For many years, health researchers have been talking about the potential of real-world data (RWD) that can revolutionize every phase of clinical research, from trial design to outcome measurement. The healthcare system tracks hundreds of millions of patient touchpoints each year, recording this information in the form of electronic health records (EHR), insurance claims, and other clinical systems that represent extremely rich sources of data and insight. So, if the industry is so excited about this revolution, why hasn’t it been adopted more widely in clinical research?
The main reason is that clinical trial teams cannot use RWD to a large extent, which is hindered by patient privacy, regulatory restrictions and different data structures. Although significant success has been achieved in the use of available EHR and claims data, deeper and more valuable clinical data has been inaccessible. It is not easy to liberate genetic, laboratory, and pharmaceutical data located deep in the healthcare subsystem, while protecting patient information and navigating in a global regulatory environment. But considering the benefits of real-world evidence (RWE), the effort is worth the effort.
The real data gap in clinical trials
There is no doubt that RWD and RWE are playing an increasingly important role in healthcare decision-making and research. Both are regularly used to monitor post-market safety and adverse events. According to Medidata’s presentation on Reuters Event Pharma: Clinical 2021, RWD also plays an important role in the regulatory approval of new indications: 75% of new drug applications and biolicensing applications in 2020 contain evidence from RWD. Many hospitals have adopted the “learning health system” model to track and analyze data for research and continuous improvement. On the contrary, most clinical trials are conducted in a “one-off” manner, unable to take advantage of the treasure trove of efficacy and safety signals in the medical data ecosystem.
The use of RWD in RCTs to develop and validate new treatments is the natural next step in healthcare delivery and clinical research development. The potential for data use begins long before the approval stage-the opportunity to enrich almost every step of the clinical trial process.
Queue identification and enrichment
The use of RWD can help optimize the trial group by identifying a more homogeneous and targeted patient population. These data may help to cluster cohorts with similar characteristics that respond to specific drugs in the same way, or have biological signals that indicate the likelihood of disease progression. Artificial intelligence can help to dig deeper into usually isolated clinical data sets, including biomarkers, laboratories, images, transcriptomes, proteomes, and genomes, thereby enabling more precise research designs to review treatments.
Such in-depth data can also shed light on the predictive trends that can be used in the trial population. Targeting cohorts that are more likely to respond positively to drugs and/or the ability to exclude populations who may be more prone to specific safety issues can allow researchers to conduct smaller clinical trials, while potentially producing faster and more decisive clinical efficacy and Security results. For example, there is evidence that cancer patients with mutations in the HER2 gene do not respond to platinum-based chemotherapy. Using RWD’s ability to retrospectively “screen” cancer patients for HER2 mutations and track their disease progression can identify patient subgroups that will become excellent targets for alternative non-platinum treatments.
Patient recruitment
One of the main obstacles to many clinical trials is being able to meet the registration quota in sufficient time to prevent the trial from closing. Although people are generally aware of the existence of untapped patient groups, the map of how to find them and their providers is still elusive. The use of RWD and the mining of in-depth data sets are expected to provide access to these difficult-to-find patients, and are particularly useful for finding patients with rare diseases, rare cancers, and cancers with specific gene mutations.
In addition, in-depth data mining can pre-identify patients who may be resistant to the standard of care. These patient pools can then be contacted and ready for off-line treatment in available clinical trials.
Finally, the standard of care for some disease states is non-intervention, because the proportion of patients whose condition may deteriorate is too small in the total population. Deeply digging into RWD can find profiles of known progressors, and predict who is most likely to endure disease progression by drawing multi-factor profiles. These factors are not only based on the patient’s disease state, but also based on treatment, prescription, and geographic-demographic statistics.
Of course, gaining health insights should never come at the expense of patient privacy. RWD is usually anonymized by generating a unique code (token) for each patient to protect their identity while enabling the team to aggregate across different data sets. However, in some cases, it may be beneficial to associate data with specific patients—for example, to provide access to clinical trials in a way that respects patient privacy.
Participation of patients and doctors
In an increasing number of cases, the successful recruitment of patients is through the use of the relationship between the patient and his HCP. But to achieve this goal, providers need to understand the trial opportunities that suit their patients. RWD can play a key role in identifying relevant cohorts and informing them of HCP trial opportunities. The provider can then advise and accompany the patient throughout the trial. The new partnership between researchers and medical service providers has the potential to bring a win-win situation for all parties, adjusting research and medical services around the common goal of improving patient outcomes.
