6 Trends Shaping the Future of Clinical Trials in 2023 and Beyond

Context: Why is the clinical research industry changing so quickly?

The clinical research industry has seen steady improvements over the past several decades, but technological advances and unprecedented global issues such as the COVID-19 pandemic have spurred the transformation of this industry at a breakneck speed.[¹]

In the past three years alone, the face of the clinical trial arena has changed completely in response to new challenges, bringing a host of new possibilities and opportunities.

In this article, we look at these altering factors and discuss the trends shaping the future of clinical trials in 2023 and beyond.

Exhibit A: Common issues causing challenges for clinical researchers

Despite the changes, there are still several significant challenges that continue to plague clinical research. Furthermore, their consequences cannot be ignored as they can lead to study complications either by exceeding timelines or going over budget.

Common challenges that hinder clinical trials, regardless of location or medical condition, include the following:

Unequal representation of minorities in clinical trial participants

When the study sample does not represent the actual patient population, it questions the validity of trial results and whether they are accurate enough for the general population. [²]

The reason for this disproportionate sampling is complex, and you can read more about it in this article, Three Clinical Trial Recruitment Challenges and Strategies to Overcome Them.

Inability to meet enrollment numbers

Despite the advances, 85% of clinical trials still struggle to enroll enough participants. [³]

And such poor enrollment severely delays a research study, costing sponsors and CROs. If an adequate number of participants are not enrolled, developing novel, life-saving drugs can become a much longer process than initially expected.

Poor patient retention

Even after enrollment numbers have been met, there is always a chance that patients are unwilling to participate for the length of the clinical trial. While participant drop-outs are normal, if the attrition rate is too high, it can affect the accuracy of results or, delay the trial further.

For more information on this challenge, check out this discussion Exploring the Significance of Clinical Trial Patient Retention and Strategies for Improvement.

Predominance of manual data entry

Despite the technology available, adoption can be slow and many still rely on staff to manually enter data in study records. [⁴]

This approach not only slows down trial operations but is also prone to human error and becomes increasingly difficult to track when trials are multicenter or international.

Difficulties navigating regulatory guidelines and standards

With new solutions and strategies come new ethical concerns regarding patient safety, privacy, and medical autonomy worldwide. [⁵]

To safeguard patients, regulatory boards and government oversight organizations continuously update and modify their policies to address these and other foreseeable issues in the future of clinical trials. However, staying up-to-date and accommodating them in study documentation and protocols is becoming more and more challenging.

Exhibit B: The alluring benefits of tech adoption and new trial models

While the challenges may be present, strategies that use some form of technological solution have shown effectiveness in diminishing and overcoming barriers. [⁶]

But this is just one aspect. Technologies can help sponsors immensely by streamlining operations, managing data and accessibility, improving trial quality, facilitating auditing and regulatory compliance, enhancing oversight, reducing the workload on study staff, and so much more. With such compelling benefits, technologies are an investment that is simply too good to pass.

Besides the tangible benefits, many of these digital solutions are becoming industry standards, and companies adopt them to stay competitive.

Exhibit C: The COVID-19 pandemic added fuel to the fire

Pre-2020, many technological initiatives were beginning to be partially adopted in clinical research to assist conventional methodologies, not replace them.

However, with the sudden spread of COVID-19 and the ensuing contact restrictions and lockdowns, sponsors were forced to make drastic changes to keep trials on track. [⁷]

Among these was the immediate phasing out of in-person and localized protocols in favor of tech initiatives that adhered to social distancing mandates and overcame travel restrictions, such as decentralized clinical trials, mHealth, and telemedicine.

Technology proved to be an invaluable asset during this time, not only because it allowed research to continue but because it also facilitated patients.

Then, as restrictions were lifted post-pandemic, clinical study sites continued to use these solutions they had invested heavily in to find that they were still better than conventional methods, thus sealing the presence of technologies in the future of clinical trials.

6 Major trends shaping the future of clinical trials

Now that we understand what prompted these trends and why it is increasingly important for sponsors and clinical researchers to get on board to remain competitive, efficient, and productive in the effort to get novel treatments to patients safely and swiftly, let’s examine 6 major trends reshaping the clinical research industry this year and into the foreseeable future.

1. Patient-centric trials

Patient-centricity in clinical research keeps the patient in mind, prioritizing their experience in all operations to reduce any personal burden they may feel while participating in a clinical trial.

