Clinical trial management software: use cases, benefits, and future trends

Clinical trials have always been challenging to conduct. They are time- and effort-intensive and fail to produce the desired outcome in 90% of the cases. Researchers have faced similar challenges for years, and these barriers are not going anywhere soon.

The studies vary significantly in terms of the number of participants and duration. However, the costs are always high, from several to hundreds of millions of dollars. According to the JAMA Internal Medicine study, the costs of a trial range from a modest $2.1 million for a trial involving just four patients to test a drug for a rare hereditary disorder to $346.8 million for a new cardiovascular drug trial, with the high number of patients unknown.

Through August 2022, non-COVID studies ran full cycle for about 3.5 years, only slightly more than last year’s average. What’s more disturbing, studies completed in 2022 are on pace for only 75% to 85% of prior-year completions, while compared to the prior decade, clinical trial duration has nearly doubled. Experts are positive that virtualization will help reduce the trials’ duration without compromising accuracy. However, the hard proof is yet to be seen.

In 2019, about 40% of studies failed to onboard the target number of participants, and the trend continues. But recruitment is not the only problem. For a study to pass all the phases successfully, patients should stay in for a relatively long time. Unsurprisingly, at least one-fifth of participants stably drop out along the way. So why do people choose to leave? The reasons vary from the lack of improvements and condition deterioration to inconvenience with accessing the trial site, with the latter also driving costs up.

Clinical trial management software is a complex system that uses robotic process automation in healthcare to speed up or facilitate some processes, provide quality analytics, including predictive analytics, and allow for secure data storage and management.
Below, you will find a summary of tasks and workflows that could be enhanced or fully automated with the help of CTMS solutions.

Surprisingly, patient recruitment for clinical trials is a task that is often conducted manually. Researchers have to explore available electronic health records to detect suitable patients. This mode of recruitment adds to the already-extended trial duration.

Researchers could leverage AI-based clinical trial management software to solve the recruitment challenge. Such systems may select patients, but researchers still need to contact them at some point before the trial. Besides, hospitals can implement a patient engagement platform to inform participants about the course of the trial and their conditions.

For example, the Cincinnati Children’s Hospital Medical Center introduced an AI-driven CTMS at the emergency department. The system screened the EHRs, selected the patients who met the trial requirements, and displayed the results on a clear-cut dashboard, thus leveraging healthcare BI to facilitate reporting and informed decision-making. The screening time dropped by 34% compared to manual recruitment, which helped meet the recruitment timeline and saved time for other activities.

Applying predictive modeling at the onset, researchers can precisely estimate the time needed to enroll participants from certain locations. The novel approach also helps measure the onboarding progress as the trial goes on, which allows for managing the process efficiently and keeping costs stable.

Predictive analytics is another solution that unites ML, statistics, and data mining and helps visualize and mitigate potential risks in ongoing trials and speed up approvals.

For example, the AI and big data researchers from the Massachusetts Institute of Technology (MIT) developed complex algorithms that considered over 140 features of the ongoing drug development trials to predict their outcomes and the likelihood of approval. The tools reached the predictive measure of 0,78 for phase 2 and 0,81 for phase 3 approvals, respectively. This approach helps reduce uncertainty and excessive costs, stimulating well-informed investments.

Recruited participants don’t like to travel to trial sites, and it’s not a whim. About 70% of potential participants live more than 2 hours away from a trial site. This inconvenience causes them to drop off early, as site visits may take time, effort, and money. Luckily, siteless trials became a viable alternative.

Siteless trials need a powerful cloud-based CTMS, which allows each participant, researcher, and clinician to connect, upload, consult, and review the required data. This hub should provide secure data storage and management and drive insights for non-tech specialists. To make it all possible, siteless trials use various technologies, from mobile apps and remote patient monitoring to robust analytical solutions and telemedicine tools. The latter helps ensure seamless communication between researchers and participants.

Clinical research companies and their investors agree that patient recruitment and retention, with diversity and inclusion in mind, is their number one priority for 2023.

This presupposes a new step in the CTMS evolution: directing patients, their families, and clinicians to relevant trials through AI and ML-driven tools like chatbots that make decisions and perform actions based on input data. These solutions will solve the onboarding issue completely and simplify the data management processes. The pace of medical AI development will soon make real-time data capture a reality. At the same time, data itself will be processed by automated analytic tools and IoT healthcare solutions, reducing the human error rate.

The approach to data security and ownership will also change. All actors (participants, clinicians, and even machines and clinical systems) will manage to upload and send data across the network via decentralized applications operated by multiple users in a secure environment built on a healthcare blockchain.