Why Clinical Data Strategy Matters
by Raj Indupuri – CEO, eClinical Solutions
For the last 20 years, I’ve had the opportunity to see the evolution of clinical data management from both the sponsor and provider perspectives. In the early 2000s, I was part of the team that helped EMD Serono transition from paper to electronic processes. Since 2012 after co-founding eClinical Solutions LLC, I have worked with numerous clients looking to find better ways to take advantage of the increasing number of electronic data sources that are part of every clinical trial. One of the big themes that has emerged over the past few years is how life sciences organizations acquire and manage their different and disparate data sources, and, combine them together for operational and clinical insights. The end goal of this work is to optimize ongoing trials and future research programs.
This is not an easy problem to solve and when discussing both technology infrastructure and specific end user requirements with our clients, the discussion eventually comes around to the overall data strategy of the organization. At eCS, we work with small, mid-size and large biopharmaceutical companies but the core data objectives and challenges they face are the same although the scale differs. The key questions that a comprehensive data strategy should answer for a clinical development organization include:
- What information is needed to support making well-informed decisions that align with business strategy and what data is needed to provide that information?
- What type of technology infrastructure is in place currently and how does it support and deliver value to end users?
- What data is widely available and what data must be under stricter access control
- What data governance framework is used to ensure data can easily be managed, accessed and protected.
- What standards does the organization plan to follow and what infrastructure exists to communicate, share and reuse those standards for consistency and interoperability both today and in the future.
Data Strategy – Not just for “Large” Biopharma
A comprehensive data strategy is not a luxury that only large organizations can afford as it is critical for developing a governed data flow and technology infrastructure roadmap and prioritization strategy. An overall data strategy guides a clinical data strategy and helps to focus the organization on the most immediate needs that will deliver value quickly to end users. As both a software and technology enabled data services provider, eCS clients have shared the challenges and data chaos that comes with having numerous CROs and data source providers. A formalized data strategy can help with these challenges ensuring organizations of all sizes benefit from and gain control of the data they are investing tremendous resources in.
In addition to laying out a data strategy, identifying processes that can be automated is another area that we are increasingly working on with clients especially in recent years. A successful automation strategy can streamline all aspects of clinical development and in this context, automation includes but is not limited to the following capabilities:
- Integrated data feeds from data source systems that automatically bring in new/updated data per a defined time schedule and/or event trigger.
- Tools to compare incoming data metadata to defined standards and perform data quality checks.
- Tools to automate transformation to the desired analysis ready datasets.
- Automatic incorporation of new/updated data into summarizations and visualizations.
According to the Gartner 2019 CIO Survey, Analytics and AI are top areas for increased investment for Life Sciences organizations in 2019, and are also considered top game-changing technologies. * The maturity of analytics across life science is still evolving and considering the analytics use cases of both today and the future is another key element of developing a clinical data strategy. Use cases to consider for delivering analytics to stakeholders include:
- What action should be driven by the outcome of the use case?
- What decision is needed to drive that action?
- What data needs to be summarized to provide that information?
- How should the data be best summarized for users to interpret it and to convey it to others?
- Preferred data presentation approach (listings, tables, dashboards, graphical visualizations)
- Interactivity needs such as drill down, tagging, setting flags, etc.
- Sharing and collaboration needs such as commenting, raising issues, etc.
Learn More about The Case for a Clinical Data Strategy
I recently had the chance to collaborate with Rob Musterer, President of ER Squared, Inc. and a clinical systems industry leader for more than 25 years on a white paper called “The Case for a Clinical Data Strategy” which is now available. In the paper, Rob and I discuss why a data strategy is so important, the key elements of a clinical data strategy and the top five considerations for implementing a successful data strategy. At eClinical Solutions, we are very excited about the digital transformation strategies that life sciences organizations are implementing and the way technology can help accelerate and enable this transition. This is the first topic in a series, the next white paper covers the key technology considerations for implementing an effective clinical data strategy.
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