Clinical trials are some of the most complex research endeavors, from a project management standpoint. Much of the complexity involved stems from issues with clinical trial recruitment, and the workflow that is needed to support it: Patient identification and contact, HIPAA authorization, application processing, pre-screening, and group assignment and management.

To say that recruitment for clinical trials is a bottleneck would be an understatement. According to Clinical Trials Arena, nearly 80% of all clinical trials fail to meet enrollment timelines, and 50% of research sites enrol one or no patients. Those delays can translate into as much as $8M in lost revenue per day. When recruitment stages are slowed or stalled, the clinical trial itself stalls. Not only does this increase the cost of the study, but it delays getting products to market and stifles innovation.

So how can some of the complexity around clinical trial recruitment be tamed?

What Creates Complexity in Clinical Trial Recruitment?

There are several sources of complexity in clinical trial recruitment, such as:

Protocol Complexity

While trials are collecting more data, there is a balancing act between collecting better data and constructing a feasible study; according to a 2020 study by Tufts University, clinical trials have been growing in size and scope over the past decade, with Phase III trials showing the most expansion. 

HIPAA Compliance

Research sites are considered “covered entities,” and so must obtain HIPAA research authorization, in writing, to enroll a patient in a clinical trial. While the sponsoring organization may provide a template for this authorization, it still must be administered, collected, and stored  by the covered entity (and done in a way that itself maintains HIPAA compliance). Contact and pre-screening can also be significantly hampered by HIPAA considerations.

High Participant Drop-Out

Patient drop-out rates can be very high, especially in Phase III trials—as high as 30%. While there are several causes of drop-out, it appears that many of them have to do with the logistics of the study: Scheduling, patient communication, and so on.

Rising Costs

The costs required to complete the development process from discovery to bringing a drug to market vary, but some have been reported to be  in excess of $2.5 billion , according to the National Center for Biotechnology Information. Much of this stems from the previously mentioned factors, such as protocol complexity. But rising costs can be an issue by itself, as high-cost studies create the expectation of better data—data that gets harder and harder to obtain as recruitment and retention falter.

Manual Processes

The workflow for a typical clinical trial contains many steps and requires a fair amount of documentation. This documentation is, all too often, collected and entered manually. Not only is manual processing slow and labor intensive, it also greatly increases the chances of losing information or entering it incorrectly. Subsequent workflow paths are needed to catch and rectify these mistakes.

So What Should Researchers Prioritize First? Automation

The key to reducing complexity is to identify the most actionable steps for speeding up processes and reducing errors. Out of the five factors influencing complexity, mentioned above, it’s the last one that can be most straightforwardly addressed: Converting manual recruitment processes and paperwork to a more automated clinical trial workflow.

In looking at the literature on clinical research, and interaction with our own medical records clients, we’ve identified seven areas where manual processes are both common and easily replaced with automation:

 

1) Organizing the overall workflow. Clinical trials have multiple steps, and they must obey a certain order. For example, pre-screening cannot happen until medical records are collected, but medical records cannot be collected until the patient completes and signs a Release of Information (ROI) form. Some steps have multiple requirements, or a contingency (“If X happens, then Y must happen…”) structure. Mapping out this workflow and automating it helps ensure that the right steps are done in the correct order.

 

2) Identifying participants. In one pivotal study, a group of researchers created an automated workflow that used routine data captured during patient care to identify potential trial subjects. These patients could then be automatically sorted and contacted to participate in the study.

 

3) Reviewing medical records and pre-screening. When data can be brought into one central repository and searched quickly, it opens the doors to all sorts of ways to pre-screen potential participants quickly, even if inclusion/exclusion criteria change.

 

4) Collecting authorizations. Again, collecting HIPAA authorizations (and any release of information) is an important part of clinical trials workflow. Authorization forms can be sent digitally, with alerts scheduled to remind participants to complete them. Once the patient completes the form, the data can be safely stored and the next part of the workflow can begin automatically.

 

5) Collecting and syncing patient data. Studies are more efficient when there is a single source of knowledge with all relevant patient data. That data might need to be made available to several different systems and locations, however. Data that is captured digitally can be populated into a single online record, reducing the need for redundant forms and streamlining processes.

 

6) Keeping participants informed. Again, one of the factors contributing to high patient drop-off is the lack of communication and follow-up. Automating communications can help. Messages can be sent according to a set schedule, or set to “trigger” when another item in a workflow is completed (for example, submitting an authorization or completing a check-in).

 

7) Processing adverse event notifications and follow-ups. Adverse event notifications can take a lot of time to process, and their follow-ups sometimes fall through the cracks, especially for larger studies. Again, automated workflow and data capture can help record these events and trigger the appropriate follow-up.

How to Get Started With Clinical Trials Workflow Automation, with Q-Action

Q-Action is our proprietary content services workflow platform, used by enterprise organizations that include the Mayo Clinic and Ascension Health. Q-Action combines tailored workflows and a centralized digital document repository so your locations and investigators can work together better. It allows researchers and teams to manage records, automate workflows, and sync data sources easily, thereby reducing much of the complexity inherent in clinical trials.

Though our solution is “out-of-the-box,” it is still highly customizable. This is important for clinical studies, as each study is unique in terms of the data that need to be collected and the details of the workflow involved.

For this reason, we suggest that you request a proof of concept as a first step to see what Q-Action can do. A QFlow Systems expert will discuss your study needs with you and your team and then use that information to build a working prototype of a system—usually in as little as 10 business days. This will allow you to see what is possible (and what time can be saved) before making a substantial investment.

And of course, this is a great time to ask questions as well! Our representatives love problem-solving and are ready to help.