Empowering the Digital Health Revolution
Author: eClinical Solutions
Published: January 11, 2021 9 min readHighlights from elluminate Engage 2020
Enabling digital transformation for life sciences companies to fully leverage and access ALL their data to create valuable new treatments, experiences, insights and opportunities that improve human health is what we do at eClinical. This mission is what inspires us and is the “why” behind the elluminate® platform which is designed for cross functional use by clinical development teams to improve the entire experience of working with clinical data and the efficiency of clinical trials.
While we discussed the challenges of the digital health revolution at last year’s inaugural elluminate Engage event, no one would have been able to predict the pace at which that revolution has accelerated with the cataclysmic force of the pandemic. Digital transformation has been brought to the forefront of conversations beyond clinical research, in all areas of healthcare and this is why we made it a central theme at our second annual elluminate Engage conference this year.
Engage in a virtual format did not change attendees’ desire to connect with industry leaders and one another or the energy of the discussions about the importance of embracing a platform approach to provide faster, self-service access to data across clinical teams. Now more than ever, we need to understand how we can move clinical development forward with new strategies and technologies. Some of the key highlights from the conference which further elaborate on these concepts are described below.
Innovation Requires A New Way of Thinking
How can we hijack our brains to get into this high energy brain state, where we can bring in new ideas that can lead us in new directions?
Dr. Jeff Karp opened Engage with his keynote, “Towards Accelerated Medical Innovation,” where he outlined his model of creative problem-solving at his bioengineering lab, The Karp Lab, in the Harvard-MIT Health Sciences and Technology (HST) division. In doing so, Dr. Karp illustrated how this approach could be applied more broadly to solving challenges in the life sciences industry.
Hiring and empowering teams is important to meeting research objectives successfully. There may not be a way to guarantee that a new hire is the exact fit for a company, but looking for people who are actually passionate about changing the world is a great place to start. Also, when a team is diverse, the collaborative effort is enhanced because there are new perspectives coming in that could spark innovation. And when team members are supported with the right resources and given the opportunity to thrive, they’re empowered to try different experiments and make new discoveries.
Another important component to how Dr. Karp’s lab functions is through a translation strategy, which can be implemented to maximize the potential to bring new therapies forward. This involves the following:
Gaining critical insights
As Dr. Karp noted, “Often we spend far too much time, especially early on, thinking about the technology and not the problem. [If we make] the assumption that the problem definition is incorrect, that gives us this angle that, as we advance early on in the project, we’re trying to gain critical insights into the problem. We’re conducting experiments to learn something new that others may have overlooked or missed. It actually provides insight that can direct you towards a potentially viable solution.”
Defining the bar to succeed
By setting the standard that must be achieved, clinical researchers can understand what it will take to exceed the best results established so far in a particular model. Not only will this dictate what responses must be demonstrated in experiments, but it will also focus efforts on specific targets that will advance drug development.
Getting feedback along the way
Dr. Karp attributed success at the lab to his commitment to building his entrepreneurial ecosystem. By forming relationships with those who could help strengthen areas where you don’t have expertise, you can form an informal advisory board that can provide constant guidance. Reaching out to people in the community and your network increases the potential to bring new innovations forward to help patients.
Dr. Karp’s presentation, “Towards Accelerated Medical Innovation” is available to watch on demand.
Enabling Technology for the Digital Future of Life Sciences
Life sciences companies have yet to realize the full potential of digital and analytics. To achieve success, companies will have to scale and supercharge the power of data analytics through unified platforms focusing on entire business systems rather than use case projects.
Raj Indupuri also touched on the importance of collaboration in his presentation, “Enabling Technology for the Digital Future of Life Sciences.” Despite the challenges of Covid-19, building strategic partnerships with customers has been critical for eClinical’s growth this year, and those relationships have cleared the way for even more innovations to new products and services in 2021.
