Tech, talent, and partnerships elevate data management: ICON

By Jenni Spinner

- Last updated on GMT

(Sensvector/iStock via Getty Images Plus)
(Sensvector/iStock via Getty Images Plus)

Related tags Icon Data management Clinical trials Research Contract research organization CRO

In this second of a two-part series, experts from the clinical research organization share thoughts on how data management technology, personnel, and partnerships have evolved.

There are many pieces to the puzzle of successful data management strategy. To hear about some of them, Outsourcing-Pharma checked in with two representatives of ICON, a global contract research organization (CRO):

  • Emily Mitchell, executive director of decentralized trials
  • Kathleen Mandziuk, vice president of project management and real-world solutions

OSP: How has the evolution of data management impacted the kind of talent that sites and CROs have to recruit?

EM: For CROs, we need to move towards analytical thinkers who know how to ask the appropriate questions of the data and want to dig into the why of the data rather than just reporting it. Sites are also looking for innovative thinkers, especially as we move towards the paradigm of hybrid and decentralized trials.

Site staff need to have people focused more on the interaction and outreach to the patient than before when they were doing only clinical assessments because while the patient may not be going into the site, these physicians still want to ensure that they have the connection with the patient and appropriate oversight. It’s kind of a paradox that decentralized trials necessitate more soft skills on the patient management side than hard skills of doing, say a blood draw or taking a temperature.

Kathleen Mandziuk, VP of project management and real-world solutions, ICON

KM: In the early years of data management things were very transactional, but today’s data collection can drive a lot of other things in the study. So for example, using claims data to help identify sites to participate in your clinical trial is a totally different mindset than the way data management or data itself was being used in the past to run the tables, figures, and listings that will go to the FDA.

So, we need creative thinkers, people with the ability to see the big picture by looking at data outside of the primary data we're collecting for the study and figure out what other data can be used to triangulate and leverage in the study itself. We want them to interpret data and see how it helps them identify trends and risks earlier, for example, so we can mitigate it well before what we could in the past.

Another opportunity I see for talent in data management is the portability of the skillset into other areas of the organization. When I started out, it was unheard of for data managers to move into real-world evidence, which is the area I now run for ICON. But because data analysis is truly at the core of so many functions, when you have experience in that mindset, it can be applied in many different areas and departments that rely on data as well.

OSP: What kind of technological tools are available to help meet all of these data management demands related to quality, speed, and meaningfulness?

EM: Tools that help to speed up the data and how it's coming in, like the import methodology, are important. Then, to be able to process that data a more powerful processing engine and a more agile way to move that data through the study life cycle is required.

From the time that you get data to the point where you are checking the quality of it, to transforming it into the standardized format needs to be streamlined so there is a more efficient process from the point of collection to the point of end delivery to the sponsor.

KM: The ability to integrate data from multiple sources such as sponsor data, primary source or patient-collected data, etc. has allowed us to prevent data quality issues which also enhances our speed to insight because we are working from the beginning with clean, high-quality data. In terms of meaningfulness, we touched on our clinical trial tokenization tools, which helps sponsors to keep track of patients or cohorts throughout the study and beyond into the real world. This is an incredible tool for looking at long-term outcomes and real-world solutions for therapies.

OSP: How can ICON help partner with their clients to yield the best results?

Emily Mitchell, executive director of decentralized trials, ICON

EM: From a data collection and management perspective, we like to collaborate with clients to understand specifically from where their endpoints will be collected, e.g. directly from the patient or from a site, or both. Having that knowledge early on will help improve the process and the staff training so that it results in better quality from the very beginning.

We also like to consult with our clients to understand how they plan to use technology in data collection or recommend where we think it might be beneficial. It may seem simple to implement technology like a wearable or sensor or add in a home health nurse after the protocol has been written, but it can become very complicated and less than optimal for data collection purposes. Planning ahead and thinking through endpoint collection saves time and ensures a smooth execution.

KM: One way that we can be a better partner to the industry is to get us involved as early as possible in the study, even in the protocol design phase. There is a lot of value that we can bring due to our experience in running so many diverse trials and having first-hand knowledge of what has worked well from the protocol design all the way to how that protocol impacts the execution of the study downstream. That goes as well for being able to advise on areas you may want to avoid.

Once the protocol is already designed and the study starting, it's much more difficult to make changes due to additional burden on the sites and patients, and other issues that can be costly and time-consuming, such as a possible need to re-consent patients. These are challenges that could be avoided by talking through the data collection and management goals early with your research partner.  

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