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The Health Sciences Blog covers the latest trends and advances in life sciences and healthcare.

Four Emerging Trends in Clinical Trial Business Processes

Greg Jones
Enterprise Strategy Architect, HSGBU Development - LS Product Strategy

This piece takes a look at the recent number of high-level industry trends emerging around clinical trial business processes.

Protocol Development and Feasibility with Global Site Selection & Management

As many other industries incorporate the operational side of things into the design side, the life sciences industry, also, is now connecting the protocol development process with site selections. This is a testament to the wisdom of the millions of trials and studies that have been run historically. 

Our clinical trial analytics application offerings (including goBalto Analyze, Clinical Development Analytics, and Oracle Health Sciences Consulting Data Management Workbench Analytics Accelerator) can provide key information to help our customers optimize this approach. This includes understanding historical site performance around study startup activities, enrollment, retention, data quality, query resolution timing, etc.  Recent new developments in this area use real world data (RWD) sources -- including electronic health record (EHR) and claims data -- to optimize the inclusion/exclusion criteria for a protocol. In addition, the focus on patient centricity plays a huge role in incorporating patient and investigator feedback directly into the protocol design process.

Enrollment Planning, Patient Recruitment, and Retention

This area likely won’t completely disappear as an area of focus for continuous improvement.  These days, the incorporation of patient centricity strategies is critical to improvements in this area. This includes the old standbys of either optimized parking and/or Uber rides for trial patients, combined with newer trends around hybrid/virtual trials requiring less in-person patient visits and using remote devices/sensors to collect data passively.  In addition, the above-mentioned RWD sources can also play a role in data-driven planning and recruitment for a trial.  RWD and/or prior therapeutic clinical trial data can also contribute to supporting smaller patient populations for rare disease programs, given recent regulatory acceptance of single arm studies.

Analytics Plus Artificial Intelligence

Descriptive analytics are not much of an industry trend in and of themselves.  The vast majority of the industry is well along the way in understanding the value they can deliver. Instead, the focus is on insuring that the analytics are available for the right person at the right time; and that the labor in delivering analytics can be reduced dramatically over time via automation activities.  So, this  perspective conceives of systems doing more of the heavy work to deliver the analytics in the organization, instead of humans, while constantly fine tuning and optimizing activities.  These automation activities can be served by artificial intelligence (AI), specifically the machine learning discipline.  Enabling algorithms that look at historic elements of the process to deliver analytics, such as mappings between various data formats, are a high value target to improve the cost of deploying new mappings in an analytics environment.

Another substantial addition is the shift underway around capturing less data for the trial via traditional electronic data capture (EDC) systems and processes.  It is now a new multi-data source and data management world; and this new world poses several new data-related questions. 

How do we track all the data sources captured, make sure that the data is clean, and resolve issues in trying to clean it?  How do we know that all the data needed is obtained from each data source for each patient to insure on-time data lock?  How do we combine all these data sources together to convert them into multiple formats for downstream analysis and  medical monitoring, as well as mine them to support risk based monitoring (RBM) processes? And, how do we convert them to study data tabulation model (SDTM) and other industry standard submission artifacts, so that biostats programmers can focus on higher value, efficacy and safety activities?

The basic type of descriptive analytics content serves as an excellent foundation to build advanced-predictive and AI-based analytics content to address the questions above. Therefore, it’s important to understand that the analytics capabilities in place in an organization serve as the de-facto platform facilitating this “basic analytics to advanced analytics” next step; and that the platform’s robustness is a critical factor in determining how quickly these new analytics can be generated.

Outsourcing

Similar to the analytics trend above, a great number of organizations continue to focus on outsourcing. Due to the constant quest for improvements in this critical area and the opportunities available, this arena continues to generate a lot of industry interest.

One key aspect is the planning, budgeting, and forecasting of clinical trials. By using enterprise software with powerful work group and analytics capabilities, instead of Microsoft Excel, an organization can improve these processes on an individual trial and trial portfolio basis.  With an enterprise solution, an organization enables these processes to be formally structured and programmatic, which yields considerable benefits around quality, consistency, and efficiency.

Another significant aspect ties back to the industry-wide focus around patient centricity.  Here, the critical concern is finding a clinical research organization (CRO) partner with service offerings that support and complement the evolving patient centricity strategies that every biopharma organization is developing today.  Important actions here include: understanding the new strategy, finding the CRO that aligns with that strategy in its service offerings, and having the appropriate oversight and accountability approaches in place for effective execution.

These are some of the key trends we’re seeing and the interaction of these trends with the broader, general focus in the industry around patient centricity, AI/machine learning, and RWD/RWE. 

They weave an interesting web of support for the challenges for our customers they make progress in delivering new therapies faster, at better quality, and more efficiently.

Interested in learning more? Contact us for a conversation.

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