Accenture Report: The Three Waves of Clinical Trials Transformation — A New Paradigm Driven by Emerging Technologies and Data Applications

In September 1928, after a two-week vacation, Alexander Fleming discovered mold growing on a Staphylococcus culture plate in the laboratory at St Mary's Hospital in London, thereby discovering the therapeutic efficacy of penicillin. After Fleming ceased his research on penicillin, others continued related studies. It was not until 14 years after the initial discovery of penicillin’s efficacy that the drug demonstrated its value in 1942, when it was used to treat victims burned in the Coconut Grove fire in Boston, United States.

Today, more than 90 years after Alexander Fleming’s initial discovery of penicillin, the average time required for a newly discovered drug to gain market approval has reached nine years. The research pathway involves scientists, researchers, pharmaceutical companies, patients, physicians, clinics, and regulatory agencies, with average costs soaring to $2.6 billion. Thanks to the introduction of new data sources—including patient-centric data collection and emerging technologies that streamline data acquisition, such as wearable devices and portable clinical equipment—this vast healthcare market is undergoing subtle yet significant changes.

Accenture examined the current clinical trial landscape and discussed its findings with healthcare experts and several international pharmaceutical clients. The study primarily assessed the current state of clinical trials from three perspectives: the experiences of patients and clinicians in clinical trials, clinical data collection and management, and cutting-edge technological approaches. It also identified three waves that will emerge in the future development of clinical trials:

Recent Developments: Leveraging new technologies to connect patients, patient data, and clinical trial sites more closely;

Mid-term Development: More digital tools are being integrated into clinical trials to accelerate patient enrollment and exclusion, improve patient compliance, and streamline data turnaround processes;

Future Development: Unified data standards will deliver a novel personalized diagnosis and treatment experience, while offering opportunities to leverage data research to transform traditional clinical trial methodologies.

Accenture’s research indicates that, driven by advancing technologies, access to real-time clinical data, and emerging use cases, the efficiency and effectiveness of clinical trials will be significantly enhanced in the next wave.

This will also enhance the industry’s innovation capabilities. Accenture has identified four key concepts driving recent developments in clinical trials: immersive therapy, continuous patient data collection, more flexible clinical trial protocols, and more closely integrated support systems.

1. Immersive Therapy

Immersive technologies have been adopted as an effective therapeutic approach for pain management. VR technology utilizes virtual reality platforms to treat chronic pain, acute pain, and anxiety symptoms.

Accenture predicts that extended reality (XR) technology will become prevalent in clinical trials in this manner. It can also improve adherence and help achieve better overall outcomes. A 2017 study by Cedars-Sinai showed that VR therapy reduced pain scores in hospitalized patients by 24%.

XR technology is also an effective tool for patient care. It can help educate patients about their treatment plans, explain the impact of diseases on human anatomy, or clarify upcoming diagnostic and therapeutic procedures.

As the cost of XR technology continues to decline and its adoption in daily life—such as in gaming or workplace training—increases, Accenture predicts that XR technology will have a greater impact across the healthcare sector. Furthermore, Goldman Sachs has projected that revenue from AR/VR applications in healthcare will reach $5.1 billion by 2025.

Patient adherence and retention represent an intriguing new frontier. Moe Alsumidaie, Chief Data Scientist at Annex Clinical, notes that patient non-adherence is a significant challenge in clinical trials, with 40% of patients failing to comply with the requirements for investigational medical products after 150 days. If XR technology can make an impact in this area, its implementation efficiency will be substantially enhanced.

2. Continuously Collected Patient Data

People now use wearable technology in their daily lives to track exercise, heart rate, sleep patterns, and more. In the medical field, it is now easier, more immediate, and “cleaner” than ever before to collect individual health data.

Built-in sensors in clothing, phones, and home devices can effortlessly collect patient data continuously, 24 hours a day.

Small, wireless, self-powered passive sensors can be placed on specific areas of the human body, allowing users to “wear” them more comfortably and thereby improving the quality of captured data. For example, temporary digital tattoos resemble a new generation of skin patches, with functionalities including ECG monitoring, fall detection, and even drug delivery.

This approach can provide a real-time, holistic view of a patient’s health status by capturing the patient’s exposome. The exposome is defined as the totality of an individual’s external environmental exposures over time, encompassing not only these exposures but also their relationships with health outcomes. By characterizing the exposome, one can more accurately delineate the potential impact of environmental factors on a patient’s treatment.

In broader patient populations, exposure data helps enterprises gain a deeper understanding of variations in treatment response. For instance, patients with similar medical histories may respond differently to treatment due to varying levels of air pollution in their respective cities. Up-to-date local data on pollen counts and other airborne particulate sensors can provide clinicians with more detailed information and help identify the causes of unexpected differences in patient responses.

