This is the first of two blogs on this critical topic. In this post, we’ll cover the importance of process in ensuring a robust strategy is in place for the acquisition and analysis of trial data in the face of a changing clinical research environment.
The current state of clinical research
Businesses around the world are being challenged to evolve due to recent events, and the clinical trial industry is no exception. However, many of these changes were already underway prior to the pandemic; COVID-19 has accelerated these innovations rather than initiated them.
The clinical research environment has been impacted in multiple ways, ranging from new regulatory requirements to supply chain interruptions and shipping delays. In addition, many of the standard clinical trial logistics are being reassessed. For example, patients are becoming much more comfortable with mobile health (mHealth) devices, accelerating plans for remote data capture, analysis, and sharing.
A new clinical data environment
These changes have ushered in a new data environment driven by shifts in three main areas:
- Patient recruitment and engagement. There is considerably more data available to find the right patients at the right time, and at times this data is so rich in insights, it can substitute for patient enrollment. In addition, the ways in which clinical trial patients are meeting with study personnel is rapidly changing as well
- Data collection. Now more than ever, trial teams must understand exactly what “quality at the source” means as well as the impact that data latency can have on decision-making
- Data monitoring. As monitoring becomes more digital, upscaling or skill set changes may be necessary for staff
Underlying it all are the process changes necessary to support the structure of a clinical trial and help us better understand what is possible for speed, cost, and the quality of data.
Why talent and advanced processes are more essential than ever
Successful organizational change depends on three key factors: people, process, and technology. It’s tempting to focus on the novelty of the latest technology, but technology is only as effective as its ability to support and enable processes and guide people. That’s why it’s important to start with process to build a solid foundation.
During the clinical trial, processes are what allow us to capture or collect the data needed for specific information. When brought together, this data provides the insights necessary for decision-making. In a technology ecosystem, data can be divided into three categories: administrative and financial data (project, program, and sponsor), study management data (workflow and reporting), and patient data (safety, risk, and quality).
Each of these categories has specific processes that, at best, work together to capture the right data at the right time. But at worst, they can become disjointed and disparate such that connections can’t be made, even when using the most attractive and exciting technology out there.
The complexity of running a clinical trial is evident when you consider that each of these data sources employs a different technology or application that is capturing the inputs necessary for decision-making. It all needs to come together in an integration hub to be processed and turned into information to help make decisions.
Elements of a robust data strategy
The importance of a robust data strategy for the acquisition and analysis of data cannot be overstated. There are five critical dimensions of such a strategy:
- Process optimization. Processes that drive data capture must be enabled for maximum speed, access, and quality
- Data sources. New data sources with different structures, forms, and functions are rapidly being added, requiring the processes and systems to have the capability to adapt
- Complexity. Analytic capabilities and data architecture must support increasing complexity, pushing organizations towards a higher analytic maturity
- Data literacy. A struggle for many organizations, data literacy is defined as an organization’s ability to read, work with, analyze, and make decisions with data, regardless of members’ roles, skill level, or the visualization tool used
- Data timeliness. When data latency decreases, there is a reduction in decision cycles and time to improvement
When employed together, these five aspects help to set clinical trials up for success.
Our data managers are seasoned in information technology and pharmaceutical research data analysis. Many hold advanced degrees in math, science, or computer science, and together provide a full complement of services including database design, development, and validation; development of case report forms; data cleaning; medical coding; and handling of serious adverse events. For a personalized consultation with one of our experts on how to optimize your clinical data strategy, contact us.