Parallelization: imperative to the compression of clinical trial cycle times

Clinical trials are the quintessential project, being both complex and requiring project management skills and techniques to bring them to a successful close. The measure of a project's success—delivering quality results on time and within budget—are key requirements in the highly competitive environment in which pharmaceutical and CROs operate, yet this benchmark is seldom achieved and increasingly further out of reach.

Studies show that at any given time about one-third of all clinical trials are behind schedule. These trials underperform due to operational failures; meaning, they’re behind schedule, over budget, or the data produced lacks the quality needed to support an approval. Perhaps the most disturbing fact is that cycle times associated with starting clinical trials have not changed in more than two decades, in fact they are getting slower, and the subsequent need for study rescue may increase the cost of trials by 20 percent or more. These trends aside, the question becomes, are clinical trials that are operating on schedule, efficient?

Over the past decade the capitalized cost to develop an approved new drug has more than doubled, and although some have labeled this as publicized propaganda from pharmaceutical companies as government funding has continued to increase, one fact that remains indisputable is that study startup — the activities involved at the outset of a clinical trial — remains highly inefficient and prone to bottlenecks and errors. Clinical trials that get off to a good start are more likely to execute well and finish on-time and on-budget – study startup is the Achilles heel of clinical trials.

With unrelenting pressures to rein in budgets and cycle times the application of project management techniques to study startup holds the key to optimizing operational efficiencies and compressing timelines.

Project management techniques to compress timelines

According to the Project Management Institute (PMI) the two most common techniques to compress schedules are fast-tracking and crashing.


In fast-tracking, activities normally performed in sequence are done in parallel for at least a portion of their duration. Also known as parallelization, activities take place simultaneously without affecting the performance of each other. Fast-tracking helps in finishing the given task in a shorter span of time than planned.

In fast-tracking, all of the vital activities on the critical path are reviewed and analyzed to determine which ones can be performed partially or fully in parallel with other activities. Activities not on the critical path, so don’t impact the overall schedule duration, are therefore not analyzed as part of this process.

Project managers typically look at using this technique first as it does not incur additional costs. However, it increases risk as activities are overlapping, but within acceptable limits.


In crashing, activities on the critical path are reviewed in an effort to find ones that can be completed earlier than planned with extra resources. A cost-benefit analysis should be conducted (in clinical trials this is risk management as defined by ICH), allowing project managers to focus on activities providing the highest compression with the least cost. In crashing, activities must be completed before the next activity can start.

Schedule compression techniques infrequently applied to clinical trials

In fairness, it is not the lack of knowledge of these techniques by clinical project managers that is the problem, it’s the inability to spot white space (i.e., the opportunity to see where optimizations in the process can be made), and when to apply these techniques (i.e., optimal timing for intervention).

Today, no visualization of the sequence of activities in global clinical operations for study activation exists, and this is essential for defining not only what metrics to capture but providing clinical project managers with a map of which activities might be candidates for schedule compression. While country-specific activities may be defined, these are not generally applicable to other countries. Recently-released ICH guidelines are renewing the focus on the need to define these activities in order to drive efficiencies in the initiation of clinical trials, and the Metrics Champion Consortium (MCC) is working with industry stakeholders to define and release an associated industry standard. Additionally, activities long considered critical path mainstays (e.g., site contracting) may in fact present white space opportunities.

Moreover, the extensive use of Excel for tracking the progression of clinical trials persists, despite it lacking project management capabilities. This woefully inadequate tracking of operational performance data prevents clinical operation teams from identifying risk factors and bottlenecks that can disrupt cycle times and budgets. Frequently, problems are not identified until milestones are missed, and project managers are forced to retroactively apply the crashing technique in order to get the study back-on-track, as opposed to proactively using this technique to compress the schedule with efficient resource allocations at the outset of the study. The proliferation of CROs now offering rescue study services is testament to the effectiveness of this approach.

Risk mitigation is therefore optimal using systems that can provide timely, preferably real-time data on trial bottlenecks, which indicate red flags to be reviewed and addressed or at least tracked carefully throughout the trial. The power conferred by such real-time intelligence directly impacts the efficiency, cost and reliability of clinical operations.