The doctor can:
- Access to more extensive research that can meaningfully improve the health of patients
- Carry out pilot activities in existing bedside workflows
- Screen, diagnose and recommend patients for potential research
- Maintain continuity of care and maintain income by supervising patients’ medical evaluations throughout the trial
- Conduct joint research with other providers to expand the impact of their work
At the same time, cooperation with suppliers enables researchers to benefit from the doctor-patient relationship in the following ways:
- Identify pre-screened participants who meet the research criteria
- Recruit and recruit patients who are more likely to participate in the study
- Improve research compliance
In the final analysis, the public is the ultimate beneficiary of the provider-researcher partnership. Expanding clinical trial channels will enable more patients to obtain life-changing treatments.
Expansion of clinical trials through the control arm
In addition to identifying suitable participants, RWD can also provide historical or synthetic control arms. These external arms can minimize patient exposure while enabling new therapeutic and/or interventional medical device candidates to emerge at a faster and more effective rate. By tracking the progress of the population of patients who meet the inclusion and exclusion criteria and are not receiving drug treatment outside of the trial, researchers can avoid the need to recruit a control population to participate in the trial in some cases. An important patient benefit of this approach is that all participants in the trial received a comparison of the drug with a placebo. RWD also allows meta-analysis using existing data sets to understand new indications and/or side effects that may not be revealed in any clinical trial due to insufficient numbers.
In some promising cases, external control helps to obtain drug approval: RWE is used to support BAVENCIO, Pfizer’s immunotherapy for metastatic Merkel cell carcinoma, and BLINCYTO (Amgen’s treatment for B-cell precursor acute lymphoblastic leukemia). Treatment) effective decision-making. Both have obtained accelerated approval using data from external historical controls.
Simplified data aggregation and continuity of care
RWD can also help improve outcome measurement in clinical research by facilitating the integration of EMR, remote patient monitoring (RPM), and electronic patient report results (ePRO) data for analysis. Here, inefficient data extraction has always been the main obstacle to realizing this potential, and new forms of cooperation are breaking these obstacles.
For example, collaboration between organizations that conduct decentralized clinical trials and providers of healthcare and diagnostic software solutions can help connect the dots between clinical research and EHR data.
This partnership helps to integrate data from multiple sources under the protection of a single clinical trial, including:
- Pseudo-anonymous metadata insights from EHR
- Consent to the patient’s medical records (including laboratory tests, imaging, scans, and clinician records)
- When these patients are study participants, their RPM data (for example, from wearables and other devices) and ePRO
Innovation partners in the industry are developing a unique method to distribute data queries and run AI models at the “edge” (without moving data to a central repository) to ensure data privacy while enabling joint data access. With the consent of the patient and the provider, these collaborations will also allow the EHR data to be mapped into the clinical trial case report form system used by the research team in order to create a more reliable record for each trial participant. Some partners are also working to go beyond traditional claims data and use machine learning to integrate deeper data sets, such as clinical records, laboratories, images, and biomarkers that are not normally available in existing data sets.
The ultimate benefit of real-world data for sponsors
Using real-world data, sponsors and CROs can improve the quality, control, and cost-effectiveness of the entire research cycle in the following ways:
- Design targeted trials to provide a more comprehensive view of patient populations, cohorts, and disease status
- Accelerate patient recruitment and improve compliance through provider partnerships
- Create a synthetic control arm
- Richer and more effective analysis by identifying correlations and disease progression (for example, studying the link between childhood and adult diabetes)
- Identify new or expanded indications for research products
- Use reliable third-party data to strengthen regulatory files to improve approval prospects
The advantages of including RWD in clinical trials go far beyond any single study. A wider window of patient health may enable researchers and providers to better predict disease onset and provide smarter treatment and care recommendations.
Of course, as with any technological innovation, increasing the use of RWD in clinical trials still presents a major challenge: integrating different and complex data sets that do not have a common language, format, or encoding may require complex programming to ensure data circulation and accuracy Sexuality and minimize deviation. The system must protect absolute data privacy/anonymization or integrate careful consent management, taking into account different regulatory standards across regions (GDPR, HIPAA, PDPA). Then there is the scientific challenge: how to implement the necessary quality controls when creating synthetic weapons to ensure that the results are valid and representative?
Despite these challenges, the industry generally believes that RWD will become an indispensable part of future clinical trials, which may provide more reliable discoveries and increase the return on investment in health innovation. The research community has the responsibility to develop the necessary guardrails, collaborations, and tools to reduce hype and get more results.
Photo: Romolo Tavani, Getty Images