As eClinical continues its mission to support clinical researchers in making data acquisition and analysis easy and intelligent so new treatments can be delivered quickly to patients, the company is looking ahead to the digital future. The cornerstones of eClinical’s product strategy and technology foundation are grounded in customer-centricity, data-centricity and a platform approach, which allow us to help simplify complexities in achieving digital transformation goals.
Part of eClinical’s vision also includes exciting new applications for AI and machine learning. The application of these capabilities will play a significant role in next generation data review and data analytics. In fact, Raj anticipates AI-enabled data review to enhance the roles of data managers and reviewers so that they can work 40-50 percent faster.
That’s why eClinical’s AI Roadmap is essential in understanding how clinical data management and operations can be transformed with three key innovations:
- AI-enabled data review
An AI-enabled data review system can identify erroneous data and prioritize it for human review with automated data recommendations.
- Patient clustering
AI-based tools provide the ability to drill down to clustered cohorts to test hypotheses and discover critical data correlations.
- Leverage AI for cross-platform analytics
This improves compliance, flags potential risks and enables data review in real time.
Lessons Learned from Implementing elluminate
It is critical for us to be able to house all of this data in one place to be able to increase data efficiency and quality.
In his presentation, “Jazz Clinical Data Factory and Clinical Insights,” Sokol Petushi demonstrated the lessons learned from implementing elluminate. Among the key factors in the successful rollout was the development of the business vision and goal of the implementation, which served as a guiding force in how Jazz decided to introduce the system. Starting small and bringing the team along to prepare them for the larger implementation of the platform was another critical part of the strategy. Finally, the importance of branding the system, in this case, the Clinical Data Factory (JazzCDF), made the solution memorable and facilitated the adoption of elluminate.
The key business benefits and goals of the elluminate platform for Jazz include:
Cycle-time reduction
This was measured by comparing the time spent on targeted activities like data quality monitoring prior to and after the introduction of elluminate.
Optimized internal resource utilization
New processes and procedures reduced the time spent by data managers on data cleaning and the time spent by BS programmers in implementing standardizations. There was also faster identification of potential study enrollment issues.
Harmonized data insights
Establishing one source of truth across all of Jazz’s studies enabled accurate and harmonized data insights while also setting up the foundation for more complex and advanced data analytics.
Improved data governance, quality and compliance
Implementing a formal data management structure helped create robust data governance, compliance and quality assessment capabilities.
Evolving the Clinical Trial
So in this interesting time of the computing model shifting, should we fear it... or should we embrace it and innovate with it to the next generation?
In his day two keynote, “Digital Transformation and the Evolving Clinical Trial,” Doug Barta presented a new approach to how we can operate more efficiently in the current clinical development landscape. Because digital transformation is impacting clinical trials across the board, enacting significant improvements in trials will require innovations in multiple areas. These include:
- Social and mobile technology to connect with patients, capture data and disseminate information;
- Data and analytics to derive understanding in real time, gain insights and increase data control;
- Cloud technology for efficiency and scalability to process and store information; and
- Machine learning and AI to augment thought processes
The areas where innovations can be made to improve clinical research underscore two key considerations for the biopharmaceutical industry: how efficient are trials and are we delivering the best experience we can as an organization? As Doug noted, “to get a drug through registration (inclusive of failures), it costs 2.6 billion dollars. That tells you that this is a high-risk business that takes a lot of money, a lot of energy and a lot of risk.”
In knowing all of these areas where improvements can be made, what if we could establish a clinical development company and start with a blank slate? How can we do things differently today? There are guiding principles you can build around, such as being as nimble and lean as possible by leveraging cloud vendors and managed service providers, working with new technology and differentiating on analytics. Keeping these principles in mind and embracing digital transformation not only helps to optimize trials, but it also builds a foundation to work with AI, machine learning and predictive analytics, which leads to better, faster decision-making.
Respecting that the computing model and clinical trials are shifting is vital to taking the next step in the digital transformation journey. If we innovate with these changes, we can advance clinical research and meet the needs of our patient populations.
Missed this year’s elluminate Engage virtual conference?
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