We continue to see enormous technological and scientific progress in the area of drug discovery, with the promise of AI to upend traditional models and approaches becoming more fully realized by the day. However, there persists a glaring disconnect between these advancements and a measurable impact on the speed by which new drug candidates are reaching patients in need.
It costs an estimated $2.23 billion to bring a new therapy to market and an estimated 10 years for a treatment to go “from bench to bedside”. Given that an estimated nine out of 10 drugs still fail at some point in clinical trials, imagine all the potentially valuable therapies that never make it to patients. Heightened by increasing competition from China in early-stage drug development, there have been no shortage of opinions on potential remedies, regulatory and otherwise, for improving the pace and efficiency of clinical trials. Often lost in these discussions is what some might see as an unglamorous solution – leveraging emerging technologies that are simplifying and speeding how clinical trial data moves between sites and sponsors.
Until very recently, the process of clinical data collection and transfer has been slow to change, remaining stubbornly inefficient and labor-intensive. Clinical data is still, by and large, extracted manually by the research hospitals that serve as clinical trial sites, followed by the re-transcription and manual uploading of that data to the systems of the pharmaceutical companies acting as trial sponsors.
Not only do these manual processes create an environment ripe for harmful errors and delays, they also require enormous amounts of time and labor. It is estimated that 25% of total clinical trial costs are related to data verification and monitoring processes, which are largely driven by manual transcription and data quality issues. As a result, an estimated $10 to $20 billion dollars are spent globally every year on processes which provide no actual value to the drug development process or to patient care.
The good news is that a series of industry-wide technological and regulatory advancements is now enabling the collection and transfer of clinical trial data from hospitals to pharma sponsors at a drastically improved speed. The growing adoption of interoperability standards and frameworks such as HL7 FHIR (Fast Healthcare Interoperability Resources), alongside the 21st Century Cures Act, has made it increasingly feasible to access structured clinical data directly from Electronic Health Records (EHRs). At the same time, modern Electronic Data Capture (EDC) systems and site technologies have evolved to better support Application Programming Interfaces (API) integrations, reducing the need for manual transcription and enabling more seamless data exchange between sites and sponsors.
While the industry still faces challenges around the standardization of clinical data collection and documentation, new software-based data transfer approaches are accelerating the pace of recent progress and driving significant improvements in the consistency, quality, and timeliness of data flowing between trial sites and sponsors.
As a result, it is now possible to reduce the amount of time spent on manual clinical trial data entry in excess of 50% while also significantly reducing the opportunity to introduce human errors into the process.
As one might expect, capitalizing on these breakthroughs is not as easy as flipping a switch. Options abound for hospitals looking to capitalize on these new data transfer technologies, but so do potential pitfalls. For any hospital considering adopting these new solutions, here are some of the key questions and considerations that should be top of mind:
Is the technology truly agnostic? Major research hospitals often work with upwards of 50 different trial sponsors at any one time, each with their own distinct EDC platform or similar system. Any solution must be capable of connecting these numerous and disparate platforms. Choosing a technology with demonstrable interoperability across a hospital’s entire portfolio of sponsors and platforms should be the foundation from which any decision should be made. Otherwise, the ROI of adopting this technology and specifically the time saving benefits will be difficult, if not impossible, to achieve.
How can I trust but verify the technology? Potential partners should not only be able to demonstrate that their technology can work with every sponsor’s platform but also showcase their real-world successes in doing so with the global pharma companies that typically comprise most of the clinical trial activity at a research hospital. If they can’t do so, it’s likely their platform isn’t mature enough yet to deploy. Be direct about asking to speak with both their hospital and pharma customers about their experiences and to confirm there are no “creative embellishments” about the scale and scope of their partnerships. You should also request peer-reviewed research on the successful applications of their technology.
Is AI a feature or buzzword? These days, you can’t throw a stone without hitting a healthcare company claiming to be using AI in some unprecedented manner. AI is indeed being leveraged in highly innovative ways within clinical trial data management, including helping to glean important new insights from unstructured data. However, it is critical for hospitals to be able to look past possible marketing spin and buzzwords and ask for specifics on how a potential partners platform utilizes AI, how the data generated can be accessed, and its direct impact on improving the speed and quality of data movement. Any claims that AI can entirely remove the human element from the process tomorrow should be viewed with a very healthy dose of skepticism.
Is a tech partner willing to serve as my “wingman” with sponsors? Pharma sponsors typically provide the capital required to implement new clinical trial data technology. Is a technology partner willing to work with you to engage with sponsors to gain their support? Do they have a track record of doing so with current customers to which they can point?
How do I build an internal case for change? Adopting new technology, however much needed, can often run into resistance within hospital settings. Successfully implementing these new data interoperability solutions will typically require buy-in from a wide swath of internal decision makers, each of which will have their own priorities and “non-negotiables”. Establishing what the challenge is that you are trying to solve for sounds obvious on its face, but it is a critical first step in ultimately gaining consensus on what technology can fundamentally address the pain associated with it. Importantly, the ability of new software-based solutions to move clinical trial data from site to sponsor without introducing risks related to storing it externally can help overcome typical concerns raised by IT, legal and compliance teams.
While the dynamics and decision-making of each hospital will inherently differ, a potential technology partner should provide you with a clear roadmap of the steps they will take to establish a smooth implementation process and ensure that team members don’t sour on the technology before it even gets off the ground. Above all, and in all discussions both internally and with potential technology partners, the benefits that these emerging technology solutions could have on patients enrolled in clinical trials should be the “North Star”. The ability of these technologies to increase the efficiency of clinical data collection and transfer will have an unmistakable impact on decreasing drug development timelines in the coming years. Leveraging technology to provide those patients with potentially faster access to promising new treatments is something that everyone across the entire spectrum of healthcare should enthusiastically support.
Photo: Urupong, Getty Images
Richard Yeatman is Co-Founder and Chief Operating Officer of IgniteData, with over 15 years of experience spanning biopharmaceutical research, healthcare data, and enterprise software. He has contributed to peer-reviewed research on real-world data in clinical trials and is an advocate for interoperable, vendor-agnostic approaches to improving the efficiency and scalability of clinical research.
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