Simplify Your Clinical Data Flow to Streamline & Automate Modern Data Review
Life Science companies seeking to simplify their clinical data processes & workflows can often feel overwhelmed by the massive amounts of data and the various technologies used for aggregation. The pressure on data management teams to clean data and provide key insights to operational leaders as quickly as possible is only increasing with the influx of hybrid and decentralized clinical trial models today.
It’s not uncommon for organizations to maintain the status quo with existing processes as they try to simply keep up with the evolving state of clinical trial management. However, taking the time to re-evaluate and optimize existing infrastructures can ultimately provide greater benefits across the data lifecycle.
Let’s examine the three reasons why simplifying your clinical data flow can increase operational oversight, shorten data management cycle times, and reduce the manual burdens on data management teams.
Three Reasons Why Simplifying Your Clinical Data Flow Can Increase Operational Oversight
1. Accelerate Cycle Times Without Sacrificing Quality
In today’s clinical research landscape, there is an abundance of opportunities to advance processes and achieve insights faster from trial data. More life science companies are leveraging new innovations to streamline and automate data coming in from various sources in order to evolve traditional processes.
In a recent webinar with Robert Musterer, Vice President of Product Management at eClinical Solutions, Berber Snoeijer, Business Consultant at ClinLine and Thierry Escudier, Principal at DCRIPT Consulting, the panelists shared their unique perspectives on the present and future state of the industry and how organizations can adapt and embrace technological advancements.
Robert Musterer described the current climate in the life sciences industry as a “perfect storm of data innovation”. As a result of this perfect storm, more people are taking interest in ways to simplify and scale processes for decentralized and hybrid models. He sees a notable increase in companies asking how they can deploy AI and ML capabilities to really accelerate their process.
He explained, “Using an AI approach where the machine is looking for data anomalies, data inconsistencies, and things that you may not even have thought to check for can reduce lag time and pinpoint where data management teams need to focus their energies.”
Berber Snoeijer, who brings more than 20 years of experience in clinical programming and statistics working with real-world data, believes that reducing repetitive work by automating the linkage of data with modern clinical data platforms like the elluminate® Clinical Data Cloud and standardizing code that has already been validated will reduce risk of quality inconsistency and greatly improve efficiencies.
The risk-to-reward ratio is a key factor when teams are looking to incorporate new methods to automate their clinical data flow, says Thierry Escudier, a clinical development operations leader with 30+ years of experience in the pharma industry. He’s seen first-hand how companies can be cautious of change due to presumed risk, and as a result may lose out on the long-term benefits of adopting new processes.
Though there has been a dramatic increase of technology adoption in daily life, there is still a struggle among clinical operations teams to consolidate processes because we have more outsourced vendors working in silos than ever before. For a clinical operations team managing a global trial, an automated system will allow for greater oversight and management over the trial rather than spending time on manual data review. Teams that are slow to streamline their data flow process will lose out on the added value a centralized clinical platform can provide – including access to faster insights from critical data points.
2. Invest in Technologies to Reduce Manual Labor
The pendulum of whether to build software in-house or purchase a ready-built technology can often swing back and forth while key stakeholders appraise their current processes. While the initial investment and adoption of a clinical data platform can sometimes deter decision makers, purchasing a pre-built data repository can help data management teams streamline their data review processes for faster submissions by removing manual elements.
As someone who spent years as a clinical programmer developing software, Rob Musterer understands the appeal of building a custom platform that can be tailored specifically to your needs. However, he acknowledges that a majority of life science organizations are not prepared for the resource drain of building in-house.
Most companies choosing to build are working with a small development team, as software development is not their primary focus. These teams are often hard-pressed to keep up with the changes in the operating systems and underlying technologies of software development. Moreover, if a key member of your development team leaves, the project could be set back years due to lack of resources.
“The industry was 100% correct when it evolved from the build mentality to the buy mentality.”