Data Transparency and Ownership

The standard wearable device market is booming, making it increasingly easier to acquire and monitor patient health-related data. In March 2019, Apple announced a new electrocardiogram (ECG) application that allows users to monitor their hearts for irregular heart rhythms using its FDA-approved Apple Watch Series 4.

With improvements in the integration of electronic health records (EHRs) and the opening of direct patient access, individuals will be able to access their personal records at any time and exercise true “ownership” over them. This marks the first step toward opening the healthcare market to patients who wish to monetize their data through sharing.

Data processing in the healthcare industry has always been a sensitive topic. Enterprises’ demand for patient data transparency encompasses not only immediate access to such data but also comprehensive openness of the data to patients themselves.

A critical question arising from this is whether companies that use patient data are responsible for ensuring that adequate testing is performed on everyone’s data, and whether they have an obligation to communicate all results to patients in an appropriate manner after testing, even if this is entirely unrelated to the original purpose for which the data were used.

Imagine a company using an individual’s genetic data to test new products with fully informed consent. During these tests, it identifies markers that may indicate early signs of Huntington’s disease in the patient. Does the company have an obligation to inform the individual? Would the individual even want to know in the first place? What if the test result is a false positive?

3. More Flexible Clinical Trial Plans

Cutting-edge clinical trials adhere to pre-specified adaptive trial design methodologies, adjusting as the trial progresses to iteratively update processes and operational procedures. These updates may include sample size adjustments (potentially enrolling additional patients to preserve statistical power), patient re-randomization (modifying allocation methods to assign more effective treatments to patients), and the addition of new treatment regimens to the trial.

These adaptive trials have been widely recognized for their potential to improve trial success rates and significantly reduce time and resource costs. Currently, adaptive trials are applied in only a small fraction of studies. Recently, a limited number of guidelines on their design and implementation have begun to emerge. In a 2018 article in the BMJ, the authors outlined best practices for developing operational procedures for such trials and expressed support for this approach.

Furthermore, data from electronic capture can enable faster analysis, thereby facilitating timely updates and iterations. The use of synthetic control arms is particularly beneficial in reducing trial risks for vulnerable populations, such as the elderly or pediatric patients.

Flexible trial designs will reduce the overall clinical trial duration by accelerating go/no-go decision-making and allowing for more relevant analytical time points. Furthermore, they will minimize patient side effects. Studies indicate that 29% of patients withdraw from trials after consenting due to concerns about adverse effects. Flexible trial designs will help mitigate this issue and improve patient retention rates.

4. More Closely Integrated Support Systems

Many stakeholders have stated that participating in clinical trials may lead to a sense of isolation for patients.

Patients should be appropriately connected with one another and with researchers to facilitate more meaningful communication throughout the trial. This will enhance trust and transparency across the entire trial process, thereby improving patient engagement and retention.

The widespread use of social media platforms provides pharmaceutical companies with the opportunity to support such connections.

Although such practices are currently prohibited within the industry (ethically, pharmaceutical companies are not permitted to directly contact clinical trial participants), the potential presented by social media has still attracted those involved in this research, and pharmaceutical companies are gradually shifting toward patient-centric research approaches.

As the trial progresses, patients need, request, and obtain more information related to the trial. The confidentiality requirements between the drug and the patient are clearly a challenge.

Accenture’s research indicates that fully digitalized trials may become the standard in the near future.

Amid this wave, physicians, patients, and their families will be guided to appropriate available trials for eligibility screening, leveraging intelligent digital agents to ensure comprehensive understanding and facilitate enrollment. Driven by stringent data standards and considerations of security and ownership, decentralized data repositories will be employed for data management, thereby enhancing the overall governance of clinical data. In this context, patients will retain ownership of their data and may choose to share it with researchers and clinics to further advance research agendas.

Accenture’s mid-term development insights focus primarily on patient experience and the rapid influx of large volumes of data, centering on four key areas: intelligent patient screening and enrollment, data trustworthiness, real-time data management, and home-based clinical trials.

5. Intelligent Patient Enrollment and Exclusion

Before patients can be enrolled in a trial, they must first become aware of its existence. Eighty-six percent of clinical trials experience delays in their planned timelines due to recruitment challenges.

To enable precise patient recruitment from the outset of clinical trials, Accenture predicts that physicians, patients, and their families will be able to leverage intelligent digital agents. These agents will guide patients to appropriate existing trials based on patient data, verify eligibility against relevant trial criteria, and provide supplementary information. Upon obtaining informed consent from the patient, the agent can complete the enrollment process.

