Developing a Data-driven Feasibility Process

Clinical research is complex and resource-intensive, and to understand just how far an organization’s resources can go, assessing a study’s feasibility is critical for success. Evaluating feasibility elements enable organizations to make informed decisions and allows for seamless execution and reporting of clinical studies. How can your organization utilize feasibility to its fullest extent?

Defining Feasibility

Feasibility is “a process of evaluating the possibility of conducting a particular clinical program/trial in a particular geographical region with the overall objective of optimum project completion in terms of timelines, targets, and cost.”

As you’re assessing your own organization, there are three ways in which you can test for feasibility:

• Recruitment viability: making sure a cohort exists so there are patients available
• Financial viability: the study will, at a minimum, pay for itself
• Resources: the people and facilities involved in the study

However, there are other things you should review, especially to make sure taking on the study is worth your staff’s time. Those include:

• Site initiation visit (SIV): Making sure site staff will be ready to be trained on the protocol, procedures, and monitoring plan
• Processes: Including taking the protocol back to the sponsor, internal processes to execute the protocol, and processes to recruit and retain participants

Why Data-driven Feasibility?

In a lot of cases, feasibility questions tend to be subjective; meaning, questions asked are more likely to elicit non-data-driven answers. Crafting objective questions can lead to a better platform on which research teams are judging all studies to make decisions.

This is where data-driven feasibility comes in. It helps you understand what is defined as successful feasibility – for example, is it the number of people you are accruing? Is it a timeline? Something else?

When metrics for success are defined and you know what you need to see to be successful, it’s time to figure out what data feeds into the metrics. In order to have the data, create a process designed to ensure data is clean, proper, and correctly collected so it can be analyzed.

At this step, establishing minimum footprint is helpful. If you have a standard operating procedure (SOP) detailing you and your team will complete every single field as part of data entry, and feasibility fields aren’t a part of them, this can lead to missed opportunities of consolidating practices.

Creating objective, data-driven feasibility questions also can help your team understand potential gaps early on in the study activation process and help determine if you will do the study or not. For example, if your data looks good, but your budget is too low, that’s a focal point for your team. Finding potential gaps in the study activation process early on will help streamline your overall activation process.

What Kind of Data Do You Establish as Minimum Data Standards?

Feasibility isn’t just centered on the activation of a study – it’s also understanding if your organization can manage the study across the lifecycle. Take a clinical trial management system (CTMS) example. You may know what data you need to put into your CTMS, but there are several other factors to consider as well:

• Are you quality checking the data?
• Is there a quality control process to make sure fields are completed?
• Are you running reports to find missing data?

Questions such as these can also drive process improvement. Knowing something needs to be done and acting on it are two different things – and these questions can help you understand where to begin when it comes to holistically collecting data.

Blank Fields

Inevitably, some questions on a feasibility review may garner a blank field. How do these fit in with your assessments?

Sometimes, a blank field is actually the right answer, or at least an acceptable one as an organization is testing for feasibility. However, in order to know that, it all depends on what’s in your data dictionary. Knowing what you are expecting in each field and the values associated with them will enable your team to understand if a blank field is acceptable or not.

Does “Blank” Mean “Nothing,” or Is It “Missing Data”?

Put simply: if the data isn’t there, the data isn’t there. As the “ultimate currency” when testing for feasibility, you must track data in order to learn from it. Much like the Food and Drug Administration (FDA) principle, “if you didn’t write it down, it didn’t happen,” if the data doesn’t exist, you can’t use it to make decision. Feasibility is built on evidence-based decision making, which comes directly from data.

Understanding What Your Institution Requires

In order to know what kind of data you need, knowing what your organization requires is a good place to start. These can apply to therapeutic or interventional studies, regardless of sponsor.

Enrollment data goes hand in hand with feasibility. When collecting this data, knowing if you have a strict definition of target versus actual and how system-wide your definitions are will greatly help. This is important because if you’re trying to do an analysis across your entire organization, and the definitions don’t line up, the data won’t make sense. Making consistent definitions can help greatly, since your team can then data mine based on the assumption of your definition.

Other examples of what your organization may require includes (but is not limited to):

• CTMS build for every interventional study
• Calendar build for schedule of events
• Budget build
• Invoices and payments
• Financials

While this is a lot of work upfront, knowing what your institution requires will only help you in the long run. It’ll become easier to determine if a study is financially feasible for your organization, which in turn enables your team to get a study to activation quicker – and, ultimately, a drug to market quicker.

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