From Thierry Escudier’s perspective, he’s witnessed organizations exhaust time and money trying to produce an internal system with no manpower or capacity to ensure its quality. Since all pharma companies are held to the same validation standards, he doesn’t see the purpose of trying to create your own unique system for data collection and monitoring. He advises teams looking to simplify their clinical data management to “choose the best vendor who can stay on track with quality and validation,” rather than expend manual labor on building a proprietary software.
For companies that are leaning towards buying a pre-built platform like elluminate, Berber Snoeijer recommends evaluating each step of your existing process and then researching the tools that are robust enough to fit those needs. For example, if your data management team is struggling to review data from disparate sources from decentralized systems, EDC, IVR, eCOA and more, then a centralized platform like the elluminate Clinical Data Cloud can help identify misnomers in the data faster with comprehensive visualizations and advanced analytics capabilities. Berber recommends that “if there is a software available that can do all of that for you, that’s much better than building it yourself.”
3. Adapting for the Future of Clinical Trials
As the industry continues to grow and embrace alternative trial models, technologies, and tools like AI and ML, finding ways to simplify your clinical data flow is more critical than ever before to keep up with the pace of data. Looking ahead, the panelists shared their key advice for moving your data management processes forward.
Berber Snoeijer: The industry recognizes that data integration, governance and achieving one source of truth are important but is still unsure of how to do this best. I expect many companies to take steps in this direction in the coming years. This might be in setting up governance structures, testing solutions and/or adopting solutions like elluminate which can easily be extended to a complete end to end process in the near future.
Thierry Escudier: With the clinical trial advancements from the past two years, companies should keep the positives from decentralized trial models but re-evaluate for a more hybrid approach for patients. A hybrid model that also makes use of software will not only put less burden on the patient, but allows clinical development teams faster access to insights from their data.
Rob Musterer: The industry is undergoing an architectural shift and the democratization and liberation of clinical data will be essential as we move forward. A clinical data automation and analytics platform like elluminate can enable organizations to take full ownership and greater oversight over data that has been siloed.
If you have been evaluating your existing data management processes and are ready to learn more about how the elluminate Clinical Data Cloud can help streamline and automate your data flow, we invite you to contact us today.
Thierry has 30+ years of experience in clinical development, digital innovation implementation/transformation and patient centricity for life sciences organizations, CROs and vendors. He works toward building success in clinical trials through digital innovation, decentralized trial adoption, and increasing the engagement of all the stakeholders in the clinical trial lifecycle.
Berber Snoeijer started in clinical research in 1997 as a biometrician and has since then worked with clinical data in different functions. In 2001 she started a CRO, Biometrics Support, aiming at the data management, data analysis and reporting of clinical trials. She switched in 2011 to work as a R&D manager dedicated to investigate and utilize the potential of real world data from electronic health records. This resulted in many different solutions including a full reporting system to give feedback information to clinical research professionals. She is experienced with software and database engineering, process engineering, and improving efficient utilization and interactions of people based on management drivers. Nowadays, she uses these skills and knowledge to help life science companies assess, design, and improve business solutions and processes at smaller and larger scales.
A life sciences industry veteran and clinical systems expert, Rob Musterer has over 25 years of experience in clinical programming, biostatistics and data management in large pharmaceutical companies including Bayer Healthcare and Schering-Plough Pharmaceuticals. Rob most recently served as President of ER Squared, Inc., a life sciences consulting firm with an e-clinical focus. Rob began in pharma as a data manager. Seeing possibilities to improve the model, he engaged in early deployments of electronic data capture (EDC) systems and championed the change management required to adopt EDC. Rob has dedicated his career to helping the life sciences industry obtain greater time-to-value from their solutions and break from historical clinical research processes through innovation. His expertise extends far beyond EDC and includes data management services, clinical trial programming, data repositories, data visualization, clinical trial management systems (CTMS), standardization, validation and more.
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