To this end, all parties involved (e.g., trial sponsors, pharmaceutical companies, contract research organizations, and hospitals) need to adopt paperless management systems. These electronic systems can facilitate the accurate collection of trial information, including inclusion and exclusion criteria, as well as the acquisition of patients’ electronic health record (EHR) data. To make such systems applicable to all known clinical trials, all collected data must be represented in a consistent, standardized, and unified format.

Artificial intelligence and machine learning will provide new technical means for intelligent digital agents. The maturation of these technologies will enable the training of digital agents to perform complex cognitive tasks, such as determining patient eligibility for clinical trials based on the specific requirements of each trial and the individual health status of each patient.

By facilitating rapid and effective patient identification and enrollment, these digital agents should be able to recruit patients easily and quickly.

From the patient’s perspective, intelligent eligibility screening can provide a more comprehensive support experience by directly guiding them to appropriate clinical trials and educational materials.

However, the efficiency of the system may affect patients’ trust in it, particularly with regard to social responsibility and ethical considerations. As technology focuses on specific populations, it may introduce inherent biases and risks. Factors related to low- and middle-income countries or socioeconomic status may give rise to inherent biases within the system. Patients can only connect to intelligent systems through internet access or accessible electronic health records (EHRs). This may also skew the eligible population toward certain demographic groups.

6. Data Trustworthiness

Data ownership, security, and de-identification are among the most prevalent topics in today’s technology landscape. As the complexity of data collection in healthcare increases, the importance of these issues—and patients’ sensitivity toward them—has become increasingly pronounced. Research by Accenture indicates that distributing clinical trial data among all stakeholders within the system can help overcome the fragmentation of healthcare systems and the associated lack of transparency.

The use of decentralized data repositories will enhance data security, ensure proper data ownership and privacy, and maintain the accuracy of clinical trial data. Furthermore, this lays a solid foundation for the online regulatory oversight of clinical trial results.

In clinical trial regulations, records of trial analyses are critically important, and blockchain technology can ensure that the complete history of a trial is immutable and traceable. Patients, physicians, devices, and clinical systems will need to communicate with the database through the use of decentralized applications (DApps). These applications are collaboratively built by numerous users on the blockchain network.

Actual ownership of medical data should be more clearly held by patients, enabling individuals to exercise more direct control over their information. Blockchain and cryptocurrency will also facilitate payments between patients and researchers for health data.

As mentioned above, the continuous collection of patient data will mean an increased burden of responsibility for pharmaceutical, biotechnology, and healthcare companies in terms of data security. However, systems built on “data standards trusted by stakeholders” can offset these growing demands. Trusted and secure data accessible to all stakeholders in clinical trials can also make regulatory reviews faster and easier. The advantages of this system will extend to other aspects of drug lifecycle management.

7. Real-Time Data Management

To efficiently process large volumes of continuous patient data, clinical trial organizations will require an equally rapid system for cleaning, aggregating, coding, storing, and managing such data. Research by Accenture indicates that further technological advancements will make this process fast, real-time, and dynamic.

Enhanced electronic data capture should reduce the impact of human error on data collection and enable seamless integration with various databases. The development and maturation of artificial intelligence will also facilitate a range of processes, including real-time data capture, autonomous agents, and connected devices.

Seamless data management will reduce the time and labor invested in clinical data management. Accenture’s research indicates that this may be one of the most valuable improvements in the clinical trial process.

Shifting to direct data acquisition from the source and integrating data systems with AI will enable researchers to devote more time to higher-value clinical tasks. The associated data can also facilitate researchers’ observation of patients’ behavioral health and allow companies to consider the impact of environmental and lifestyle factors on treatment outcomes.

8. Home Testing

In some clinical trials, frequent outpatient visits for patients will soon become a thing of the past. Internet-connected devices, advancements in the logistics industry, and improved virtual communication will mean that outpatient consultations can be conducted at times and locations more convenient for patients.

A prime example is the AOBiome trial. This Phase 2b acne study screened over 8,000 candidates and enrolled 372 patients in a 12-week trial with no on-site visits. Compared with traditional trials, it also demonstrated greater inclusivity in recruitment, particularly with an increased proportion of non-White participants.

Patient-centric connected devices will be key here. Connected devices (such as wearables, nanotechnology, XR, virtual assistants, and robots) can be used to accurately and efficiently capture measurements during virtual consultations. The widespread adoption and use of virtual communication have already facilitated remote interactions between clinicians and their patients.

