Rohit Nambisan

Due to more complex protocols and increasing data sources, clinical trials are generating triple the amount of data than the previous decade, with an anticipated rise over the next few years.

 This influx of study data isn’t necessarily a drawback; on the contrary, it is a great achievement for the clinical research industry, assuming the data is being leveraged by study sponsors and CROs to be proactive in their clinical trial development and operations. However, the rapid increase in data and disparate data sources has left the industry rather stunned. How do we accurately and efficiently manage this influx of critical study data?

In addition to the volume and diversity of study data, the quality is also of concern. In a study conducted by Harvard Business Review, 97% of critical data used in business decisions contains errors or is incomplete.

 Another study found that most executives are not confident enough in the accuracy of their data and analytics to take data-driven actions.

 More troubling, the United States, alone, wastes approximately $3.1 trillion on poor-quality data.

 Poor data quality equates to more than just monetary loss; for researchers it means critical time lost due to the need for manual rectification, all while attempting to stay on target with strict deadlines. Additionally, it can lead to insufficient data to assess the endpoints, resulting in delayed or complete absence of submission. But who pays the ultimate price for the increased cost and time spent? The patients relying on life-saving therapies, devices, and vaccines.

Lokavant and Halloran Consulting Group conducted a poll with leading biotech executives in clinical development to understand the state of clinical trial data quality, contributing factors, and how sponsors are rising to the challenges of data management. While data quality and management issues are certainly prevalent today, managing studies will only become more difficult in the future—68% of biotech executives said that they expect to spend more time on study oversight due to growing data complexity (see Figure 1 below).

 Lokavant and Halloran Consulting Group, 2022

While the clinical research industry spends a tremendous amount of effort ensuring the collection of high-quality clinical data, the same cannot be said for the operational data in a study. With a diverse set of tools and processes for data collection and analysis comes an abundance of imprecision. What causes this level of inaccuracy in operational data?

 The clinical research industry is filled with numerous data capture systems, unique processes and definitions, resulting in data management chaos. The same data element can vary between different studies, systems, and teams on the same study. The industry tends to overlook data governance, which involves alignment on data definitions to ensure high-quality operational data and compliance throughout the lifecycle of the study.

For example, "study start” is a term used by many organizations but has varying definitions. Some organizations may define this as the date when a patient is first screened in a study; others may define this as the date when the first site is activated. The same is true for many other study milestones, both clinical and non-clinical.

: Many clinical trial managers have not had the statistical training to manage the operational and clinical data influx. They are typically tasked with understanding the nuances of the data but lack critical skills required to interpret and analyze the data at scale.

With the rise of artificial intelligence (AI) in clinical operations, highly sophisticated algorithms are used to detect highly nuanced signals in large sets of study data. However, these technological advances are only valuable if they can be interpreted by the study operator within the appropriate context. In this nascent but rapidly developing field, the skillsets required to interpret these complex AI algorithms are often lacking, and clinical teams typically do not have the resources to keep up with the latest developments in AI.

Most biotech executives continue to heavily rely on dashboards and spreadsheets to review study oversight metrics (see Figure 2 below). While these tools offer many advantages,problems occur when spreadsheets and dashboards are used as business intelligence tools by many concurrent users. This can lead to siloes where the data are processed, analyzed, and altered outside a single source, creating multiple versions of truth.

If the available tools to aggregate and standardize data are not utilized, processes will take significantly more time to manage.Eliminating manual processes with a clinical intelligence platform will significantly reduce the exerted effort and improve data quality by maintaining a single space for users to easily access and analyze data.

The need for integration and automation is particularly crucial when data are being generated by many unique sources, a commonality across the clinical research industry. For example, hybrid or decentralized clinical trials (DCT) may generate data from remote monitoring technologies, patient self-reporting, site-based data collection, and many other sources. With numerous data sources enters the risk of multiple versions of the same data points. The ability to access study data from a single platform will reduce the need for significant reconciliation and allow for speedier, improved decision-making.

There is a cultural tendency in drug development to make decisions based on hearsay rather than data. For example, one myth that has circulated throughout the industry is that 20% of clinical trial sites will fail to enroll a single patient. This narrative has been widely accepted and relied upon to make critical decisions in study enrollment predictions. However, Tufts Center for the Study of Drug Development compiled an analysis of multiple clinical studies and concluded that only 11% of sites fail to enroll a single patient.

 Furthermore, this percentage can vary based on the therapeutic area or even the location of the study, providing more emphasis on leveraging contextually relevant data to drive critical study decisions.

Many organizations have the relevant historical study data and outcomes to forecast future performance more precisely. However, the effort required to systemically capture these insights is often too onerous, causing teams to default to heuristics, industry norms, or personal experience.

While culture and leadership are key enablers to better decision making, issues related to accessibility, skillsets, and quality are the primary roadblocks for biotech executives (see Figure 3 below). By realizing operational data problems exist, organizations can implement better practices to make improvements.