Ultimately, crashing should be used sparingly as there are significant cost implications and its overuse may indicate a more systemic problem – suboptimal planning. This is why scenario planning is so valuable, and critical to helping manage the variability in clinical trial setup decisions. Scenario planning helps clinical project managers define a series of scenarios that will help reduce errors from the outset and ensure optimal setup of a clinical trial.

In the end, it is the parallelization technique that represents the greatest opportunity to realize efficiency gains that are reproducible across all studies, and not just on an as-needed basis.

Identifying opportunities for parallelization

Visualizations and standardized metrics aid clinical project managers in the identification of white space and machine learning algorithms aid in the timing of inventions (i.e., when to parallelize processes to minimize risks associated with the intervention).

Web-based maps, offering real-time traffic conditions and route planning, provides a fitting analogy. Imagine traveling through multiple countries having to navigate routes with different road rules and signs in foreign languages, while being unaware of potential upcoming traffic bottlenecks or alternative routes, or worst still using paper-based maps and transcribed directions, which might now be obsolete or erroneous. In essence, this is study startup – having to deal with globally dispersed teams and country-specific regulatory guidelines and agencies.

Determining the start-to-finish guided directions, optimal route and estimated arrival time is dependent on utilizing a robust framework that not only guides sponsors and CROs to compliance using workflows consistent with organizational standards and country-specific regulations, but also provides the ability to capture comprehensive, granular, and standardized metrics needed for predictive analytics.

Predictive analytics can guide clinical operations teams in milestone planning, with in-application planning assistance using leading indicators (e.g., sites in study, start month, IRB/EC type, etc.) for associated aspect/milestone estimations. This proactive planning assistance can alert teams to unforeseen issues, which allow for discussions and decisions to be made before studies incur risk due to missed timelines, as well as indicating when parallelization of processes should begin while minimizing risk exposure. Just as fellow drivers help inform navigation apps with real-time feedback on traffic and driving conditions to help ensure your route is always optimized.

Machine learning tools and automaton free clinical project managers from tedious, repetitive activities so they can focus on strategic activities, drive optimal proactive planning in study execution, and aid in-depth internal reviews of organizational processes, resource allocations, study costs and quality assessments.

For example, after a site has been identified as an ideal candidate for a clinical trial and the Confidential Disclosure Agreement (CDA) has been signed, the feasibility survey and other activities must be completed before the site can be formally selected for the study, assuming no red flags are raised. This process takes on average 7.9 weeks (Figure 1) according to research conducted by the Tufts Center for the Study of Drug Development (Tufts CSDD).

Parallelization allows for this step in the selection process to be overlapped with the activation process, if it is assessed that the likelihood of the site being rejected is low (Figure 2). In this scenario, the Study Package can be sent to the site as soon as the CDA is signed, resulting in an overall compression of the schedule from 36.4 weeks to 30.4 weeks.

This simple example underscores the opportunities conveyed by real-time operational insights and powerful predictive capabilities that can proactively guide clinical operation teams in the setup and execution of clinical studies. Moreover, these capabilities provide the foundation for the identification of white space and process optimizations that can be applied across the clinical trial continuum, transforming a process that has historically been sequential in nature (Figure 3) to one in which parallelization becomes the norm (Figure 4.)

Driving incremental but not dramatic change may explain why organizations, especially large, complex pharmaceutical companies operating in a highly-regulated environment, are slow to adopt truly innovative technology solutions with the potential to drive down costs, reduce cycle times, and ultimately get new drugs to market faster. Organizations often look for ways to streamline current workflows rather than fundamentally disrupt them.

This mindset is being challenged by forward-thinking organizations with support from key decision makers who are embracing a unified eClinical platform approach. This technology makes it possible to move the needle in a dramatic way on process changes by breaking down organizational silos, by developing more robust and extensible predictive capabilities, leading to further reductions in cycle times, greater site engagement in studies, better adherence to study budgets, and audit readiness.

Learn more about how your organization can benefit from real-time operational insights in starting clinical trials.

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