Advancements in home delivery technologies, such as improvements in cold chain logistics, medical devices, and 3D-printed medications, will transform the traditional model of providing services to patients during clinic visits by directly delivering trial-related supplies to their homes. This shift from clinics to patients’ homes will enhance patient compliance and minimize mid-trial dropout rates caused by the need for frequent clinic visits. Home-based trials can also save pharmaceutical companies money, as establishing and managing each trial site costs an average of $9.7 million.

A potential downside is that as technology-mediated interactions increase, so does the risk of dehumanization. Technology cannot fully replace the communication, support, trust, and care provided during follow-up visits. As our reliance on technology grows, the management of interpersonal relationships becomes increasingly important. Developments such as home-based trials will need to prioritize this aspect.

How can clinical trials be conducted without posing any risk to patients? Accenture’s research points to a future in which algorithms will simulate patient journeys through trials to predict clinical outcomes. They propose three transformative ideas for the future development of clinical trials: global standards for clinical trial data, personalized trials, and clinical trials conducted solely using data and algorithms.

9. Global Clinical Trial Data Standards

For anyone utilizing healthcare or clinical data across multiple systems, the standardization of data collection and management is a well-known pain point.

Practitioners hope that global regulations will provide unified standards, including those from standard-setting bodies such as the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH), industry organizations such as CDISC, and other collaborative groups.

Such a global, standardized, and secure database holds untapped potential for new research discoveries. Consequently, the pharmaceutical industry has been striving to advance this vision to date. However, establishing a unified data standard may not be so easy.

Creating data structures that are trusted by all parties and immutable is crucial for global standardization efforts. Among these, establishing unified standards for all AI platforms used in data management is the top priority.

Many enterprises have independently attempted standardization in their operations, but later realized that these standards can only be truly effective through collaboration.

A globally applicable general declaration would eliminate the challenges encountered in the aggregation and application of clinical trial datasets. It would also establish an independent, global-level pool of clinical trial data.

This may be an ideal solution to many problems, but implementing it in reality remains fraught with challenges.

Imagine that meeting a single set of regulatory standards and internationally recognized ethical guidelines could allow new drugs to satisfy the regulatory requirements of different countries worldwide. Global data would enable universally accepted clinical trial results.

10. Personalized Trials

Personalized (or precision) medicine is a top priority in omics research; *Nature* alone published multiple significant papers on this topic in 2018. Research and advances in building predictive models using healthcare data are expected to drive the widespread adoption of personalized medicine in the future.

Ample information on specific patients will provide deeper and more accurate insights into their medical history and surrounding context. This will enable predictive models to personalize treatment modifications based on patient-specific data.

Therefore, traditional clinical trial regulatory approaches must be reconsidered to allow for investigational studies of the overall treatment process (e.g., cell and gene therapies, such as CAR-T cell therapy) rather than focusing solely on the product.

Predictive models require large volumes of accurate data for training. To ensure efficiency and productivity in data application, patient medical data must be organized in standardized formats. Assuming such infrastructure is in place, advances in deep learning and predictive modeling can leverage vast databases to generate insights and produce predictions for each individual participating in clinical trials.

Clinical trials tailored to each patient should minimize, or even completely avoid, side effects and improve the prognosis for every patient. They should also provide specific treatment options for each participant in the trial, which may facilitate further research into treatments and disease progression.

11. Clinical Trials Conducted Solely Through Data and Algorithms

By replacing traditional clinical trials with algorithms, we can further reduce the risks to trial participants. In this scenario, data from global, standardized, and secure clinical databases will be used to evaluate the progression of simulated patients through the trial and accurately predict trial outcomes.

This is not as unrealistic as it may seem. The development of simulated clinical trials is currently in its early stages.

Further research and development of the theory and application of these methods will yield more robust and advanced simulation techniques for purely data-driven clinical trials. Many practitioners believe that algorithm-based trials are perhaps “the most likely” and “the most interesting.”

Data-driven clinical trial processes could reduce the average trial duration from nine years to just a few hours, with trial costs and patient risks plummeting. Against the backdrop of advancements in quantum computing, this new approach to clinical trials appears increasingly feasible.

Furthermore, by integrating algorithmic trials with the evolution of drug discovery, it is fundamentally ensured that every approved and manufactured drug is efficacious and possesses commercial viability.

New real-time patient data sources and improved technological accessibility will have a profound impact on everyone involved in the clinical trial process. However, the entire clinical landscape must evolve to enable and accommodate such innovation. In this nuanced, traditional, yet forward-looking industry, the development of ethical and regulatory frameworks, along with further discussions, continues.

The near-, mid-, and long-term evolution of clinical trials will depend on industry leaders who clearly recognize the advantages and importance of this data-driven future direction.