This presentation was originally introduced at Arena’s Outsourcing in Clinical Trials and eClinical & Clinical Data Management Innovation Virtual Conferences.
Over the past year, the clinical trial industry has been rapidly transformed. There’s been many discussions about how we can apply the lessons learned from the pandemic — collaboration, speed-to-market, and digitization — to improve the field going forward. The #NoGoingBack initiative emerged from this idea, focusing on sharing knowledge to transform the clinical trials industry. Unfortunately, many of the trends that emerged from this movement — Artificial Intelligence, Machine Learning, and digital transformation — are impossible to leverage without actual strategies. And without reliable, accurate data, there cannot be the necessary digital transformation needed to implement new solutions that would benefit trial timelines. We may not have all the answers for #NoGoingBack, but we do have years of clinical data expertise to illuminate digital strategies that can power the future of clinical research.
Catalyzing Digital Transformation
Three major trends emerged from the pandemic: digitization, decentralized trials, and faster times to market. In terms of digitization, the volume, variety and velocity of data is increasing while technology is creating more data than ever before. Data can be provided and accessed more easily, which allows for richer insights about patient experiences. It also creates challenges for acquiring, standardizing, and consuming data. Decentralized trials have been around for years, but the pandemic provided a much-needed catalyst to pivot to these models permanently. The result of these trends, as well as the increase in overall public awareness, was the accelerated time to market for new medicines, which has become the new standard for clinical trial development.
At the same time, the clinical development industry was already seeing huge increases in volume and diversity of data pre-pandemic. Our 2019 Tufts–eClinical Solutions Data Strategies and Transformation Study looked at how organizations were handling clinical trial data. For example, one top 10 pharmaceutical client now has 70 percent of its data coming from external, non-EDC systems. Although modern technologies are available, 75 percent of companies were still using SAS and Excel for data collection and data entry. Probably the most concerning result was that increased data sources were contributing to longer timelines. LPLV to database lock cycle times have increased by 40 percent in trial that use four or more data sources. Since we’re seeing an average of eight data sources per trial, that increase is very significant.
Maximizing the Value of Data
Data is the currency of the life sciences industry. Without reliable, accurate data, we only have conjecture, which isn’t sufficient in an industry that is literally life or death. With properly obtained, vetted, and controlled data, we get results that we can trust and analyze for data-driven decisions. Unfortunately, the process of consuming, standardizing, and reviewing data has remained largely unchanged over the past 25 years. This has resulted in slower trial timelines and decreased productivity, making the new sources and types of data relatively useless. Will the pandemic be the catalyst that leads to a digital data transformation?
Modern clinical trials need a modern data infrastructure. The traditional data lifecycle involves collecting, organizing, and reviewing the data. After that, there’s the analysis then eventual submission. With many different data sources and formats, collecting can be complicated.
Typically, data is collected while integration remains an afterthought. Although the traditional model has improved and become more fluid with electronic data from the start, there’s still value and time lost with the siloed approach to organization and analysis.
In a modern approach to clinical data management, the key to an effective infrastructure is interoperability. With many different data sources and systems, the goal is to find a data collection system that is flexible. This allows clinical trials to meet the needs of diverse patient populations by integrating decentralised trial approaches that use wearables, biometrics, and real world data. Another core technology of a modern data management strategy is an automated data pipeline, allowing for the continual ingestion of data without manual intervention, while continuously mapping to industry and company standards for cross trial analysis and insights. Leveraging one source of truth that has self-service access to both raw and standardized data, data managers and medical monitors can work more effectively and collaboratively. Instead of spending 30 percent of their time on data assembly tasks, these functions are able to focus on preparing high quality, analysis ready data sets faster.
Managing Data for Digital Transformation
Digital transformation of the life sciences industry is a strategic imperative and all life sciences organizations need to be thinking about their clinical data ecosystem. As clinical trials are building out their data architecture, they need to plan for interoperability, automation, and new opportunities to upgrade and automate their clinical data pipeline. Platforms like elluminate® are foundational for digital transformation, delivering tangible value to companies of all sizes to maximize the value of data assets, optimize outsourced operating models and build new experiences for end users across functional areas including clinical operations, medical monitors, data management, statistics and programming and data sciences.