid pace, that was an anomaly rather than the norm. The average time-to-market for a new drug is 12 years—a long time for patients who are waiting for life-changing therapies. The cost of developing new drugs continues to grow—exceeding $4 billion, depending on the therapeutic.

There are many reasons as to why trials are so slow and expensive. The clinical research industry operates under heavy regulation, a necessary safeguard for patient safety. In addition, trials are becoming increasingly complex, particularly with the rise of advanced treatments like cell and gene therapy. Moreover, these cutting-edge therapies often target smaller patient groups, making recruitment more challenging and time-consuming.

The Promise Of New Technology

Does technology hold the key to accelerating timelines and decreasing costs? Right now, generative artificial intelligence (AI) is at the forefront of discussions in nearly every industry. We are seeing it used to generate training materials, answer customer queries and even develop code. The potential that AI offers is exhilarating, particularly when we think about going beyond “pure AI” like ChatGPT to solutions that are specific to an industry.

In the clinical research industry, AI and machine learning (ML) are already being tried out in preclinical, study design and patient recruitment applications. We are seeing use cases for drug identification, selection and prioritization. AI can be used to match individuals to trials. AI can also help optimize site selection and collect, manage and analyze clinical trial data.

However, AI is not without its challenges, and I believe it should not be seen as a panacea for all that stands in the way of faster, cheaper drug development. For example, the clinical research industry is still grappling with issues of bias, security risks, inaccuracies and patient and data privacy as well as regulatory implications for AI—all of which must be explored and addressed. While the goal is to reduce costs and streamline processes, there’s also the matter of the cost and time required to implement AI/ML solutions.

In the meantime, organizations across industries are feeling the heat—and the fear of missing out—when it comes to AI and other breakthrough technologies. However, not every technology is right for every setting or implementation. As in all applications of technology, researchers will succeed when they pragmatically consider the tools that are best for their organization and strategy and find solutions to fit problems—not the other way around.

Slow Down To Speed Up

As technology leaders, we sometimes make the mistake of thinking too ambitiously about technology’s role in leading change. A thousand exciting tools are out there for health researchers—but it’s how they will deploy, connect and use them in novel ways that will make a difference in timelines and costs for clinical research.

While I am excited and optimistic about AI, I also know that some of the challenges facing clinical research are a result of disparate technology solutions that make jobs harder, not easier. When looking to solve a problem, it’s not enough to simply bolt on another technology solution without realistically assessing its impact. In some cases, you can be creating new problems.

We recently spoke with an oncology researcher who had two dozen different IT systems running at once, each for a single element of the trial and each with its own usernames and logins. Technology in this case has made some parts of their job more difficult and less efficient.

This situation illustrates that, rather than adding on new technologies, the focus should be on streamlining and connecting technologies. In the early stages of a trial, steps like building a budget, enrolling patients and more can last months or even years depending on trial complexities and regulations. It’s this part of the clinical trials process that the industry has been struggling to optimize for my entire 20 years in the field.

While technology is part of the challenge facing clinical research today, it is nonetheless also part of the solution. I challenge my fellow health technology leaders to pursue a standard of interoperability among data, point solutions and the entire clinical research ecosystem. An ecosystem that is open and dependent on a few best-in-class technologies that fit together can operate much more efficiently than one that is dependent on countless siloed tools and thousands of inputs.

Uncomplicated tools that facilitate better interactions between doctors, investigators and patients sound so simple, but the results would be revolutionary. I see AI as a complementary solution, which will be even more effective within a connected and simplified technology ecosystem.

Before adding the complexity of AI to the technology mix, we need to be thoughtful about the implications—particularly for security and data privacy. We also need to be sure that more technology is warranted. AI and other technology tools should be used in a targeted and connected manner to alleviate staff burden and automate tasks, pull together vast amounts of data to improve decision-making and streamline processes that will ultimately lead to faster and more cost-effective processes.

In clinical research, we are on the precipice of approving treatments for some of medical science’s biggest challenges, from Parkinson’s to Alzheimer’s to multiple sclerosis. While AI holds so much promise, it will take some time to fully realize transformative gains. Site staff should be allowed to focus on the research and the patients. By taking a pragmatic approach to the technologies that support research, we can get there even faster—helping patients who need it the most.


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