How we can improve clinical trials with digital technologies

Let’s start with the good news. Over the past decade, clinical trials have undergone significant improvements, driven by advances in technology, changes in regulatory guidelines, and increased recognition of the benefits of a patient-centered approach. Here are five key examples of advances in clinical trials:

1. Digitalization of clinical data management. The global adoption of electronic data capture (EDC) systems and electronic health records (EHRs) is streamlining data collection and management, making trials more efficient and reducing errors associated with paper-based processes. This digitization enables real-time data analysis and facilitates remote monitoring, improving trial control and patient safety.
2. Adaptive trial designs. Adaptive trial designs allow modifications to the trial protocol based on accumulating data, which improves efficiency by allowing adjustments to sample sizes, treatment arms, or endpoints during the trial, increasing the likelihood of successful outcomes. Adaptive designs also help speed the identification of promising therapies or the discontinuation of ineffective ones.
3. Patient engagement and involvement. Patient engagement strategies aim to incorporate patient perspectives and preferences, ensuring that trials are more relevant, accessible, acceptable to participants and improve patient care. This involvement leads to higher retention rates, improved patient compliance, and more meaningful outcomes.
4. Decentralized and remote trials. The COVID-19 pandemic has accelerated the adoption of decentralized and remote trial models. These approaches use telemedicine, remote monitoring devices, and mobile health apps to reduce the need for patients to visit physical trial sites, making participation more convenient and inclusive. Remote trials have the potential to increase patient enrollment, diversify participant demographics, and improve data collection.
5. Real World Evidence (RWE). Real-world data (RWD) and RWE are increasingly being used to supplement traditional clinical trial data. RWD is collected from a variety of sources, such as electronic health records, wearable devices, and patient registries. By integrating RWD into clinical trials, researchers can gather additional insight into treatment effectiveness, safety, and long-term outcomes, improving understanding of the therapy’s real-world impact.

The bad news? Conducting efficient and cost-effective trials is still a challenge. Clinical trials are complex – and the more complex the trial design, the more difficulty a company can face in designing and selecting appropriate endpoints. Defining clinically relevant and statistically robust endpoints that are consistent with regulatory requirements and the patient’s perspective will always be a challenge. Furthermore, adaptive trial designs, while advantageous, can introduce additional complexities into trial planning and execution—and these types of trials are increasing in the industry. However, this latter challenge can be mitigated by using skilled biostatisticians with experience in complex types of trials.

One challenge that is a hallmark of clinical trials is enrolling enough participants in the required time frame. Recruitment difficulties can lead to delays, increased costs and a lack of diversity in the study population. This is particularly true in trials for rare diseases, where patients who meet the study’s inclusion criteria are few and infrequent. In a traditional clinical trial, you may need to set up multiple trial sites for very few patients, which increases the cost of the trial. This is where decentralized trial approaches can help by allowing patients to enroll from around the world, with less reliance on physical trial sites.

Although decentralized trials (also known as virtual trials or remote trials) offer advantages in terms of convenience, patient participation and data collection, as noted above, not all trials are suitable for this approach. Various factors determine the potential for virtual visits, including the nature of the trial, therapeutic interventions, data collection requirements, and patient population. Trials suitable for virtual trials may include observational studies, noninvasive interventions, chronic disease management, postmarketing surveillance, and patient-reported outcome studies.

On the other hand, trials that will require in-person visits are likely to be interventional trials, phase I safety studies, or imaging or biomarker assessments. Vulnerable or high-risk groups also benefit more from in-person testing.

It is important to note that the decision to conduct a trial virtually or to require in-person visits depends on careful consideration of the trial’s specific objectives, participant characteristics, regulatory requirements, and available technology. Hybrid approaches that combine virtual and in-person elements can also be used to balance the convenience and need for physical interactions.

Despite advances in clinical trial data management, data integrity can also be a significant challenge. Clinical trials generate a huge amount of data that must be managed and integrated across multiple sites, systems and stakeholders. Data quality assurance, standardization, and interoperability are critical to meaningful analysis and interpretation—and to avoiding fraudulent data.

In my opinion, one of the most important changes in clinical trials is digitization. Today, EDC is the minimum standard for clinical trials and data collection, but we can do even better by adopting newer technologies such as electronic clinical outcome assessment (eCOA). eCOA systems enhance clinical data collection outside of traditional research sites and improve data quality through the use of smartphones, tablets, or portable devices—provided to patients or used on their own devices in a bring-your-own-device test.

Even with rigorous statistical planning and adaptive trial design, clinical trials have a level of uncertainty due to the human element and the inherent variability of disease progression, treatment responses, and patient adherence. Uncertainties related to clinical outcomes, sample sizes, and statistical power may also affect trial design and performance.

It’s also important to remember that clinical trials operate within the broader health care ecosystem, which is influenced by external factors such as health care policies, market dynamics, patient preferences, and geographic differences. These extraneous factors may affect patient recruitment, affect the trial schedule, and affect the generalizability of the trial results.

The pharmaceutical industry must continue to improve its clinical trials. Digital technologies have helped address some of the challenges posed by the pandemic and introduced new trends in the practices of contract research organizations and pharmaceutical/biotech companies in conducting clinical trial analysis. Digital data capture technology has ushered in a significant change, rendering traditional paper record keeping methods obsolete. Advances in technology are also enabling more patient-centered approaches to conducting trials, with trials using remote data collection technologies, reducing reliance on clinical sites. As the industry moves in this direction, it will be critical for CROs and sponsors to have a comprehensive understanding of virtual, decentralized, and hybrid trial approaches, as well as a solid understanding of real-world data.

Putting patients at the center of clinical trials is critical, which means involving patients in trial design, ensuring that informed consent processes are clear and understandable, minimizing burdensome procedures, and taking patients’ preferences and perspectives into account. A more patient-centered approach allows for improved patient recruitment, retention, and supports more reliable and valid data.

Embracing digital and virtual technologies can make this possible. However, pharma will need to understand how to handle and analyze the huge increase in data, including real-world data. Trials will need to be carefully designed and processes for data validation and verification must be in place. Done successfully, the payoffs are significant: increased efficiency, reduced costs, and more accurate data.