Clock Speed Optimization for Clinical Trials | MaxisIT
Move from clinical trial data review to regulatory submission faster by adopting a clock speed approach. Here’s how:
Clock Speed Optimization for Clinical Trials
An Agile Method to Accelerate Clinical Data Analysis and Reporting
Digital technology and artificial intelligence alone won’t improve data management cycle times. To move from clinical data review to regulatory submission faster, biopharma companies and CROs must combine advanced technology with process improvements. When they do, results can be dramatic.
A growing number of clinical data management, as well as analysis and reporting (A&R) teams are turning to clock speed optimization to achieve dramatic, continuous cycle time improvements. It does so not by actually watching the clock, but by retooling processes to enable teams to respond to both expected and unexpected events more easily. Clock speed promotes quick decision-making, resulting in the faster completion of a wide range of tasks.
Clock speed optimization not only helps streamline data management and A&R, but may also prove useful for clinical supply chain, clinical operations, and other functions within clinical research.
Here, we’ll focus on data, and how you can ensure quicker delivery to regulators with a clock speed approach.
What Do We Mean by Clock Speed?
Clock speed typically refers to the operating speed of a computer or microprocessor. In this instance, clock speed measures the number of cycles a CPU executes per second. The more cycles per second, the faster the processor.
Clock speed is also an approach used in agile methodology, which is a project management framework commonly used in software development and manufacturing. In these scenarios, clock speed means speed of decision-making.
Teams focused on clock speed develop processes to generate consistent performance improvements. A clock speed approach works well for small teams, provided they are empowered to make quick decisions.
Data management and A&R teams can also take a clock speed approach to improve decision-making and performance. The clock starts from the moment data is received by data management, through biostat programming, and on to final submission.
Why Focus on Clock Speed?
Every day of delay leads to millions in lost revenue for a clinical trial sponsor. The difference between first- and third-to-market can be striking. Here’s an example: Moderna and Novavax both have effective COVID-19 vaccines on the market. Moderna received Emergency Use Authorization (EUA) from the FDA for its vaccine in December 2020. It earned $18.5 billion in revenue from that product in 2021 and $19.3 billion in 2022.
Novavax, which received an EUA in July 2022 for its COVID-19 vaccine, reported $1.1 billion in total 2021 revenue and $2 billion in 2022. Most of the western world knows Moderna and Pfizer. Novavax is relatively unheard of.
By getting its product approved six months ahead of Novavax—and only one week after Pfizer-BioNTech—Moderna earned $17.4 billion more in revenue.
How to Cut Cycle Times by 50%
Years before COVID-19, Pfizer implemented a lean approach to resolve delays when working with academic medical centers. The organization used value-stream mapping to identify three potential causes for delay. It then implemented three potential methods for improvement, which included:
• Use of new agreements instead of reusing those previously negotiated for increased flexibility
• A plan to enable Pfizer to understand and respond to CRO concerns faster
• A plan to enable CROs to escalate issues to Pfizer more efficiently
After implementing these changes, cycle times for working with academic medical centers dropped by 50%. This resulted in faster site activation and trial performance on par with non-academic sites with shorter contract cycle times.
How Clock Speed Optimization Works
Information in clinical trials tends to move linearly. Tasks are completed sequentially. The clock speed of task C depends on the speed of tasks A and B. When A and/or B operate slower than expected, it puts task C on standby. This approach can lead to inefficiencies and delays in the overall process, negatively affecting the speed at which data is processed and analyzed.
With a focus on clock speed optimization, each unit continually strives for constant improvement through iterative processing. Tasks are completed in parallel. By working in a collaborative manner, teams adapt and optimize processes based on real-time feedback.
https://www.maxisit.com/wp-content/uploads/2024/02/MaxisIT-Value-Stream-Mapping.pdf
Clock speed optimization involves value-stream mapping, another process used in lean management. VSM uses a flowchart to map every value-driving step in a process and how the information in those steps flows. With every value-stream mapped, teams can identify areas of waste and inefficiency.
The Clock Speed Formula
Clock speed can be measured using this formula:
Clock Speed = (Number of Units Processed) / (Total Time Taken)
Number of Units Processed = (Complexity / Concurrency) x (Experience / Resources)
Total Time Taken = (Duration / Variability) x (Technology / Standardization)
Complexity
Concurrency
Clock Speed = X X
Duration
Variability
Experience Resources
Technology
Standardization
To improve the clock speed and cycle time of an entire process, each unit should process output from the previous unit while continuing to improve as they finish. Work gets done faster because resources are used to their fullest potential.