For most organizations, the root cause of these data issues will be a combination of poorly defined standards, a lack of integrated systems, and a skills gap with respect to advanced analytics. While organizations often look to solve each of these drivers separately, the most effective solution tends to be an integrated approach that addresses these drivers simultaneously.

Whether in-house or through technology partners, operators in clinical studies should adopt master data management principles in parallel to deploying an integrated platform that sets those principles into practice. This requires a data layer that allows study teams to continue their use of disparate systems, but harmonizes data across these systems, studies, and processes. While this represents an initial investment, it is more cost-effective to be proactive in generating high-quality, precise data than to later identify inaccuracies for resolution. Creatinga single source of truth will have an immediate impact of reducing reconciliation effort while ensuring better data quality.

Additionally, this high-quality data infrastructure allows organizations to unlock the value of more advanced data analytics, like artificial intelligence, to further augment data quality in increasingly large, complex clinical trial datasets. These analytics can support a wide-range of processes, from early risk detection and mitigation to risk-based quality management, to medical monitoring, to more effective study planning and forecasting—all of which lead to better data quality and improved study outcomes.

Since study teams may not have the expertise to interpret the output of these sophisticated algorithms, sponsors and CROs should consider platforms that are both intelligent and intuitive, allowing study teams to act within their familiar workflows rather than learning new statistical skillsets on a regular basis.

The challenges of data management and quality will continue to soar if the industry remains complacent. Ensuring high quality data in clinical research is paramount; the consequences of ill-informed decisions not only add to the ballooning costs in healthcare but could delay or preclude life-saving therapies to those who need them most.

This is the second in a series of articles where we explore how companies are harnessing and leveraging operational and clinical data to maintain quality and choice. Stay tuned for additional articles with useful insights on data-driven approaches in risk management, vendor oversight and portfolio management.

, Vice President, Customer Success, Lokavant, Inc.,

, Director, Sales and Marketing, Lokavant, Inc., and 

https://www.globenewswire.com/news-release/2021/01/12/2157143/0/en/Rising-Protocol-Design-Complexity-Is-Driving-Rapid-Growth-in-Clinical-Trial-Data-Volume-According-to-Tufts-Center-for-the-Study-of-Drug-Development.html

https://www.businesswire.com/news/home/20171107005745/en/Industry-Research-Shows-97-of-Companies-to-Increase-Use-of-Real-world-Patient-Data-for-More-Accurate-Decision-making

https://hbr.org/2017/09/only-3-of-companies-data-meets-basic-quality-standards

https://www.cio.com/article/236500/executives-still-mistrust-insights-from-data-and-analytics.html

https://hbr.org/2016/09/bad-data-costs-the-u-s-3-trillion-per-year

https://www.globenewswire.com/news-release/2013/01/15/1187761/0/en/New-Research-From-Tufts-Center-for-the-Study-of-Drug-Development-Characterizes-Effectiveness-and-Variability-of-Patient-Recruitment-and-Retention-Practices.html

Articles

Poll taken by leading biotech executives in clinical development addresses state of data quality and management.

The Future of Clinical Trials: Turning Data Chaos into Trial Intelligence

The evolution of means for measuring clinical trial quality cannot be categorized as divergent. Until relatively recently, trials have been managed by limiting the options for researchers and patients, effectively applying stringent guard rails to reduce variability in tools, methods, and approaches. Beyond the advent of ePRO and eCOA technology capturing data directly from the participant, most of the work has been at the hands of sites steering trials via rigid protocols. The status quo of controlling quality at sites provided a streamlined approach because it allowed for methods such as regular visits and 100% source data verification to impose an acceptable threshold for quality. With the rise of decentralized approaches and a changing regulatory landscape, this framework is set to change for the better.

The industry is shifting from a paradigm of designing studies by narrowing optionality to a framework of leveraging choice to promote flexible participation in trials. Clinical trials are now being structured to ensure accessibility for patients and productivity for researchers. The pandemic has only served to highlight the limitations of the status quo and accelerate the adoption of next-generation digital innovations; over 90% of executives at a recent McKinsey Clinical Operations round table expect virtual trials to become a major component of their portfolio, up from 38% in 2019.

Trials are increasingly scoped with technology to meet patients where they want to be, ultimately driving equity and efficiency in clinical research.These technologies—from wearables for endpoint collection to telemedicine visits to full-blown virtual metasites—generate an abundance of data faster than ever before, providing evidence for the next wave of adaptive trials and decentralized approaches.But as trials rapidly evolve, it is crucial that quality is not compromised.

Reimagining study management to preserve quality and choice

While we now have the technology, process, and regulatory momentum to create optionality for researchers and participants, parallel innovations are needed for the operators managing these studies.Maintaining safety and measuring efficacy are equally important and can be more complex in a study that offers choice to participants.Naturally, optionality may create more risks to quality, so we must adapt the way we manage trials accordingly.Study management must be more flexible, more targeted, and more-data-driven.

