2020 has crystallized the fact that the drug development industry must finally address the many challenges that have long plagued clinical trial data. Decentralized clinical trials (DCTs), which gained appreciation in recent months, promise to bring innovation, and revitalize our industry. Yet, to properly support and enable DCTs, we must undergo a digital transformation; we must eliminate the pain involved in aggregating and integrating data from multiple sources, reduce the lag between data capture and data analysis, and improve the ability of study teams to gain deeper insights and operationalize the data.
 
Such transformation, once merely conceptual, now seems inevitable. Fortunately, it is now also achievable. With a forward-thinking strategy and the right technology in place, sponsors can successfully run flexible, dynamic DCTs.
                          
Bits, Bytes and Bolts
The drug development industry has a long history of suturing together bits and pieces from different systems to collect, analyze and visualize trial data, creating a technological Frankenstein. While all players in the industry rallied to adopt new patient- and site-centric technologies at the start of the pandemic, in many cases, these innovations were stitched onto already underperforming legacy systems. Meanwhile, a potential avalanche of data from real-world evidence (RWE), wearables, imaging biomarker labs, electronic Patient Reported Outcomes (ePROs), and electronic Clinical Outcomes Assessments (eCOAs) is exacerbating the core problems – centralizing data collection and analysis, and then converting that data into knowledge.
 
The Ideal Platform
If we had the luxury of designing a clinical trial technology platform unhampered by sunk costs in legacy systems, human resistance to change, and the industry’s general aversion to risk, it’s likely that we’d find wide agreement on many aspects of the “ideal state.” In this perfect world, trials would be supported by:
·       A single, cohesive platform (not a collection of loosely-linked point solutions) capable of collecting, reviewing, analyzing, and visualizing disparate – and often unstructured – trial data. 
·       Centralized data collection to help standardize quality across sites and enable full traceability for auditing purposes.
·       A holistic view of trial data, with real-time visibility of trends across patients, sites, and trials, appropriately provisioned by function.
·       The analytical power to track study progress and site performance against key indicators, even for the most complex trials.
 
Getting There from Here
Such an envisioned ideal is not out of our reach, but it will take a well-conceived strategy, the collaboration with multiple partners, the engagement of all stakeholders, a clear roadmap, adherence to the principles of change management, and some institutional fortitude to achieve it. Following are some of the factors necessary to succeed:
·       Senior management must advocate for digital transformation. The directive must come from the top, with a clear statement of the objective and articulation of the benefits.

·       Adopt a strategy of “bringing the technology to the data.” Rather than waiting for data to make its way to the relevant information systems, aim for real-time capture of source data (e-Source) with robust point-of-entry edit checks and logic checks.

·       The choice of technology must be purposeful. The aim should be to remove complexity, streamline processes, use adaptable functionality, and enjoy the benefits of immediate visibility to trial data. This involves reducing the number of systems and replacing legacy systems with a platform that is flexible enough to accommodate data ingested in multiple forms from various sources. Flexibility is key, as no two trials are alike, and it is virtually impossible to plan for every permutation.
 
·       All stakeholders should be engaged. Internally, this includes teams such as clinical operations, data management, biostatistics, and medical monitoring.Sites are currently burdened with having to learn and use multiple systems across studies, so it’s prudent to consider what will work best for them. And, because decentralized trials involve collecting data directly from patients, it’s important to ensure that the technology is easy for patients to use.

·       Re-think what data are collected, how it flows through the process, and how often it is reviewed. It’s time to question the relevancy of data points to avoid collecting data that is never going to be analyzed. The flow of data should be reviewed to remove bottlenecks or choke points, acknowledging acceptable levels of risks. It’s also important to consider how often various data elements need to be reported and reviewed; likely, not all data needs to be available in real time.
 
·       Aim for a user interface that mimics those of consumer systems. It must be intuitive, interactive, and engaging, with clear navigation that is similar for all users to support collaboration.

·       Ensure that visualizations provide direct access to the relevant data. Users should be able to drill into the target data as needed to examine the data point in detail.
 
While adopting a digital approach to trials can be time-consuming and costly, such investment is vital if the industry hopes to modernize trial conduct. With more nimble technology, sponsors can realize efficiencies and access the insights they need to optimized decision-making, compliance, and patient safety.