Biostatistics Cycle Time DataDataReceiptby OperationsDataNormalizationDMManualReview Clinical ReviewSDTMDevelopment ADaMDevelopment Submission TLGDevelopmentReportingCSR/Milestone Event DataDeliverytoBiostatsDataReceiptbyBiostats
Clock speed improvements in each area of biostatistics (sections drawn in blue) lead to cycle time improvements and a shorter time from clinical review to regulatory submission.
Benefits of a Clock Speed Approach for Clinical Trials
Clinical trials have operated using a linear approach for decades. To align with today’s complex drug development needs, a more flexible approach is required. Agile methodologies facilitate flexibility and collaboration. When paired with advanced cloud-based technology, clinical teams can realize the following improvements:
Shorter development cycles
By adopting agile methods such as clock speed optimization, clinical teams can realize shorter innovation cycles from months to weeks, or even days. Improvements include faster rollout of new data management technology, faster study builds, and faster A&R.
Increased responsiveness
Continuous incremental improvements
Agile focuses on iterative development in short cycles rather than locking down requirements early and having long development cycles. Ongoing improvement enabled by short cycles and faster clock speed is valuable for enhancing data management capabilities. This process helps improve efficiency by focusing on the critical. It also helps mitigate risk because issues are identified and addressed sooner than they would be via a long cycle.
Improved collaboration
The clock speed approach encourages cross-functional teams to work collaboratively for a more successful result. Endless email communications would be reduced, and the sharing of knowledge among professionals would elevate the entire group.
Managers must empower teams to be self-organized and collaborative rather than hierarchical and micromanaged. Thinking more broadly, when teams work in parallel, idle time is reduced, which leads to faster data processing and analysis.
Complex clinical trials often involve mid-study changes in dosing regimen and other factors. Agile methodologies align with the flexible nature of today’s clinical trials. Focusing on clock speed allows teams to adapt quickly to protocol changes. 6. https://www.emerald.com/insight/content/doi/10.1108/JOSM-11-2020-0422/full/html
https://www.agilealliance.org/characteristics-of-agile-organizations
https://www.worldpharmatoday.com/articles/transforming-the-clinical-data-management-process-with-agile
6. Clock Speed
Statistical Computing Environment: Your Clock Speed Accelerator
MaxisIT’s Statistical Computing Environment (SCE) compliments agile clock-speed focused work environments. Cloud-based with powerful analytics and machine learning-based tools, SCE is designed for flexibility and collaboration. The platform supports most programming languages and includes over 40 data source integrations, enabling programmers and biostatisticians to develop new software without added friction.
SCE also includes project management tools based on agile methodologies for improved traceability and quality. Iterative, collaborative project cycles can be executed and reviewed via relevant metrics.
Conclusion
Pharma and biopharma companies and CROs face ongoing pressure to develop more complex therapies at a quicker pace while meeting stricter regulatory requirements. Advanced technology can help meet these objectives. When combined with agile methodologies such as clock speed optimization, the results can move from subtle to dramatic.
About MaxisIT
MaxisIT’s purpose-fit and intelligent clinical data analytics platform helps improve clinical trial performance, mitigate risk, and optimize clinical outcomes. We provide a centralized and reliable source of truth for diverse data types from various sources, giving life sciences companies real-time insight to shorten cycle time and increase return on investment.
Incorporating an end-to-end clinical data pipeline from intake to visualization, MaxisIT's solutions are powered by AI/ML and metadata-centric approaches. Our impressive portfolio of over 3,300 clinical trials and an unparalleled 100% customer retention rate affirm the quality and reliability of our services.
Moulik Shah Founder & CEO, MaxisIT
Moulik Shah is a passionate healthcare technology entrepreneur and the visionary CEO of MaxisIT, where he has been at the forefront of leveraging technology to transform pharmaceutical and life sciences clinical trials.
His dedication to improving patient outcomes and his leadership in directing patient-centricity, patient diversity, interoperability, and real-world-data-led collaborations have been at the core of his vision of an integrated healthcare ecosystem based on effective use of data and analytics platforms.
He has been instrumental in driving innovation and progress in the industry. Under Moulik’s leadership, MaxisIT has become a leading provider of clinical data and analytics which is driving real-world impact in the pharmaceutical and life sciences clinical trials.
https://www.maxisit.com/