Thankfully, some of the regulatory frameworks and processes are already in place.ICH Good Clinical Practice guidelines have laid out frameworks such as Risk-Based Monitoring

to allow for more targeted oversight of trials.The FDA even recently released a request to flag “remote” and “onsite” datasets in oncology “to foster use of ‘decentralized’ aspects of clinical trials prospectively in the post-COVID era.”

 Other industry organizations such as Metrics Champion Consortium (MCC) or the newly formed Decentralized Trial Research Alliance (DTRA) have ongoing efforts to operationalize this guidance through common metrics and best practices.

On the technology side, remote monitoring solutions were introduced to support trial operation teams. These solutions—often comprised of dashboards and workflow to communicate virtually with sites—assisted in moving away from rigid protocols focused on in-person site visits and 100% source data verification.While uptake was relatively slow through 2019, the pandemic and resultant lockdowns have been a powerful accelerant.However, with the volume, velocity, and complexity of data associated with next-generation trials, maintaining quality today requires more than incremental point solutions.Instead, teams need powerful tools and analytics that keep up with the pace and complexity of novel trials. Clinical operations must be empowered to see their study health in real-time and receive actionable, data-driven recommendations on how to proactivelymaintain quality.

Data-centricity as the key to quality by choice

Enter the increasing need for a data strategy, technologies and related data competencies. By providing the right data to study operators at the right time, they can effectively ensure quality in any next-generation trial.There are key strategies that all study sponsors and their research partners must address today to future-proofstudy management. Some of the most impactful we have seen in our experience supporting industry innovation include:

Flexible data collection, but standardized data analysis

: The average clinical trial already uses six different data sources, and that number is only expected to grow.

 Novel data sources will continue to emerge and should be embraced to match the specific needs of patients and researchers in any given trial.However, for many study teams, taking in data from so many new, different source systems requires arduous, manual reconciliation that distracts from their core focus on mitigating quality issues. Instead of this manual process, or restricting choice by limiting which systems they use, Sponsors should consider creating a single source of truth—a data platform that captures and harmonizes key datapoints and metrics in real time.This platform should surface analysis and insights to the study team in an intuitive way regardless of from the source of these data.Ultimately, this approach allows for comparison across studies, and lets the study team focus on 

 the data are saying vs. where they are coming from.

Moving from reactive to proactive study management:

 In next-generation trials, it is no longer sufficient to look only at what happened in the past days, weeks, or months in your trial. The cadence of these trials and data captured is often too fast; study managers need to be ready for what quality issues will emerge 

.Part of the solution is leveraging historical data in a systematic way to better predict future outcomes.However, as mentioned earlier, the pace of data generated by new, more patient-centric trials is growing exponentially, and so relying on historical data alone will leave study teams making decisions on dated information. While study teams should leverage historical data to inform choices from design to execution, the right solution will combine this information with incoming real-time data from their studies. A complete solution will translate these data sources to action, using powerful analytics to suggest improvements to study design and execution that eliminate current and future quality issues.

Deep partnership between clinical operations and data and analytics experts: 

The strategies mentioned above are fundamental shifts in how many organizations think about managing studies—they are essential, but not easy. We have seen organizations have the most success and rapid impact when clinical and data experts partner closely from the very start. During scoping and change management, clinical teams provide critical inputs on the most important use cases and metrics to track or predict while technology, data science and analytics teams explore what is possible, often supplemented by specialize experts like Lokavant. In implementation, this partnership allows the data team to focus on implementing key technology improvements while clinical operators change relevant processes, and ultimately focus on high value work. Post-implementation, these teams establish a strong feedback loop for continuous improvement, periodically surfacing, evaluating, and implementing new technologies and trial structures. This data framework not only positively impacts clinical and development teams, but the benefits are also further realized at the site and patient level. Data exchange fuels decentralized trials and will continue to play an important role in trial flexibility, providing further options for patient participation.

So, what can your organization do now to prepare for an increasing patient- and data-centric future? This is the first in a series of articles where we explore how companies are harnessing and leveraging operational and patient data to maintain quality and

choice. Whether it is for risk management, vendor oversight, or portfolio management, each article will introduce a problem, present a use case and provide practical and useful approaches that stakeholders can use to help them on their data journey.

, Advisor and Founder, Clinical Innovation Partners, 

https://www.mckinsey.com/industries/life-sciences/our-insights/no-place-like-home-stepping-up-the-decentralization-of-clinical-trials

https://www.fda.gov/files/drugs/published/E6%28R2%29-Good-Clinical-Practice--Integrated-Addendum-to-ICH-E6%28R1%29.pdf

https://www.fda.gov/about-fda/oncology-center-excellence/advancing-oncology-decentralized-trials

https://www.contractpharma.com/contents/view_online-exclusives/2017-11-21/volume-and-diversity-of-clinical-trial-data-sources-expected-to-soar/

Rohit Nambisan

Articles

The Future of Clinical Trials: Turning Data Chaos into Trial Intelligence

From Quality by Constraint to Quality and Choice