Carving a Path Toward Pragmatic Innovation in Clinical Trials

A pattern quickly emerged after speaking to clinical data executives at events in Basel, New York, London, and Copenhagen. Although thousands of miles apart, the focus on simplifying and standardizing data was clear. This common thread is a result of increasing complexity in the clinical landscape and more companies are adopting pragmatic innovation to streamline study execution.  

The FDA recently issued guidance that encourages practical clinical trials for specific situations. By adding design elements into a study similar to routine clinical practice, more patients (including those from diverse populations) can gain access to participate, enroll, and contribute to clinical research.

Insights from clinical leaders helped surface five trends in the move to pragmatic innovation that will shape the future of clinical data management. 

Prioritize RBQM in your strategy

Although regulatory agencies have been recommending risk-based models for some time, many organizations still seek the security of comprehensive review models and source data verification (SDV). Yet, clinical leaders believe risk-based quality management (RBQM) can deliver value in trials quickly and are taking action to reap the benefits. Some are already adding advanced solutions and upskilling clinical data managers to accelerate the shift from data checks to data science. 

One global biopharma is combining risk-based checks with technology to enable clinical research associates (CRAs) to see SDV requirements without downloading a report or applying macros to a spreadsheet. This can eliminate thousands of patient visits and hours of clinical data work. 

An emerging risk-based approach is using historical trend data for proactive issue management. Data analysis can show trends and how they evolve, and pinpoint how issues are resolved. This requires early input and alignment across functions and teams, and mitigation plans and procedures in place to manage risks. The goal is that when a new trial starts, teams will have access to review and monitor data to pinpoint inconsistencies and share signals. 

Applying risk-based approaches has potential to deliver measurable value to clinical trials. Proactive issue detection can provide higher data quality, centralized data reviews can improve resource efficiency, and faster database lock times can accelerate time to market. 

Shift from data management to data science

The Society for Clinical Data Management (SCDM) noted the need for biopharma companies to embrace a more scientific approach to clinical data, and transition clinical teams from managing data to applying data scientifically. With companies leveraging automation, the data manager role is shifting from collection and cleaning to delivering insights and predicting outcomes. Yet, the move to data science presents challenges, especially the need for clean and harmonized data. 

To enable data science, data management and other functions like clinical operations and pharmacovigilance can work together to streamline data flow. Especially with the ever-growing number of data sources in trials, allowing data managers to prioritize high-value activities to drive data sciences can make a significant impact in productivity.

Although the shift from data management to data science is underway, there is a need to establish clear KPIs and performance targets at the start and end of each study while maintaining the highest quality levels. There are also additional areas of development, including optimizing patient data flow, integrating data quality and review, using AI, ML, and advanced analytics, and enabling digitized and automated analysis, to enable data science. Embracing this shift will require data managers to focus more on analysis and interpretation and less on completing a checklist. 

Go all-in on smart automation

Smart automation seeks the best approach — whether AI, rule-based, or another — to optimize efficiency and manage risk for each use case. Its focus is simply on delivering value, not generating hype.

By taking a rule-based approach to automation, human oversight isn’t required. More companies are investing in automation to add capabilities that deliver benefit quickly while building a foundation. This can include feedback loops and integrating high-velocity APIs for AI use cases that can be applied in the future. Another example is using rule-based automation to speed up data cleaning, transformation, and reporting. The approach helps increase trust in the data and reduces manual work for data managers. 

Today, biopharmas are using automation for data cleaning to speed database lock times. Rule-based automation provides the most significant cost and efficiency gains in the medium term. In the long term, many leaders envision GenAI will be the co-pilot during clinical studies. AI can potentially deliver prompted suggestions, identify fraud, or predict compliance adherence. Establishing a clean data foundation, powered by smart automation, will enhance quality and provide the useful data needed to power AI use cases in the future.

Focus MDR and data standards on what matters

With metadata repository-driven (MDR) solutions, clinical data teams bring together study design, data collection, analysis, and submission. As electronic data capture (EDC) became the principal application used in data collection, the growing perception was that all (or nearly all) data collection metadata should be stored in one system to automate study builds. 

The truth is that gathering data in a repository has proved challenging for organizations scaling metadata management. This is likely because of the reliance on spreadsheets. 

An emerging strategy that has proved more effective is to focus MDR on the things that matter: the study design metadata that are common, shared, and critical to data management and statistics. For example, when evaluating common study design metadata between data collection and data analysis, there could be as little as 25 properties (out of more than 1,000) of EDC metadata that affect downstream programming and analysis. 

Alternatively, the study design can begin with MDR, and during the data collection stage, teams confirm standardized data definitions. This allows data management and stats to work in parallel to deliver the same definition. Shifting the approach away from an all-encompassing MDR toward simplified standards can accelerate the path from study build to database lock. Taking this more pragmatic approach means clinical teams can deliver value faster. 

Make patient optionality a reality 

Only 3% of U.S. physicians and patients participate in clinical trials for new therapies. One outcome of low participation is that almost 80% of studies fail to meet enrollment timelines, causing expensive delays.

The rise of decentralized clinical trials (DCTs) drove discussions and debates around where trials take place, not the impact on the overall trial experience for patients, research sites, regulators, data managers, and so on. The industry is shifting. Instead of concentrating on location, clinical leaders are focusing on patient optionality. An important development since decentralized tools are a standard way of operating where patients decide how they participate in a study — whether at home, a site, or a clinic — to drive timely and efficient research. 

Sponsors are considering a more holistic approach to the trial experience, ensuring patients are not overwhelmed with the number of devices and tools. Establishing clear ‘bring your own device’ (BYOD) policies can deliver convenience while maintaining data quality and security during a trial.

Clinical data leaders are also beginning to alleviate the patient burden by asking study participants for less data. This is established during the protocol design stage. It starts by thinking through the tangible benefits for patients before introducing new applications (for example, eConsent) and leveraging surveys to gain a deeper understanding of the patient experience and identify improvements.

Be pragmatic to simplify and standardize clinical trials

With the growing complexity of clinical trials, life sciences is increasingly applying pragmatic innovation. Adopting a pragmatic approach means nimble clinical teams will move beyond legacy practices without risking quality. To do this effectively, research site engagement will be more bespoke to understand and support their goals for treating patients while making data flow a reality. 

Prioritizing risk-based management, data science, smart automation, standards, and patient optionality are critical for the industry to keep up with market changes. The recent FDA guidance that encourages ‘pragmatic trials’ in specific scenarios is a move in the right direction. Sponsors and CROs can begin by designing elements that closely reflect the standard clinical practice, readying for a future where more patients join and participate in clinical research.

Photo: Deidre Blackman, Getty Images

This post appears through the MedCity Influencers program. Anyone can publish their perspective on business and innovation in healthcare on MedCity News through MedCity Influencers. Click here to find out how.