This approach is especially important because it improves the rapport between patients and researchers and tackles major challenges like low enrollment rates. When the recruitment process is optimized for patients, it is more convenient and easier for patients to join. Therefore they are more enticed to enroll.

Furthermore, if patients have good experiences, they will speak more highly of your trial and can encourage positive sentiments about clinical research. [⁸]

As for designing patient-centric clinical trials, there are several practices sponsors and CROs can incorporate during their trials, including the following:

Introducing flexibility in schedule through remote visits
Simplifying protocols so that they are clearer and easier to follow
Maintaining effective communication with patients
Creating engaging clinical trial content
Introducing mechanisms to collect patient feedback and incorporating it
Providing participants reimbursements to lessen the financial burden of participating
Consulting with patient advocacy groups to design clinical trials that address the needs of actual patients besides answering research questions

These are just some of the ways sponsors can develop patient-centric trials. For more information about patient-centricity and its future in clinical trials, refer to this article: Putting Patients First: A Guide to Patient Centricity in Clinical Trials.

Challenges to overcome

In the quest for patient-centricity, the validity of the clinical trial must not be compromised. Researchers still need to set up robust protocols to ensure the trial is scientifically accurate, and in some cases, patient comfort may need to be balanced with other factors. [⁸]

Certain medical conditions and interventions will require difficult procedures, tests, or complex protocols, but that is just the nature of clinical research. However, there is always room for balance, and when faced with complex trial protocols, researchers can make patient-centric adjustments in other areas to maintain a positive patient experience.

2. Decentralized clinical trial models

Decentralization in clinical research is not new and has been in the works for decades, albeit not at a fast pace. While the benefits were well understood, it wasn’t until the 2020 COVID-19 pandemic that the concept of remote clinical trial was launched to the forefront.[⁹]

While sponsors and CROs were forced to adopt decentralized trials (DCTs) and hybrid trials during the pandemic, this type of clinical trial has continued to be effective as it overcomes geographical barriers as participants interact with healthcare proxies at local healthcare facilities or even at-home visits which increases the reach of a clinical trial and has the potential to bring in a more diverse population of trial participants.

Furthermore, decentralization techniques also reduce administrative burdens via digital solutions that streamline operations, centralize data, reduce staffing requirements, and support management responsibilities.

Challenges to overcome

However, decentralized clinical trials are not always feasible as the degree of decentralization depends heavily on the medical condition, novel intervention being investigated, and diagnostic tests being used.

Therefore, trials requiring close personal care, specialized equipment for tests and procedures, or involving a drug that cannot be safely delivered from patients cannot be fully decentralized.[⁹]

Finally, decentralization requires all clinical trial staff to be comfortable using the new systems. Overcoming this learning curve requires substantial investment in training to ensure this model works efficiently.

3. Rapidly increasing adoption and integration of MedTech, eClinical, and mHealth systems

Technology has always had a significant impact on shaping the future of clinical trials through the development of software and systems that digitalize certain aspects of clinical trial operations. [¹⁰]

Known as mHealth, these digital solutions refer to the mobile, wireless health technology that removes many barriers separating researchers and participants. Some of the most popular technologies over the past few years include: [¹⁰]

ePRO and eCOA
Wearable, online monitoring devices
eConsent
Telehealth, such as remote study visits via phone or video calls
EDC for quickly aggregating data from various sources

These solutions enable decentralized operations and streamline numerous operations for consistency and coherence, such as remote data collection.

Along with increased adoption, there is an ongoing move toward automatic integration via API, further reducing manual tasks through comprehensive, single-platform mHealth software that combines the functionality into an all-in-one single sign-in system, such as IQVIA’s Orchestrated Clinical Trials, Veeva Clinical Suite, and Medidata Clinical Cloud.

Challenges to overcome

Given concerns about the patient data these systems have access to and the risk of tampering and hacking, there are growing concerns about data privacy, resulting in stricter regulatory guidelines and standards, which can be challenging to comply with.

Additionally, as the complexity of digital solutions increases, so does the need for technologically-competent staff. Hiring and training such personnel takes resources; not all clinical sites can take that burden.

Moreover, an “all-in-one” solution does not mean every mHealth system is right for every clinical trial. Sponsors must take the time to research to ensure they are investing in the one that fulfills their organization’s specific requirements, such as the ability to integrate with existing systems and workflow; otherwise, they will cause further delays.

Many companies even offer “mix-and-match” options that let sponsors pay only for the solutions they need instead of the entire system.

