Thursday, Feb 9, 2023
4 Tips When Using Tech to Manage Clinical Trial Complexity
Jennifer DuffGeneral Manager, Zelta (by Merative)
Clinical trials are more complex than ever. Pharmaceutical companies, medical device companies, and clinical research organizations (CROs) are collecting more research data than ever. The amount of data from novel sources that support decentralized trials has also increased over the last several years. As a result, research sites and clinicians are struggling to meet sponsor deadlines for increasingly complex trials.
Technology can help solve this challenge, when it’s deployed appropriately. Here are four tips for managing clinical trial complexity:
1) Stay focused on the experience
When I say experience, I mean that in a couple of different ways. First, we should focus on the experience of the user – the sponsor team designing and conducting the trial. Better workflows can make the difference between meeting and missing a critical research decision point or deadline. Second, and just as important if not more so, we should focus on patient and clinician experience. The future of trial recruitment will be heavily influenced by the patient’s expectations for a smooth digital experience and the clinician’s ability to deliver to sponsor expectations.
2) Enable direct data flow
Direct data flow, meaning the ability to collect data directly at the source (i.e., the patient, clinical setting, directly from equipment or testing systems) and flow that data to the sponsor systems, has gained momentum with the COVID pandemic and the associated healthcare disruption. Direct data flow is complex, and some technologies contribute to complexity, especially if they are not connected to each other or otherwise inhibit data flow by requiring data transformations.
As an example, clients are more frequently asking how electronic health records (EHRs) can be directly integrated with electronic data capture (EDC) systems to simplify data collection for clinical trials and better integrate that into clinician workflows. There are significant differences between how healthcare data is collected in a clinician setting versus how it is collected for clinical research. As the industry identifies ways to smartly apply technology solutions and data transformation that reduce the data disparities between the use cases, the ability to connect clinical data with clinical trials data becomes a viable path forward.
3) Apply artificial intelligence (AI) and machine learning (ML) strategically
When working with AI and ML technologies, it’s important to have a specific purpose and a clearly defined use case/problem statement to be solved. We were among the first to apply AI to medical coding and have had success in that area for a long time, showing that machine learning solutions embedded within workflow can significantly reduce overall effort. Because of this experience, we are working on other targeted applications of machine learning, specifically for streamlining operational workflows and improving overall quality.
4) Lean into the expertise of your technology vendor
Research requires completing a host of difficult tasks in support of complex trial designs. Technology vendors should be able to help solve those challenges. They should be able to proactively advise in these cases and not just be reactive recipients of information.
For example, adaptive clinical trial design enables planned modifications to one or more aspects, which offers distinct advantages. We helped a client with adaptive trial design, so that researchers can add multiple treatment arms throughout the study lifecycle. Another client used adaptive design to enable adjustments to the randomization schedule, based on the variable or unequal probability of treatment assignment.
Complexity will continue to increase in the clinical trial space, but innovative technologies continue to help teams rise to the challenge. Applying these principles can also help future-proof clinical trial design in the years to come.
Jennifer Duff is Merative’s General Manager for Zelta. She has more than 24 years of experience in the Life Sciences industry and specializes in enabling and scaling industry-leading services and technology solutions for biopharma clients.
You May Also Enjoy