4. Accessibility: Fostering awareness and increasing diversity in clinical trials

The future of clinical trials relies heavily on accessibility because it overcomes many geographical, social, and financial barriers preventing underrepresented groups, such as minorities and underserved populations, from participating in clinical trials.

Not only does it make it simpler for them to take part, but it also improves the diversity of study samples, making them more representative of patient populations and, therefore, easier to translate results to the general public. [²]

Furthermore, accessibility improves awareness and raises health literacy about clinical research participation, encouraging people to find more information and enroll. This is especially important among some communities who have a justified negative perception of medical research because of the long, unfortunate history of a lack of access to medical research.

Sponsors and CROs don’t have to look far for accessibility solutions as online platforms such as Power are available. These sites provide the latest information on clinical trials and allow sponsors to speak directly to their target patient population.

Additionally, many other online resources provide accessibility plans, such as the FDA industry-wide guidance, the NIH strategic plan for 2021 - 2025 for addressing health disparities, and diversity strategy guides.

Challenges to overcome

Approaching under-represented communities should not feel like an afterthought. Outreach material needs to be honest, transparent, and culturally sensitive consistently.[¹¹]

Furthermore, eligibility criteria need to be reassessed to ensure that they are not the barrier stopping willing people from such groups from participating.

However, it is critical to find a balance as inclusion and exclusion criteria are necessary to provide statistical power to clinical trial results. Finding a balance is key.

5. Artificial intelligence (AI) and machine learning (ML)

AI and machine learning are already being leveraged in clinical research to help reduce costs and streamline almost every stage of clinical trial operations, from matching potential participants to clinical to managing patient data. [¹²]

These AI and ML systems take advantage of the vast data stores across multiple healthcare sources, pulling and organizing structured and unstructured information for rapid access and analysis.

Furthermore, this tech is augmented with user-friendly interfaces that give all clinical research stakeholders: such as sponsors and researchers, the ability to see real-time information for faster, better decision-making throughout the lifetime of a clinical trial.

Some of the most innovative uses of these AI models include:[¹³]

Digital twins of subjects to create artificial control groups that simulate how subjects would respond if they were not given the novel intervention
Deep learning scans that ingest health records and pathology reports to compare against eligibility criteria greatly decrease the time spent pre-screening potential participants.
Designing and executing more efficient automated processes and computational tools in trials that are less error-prone and generate better outcomes.

Challenges to overcome

Even though AI professionals have found their place in the clinical research world, there is still a lack of expertise that does not meet the ever-rising demand.

Additionally, there is still a considerable reluctance to adopt AI and ML models in the healthcare and clinical research industry. One major reason is the difficulty in understanding these complicated models. Therefore, investment is necessary to train clinical trial personnel in AI practices and languages.

Finally, there are growing privacy concerns arising from the patient data such models can access and how to control it to prevent misuse.

6. Big data: Increased use of real-world data and real-world evidence

While these are two separate concepts, big data and real-world data (RWD) share a link and will continue to play an important role in the future of clinical trials. [¹⁴]

Big data refers to the massive troves of data available nowadays. On the other hand, RWD, and subsequently real-world evidence (RWD) that comes from studying RWD, is collected from:[¹⁵]

Real health records from patient populations
phase IV clinical trials, post-marketing surveys, and surveillance studies
observational and longitudinal studies

While the sources vary, both provide large quantities of data analyzed to reveal important health outcomes. These outcomes are fed back into drug development efforts that open the door to creating new trial models.[¹⁶]

This positive feedback loop also enables sponsors and CROs to design clinical trials that focus on evidence-based medicine practices and address patients' most pertinent concerns in their treatments.

Challenges to overcome

One of the most significant challenges hindering the use of big data, RWD, and RWE are regulatory considerations that still need to be sorted through and developed into a solid framework that sponsors and CROs can use to design their clinical trials.

Furthermore, compared to other clinical trial models, such as traditional randomized controlled trials (RCTs), RWE studies lack control over covariates, making them less statistically powerful in some cases. Finding ways to manage these covariants and account for them in study outcomes is necessary to ensure their accuracy.

Conclusion

As the landscape of clinical research continues to evolve with advancements in technology, the future of clinical trials fills with exciting possibilities.

While many challenges still plague the industry, such as enrollment problems and diversity concerns, numerous solutions and strategies are on the horizon that show promise in alleviating these concerns for better, faster drug development.