Analyzing Process Data to Optimize Clinical Trials

process performanceThis is part three in a series describing how Novella Clinical’s Business Optimization department uses process data and analytics to improve delivery for our customers.

Process Performance

 

In our first two posts we discussed how predictive analytics can influence clinical trial design and execution in areas such as risk-based monitoring, country selection, study start-up and enrollment projections, as well as how we evaluate current performance. In part three of the series, we review process performance to better understand how successful we were in delivery to our customers, as well as documenting lessons learned that can be applied to future scenarios.

Delivering our Promise

Customer service and delivery are central to Novella’s corporate mission. As a service provider, it is essential we thoroughly understand each customer’s requirements and meet all contractual obligations.  This drives our internal target setting that must be continuously monitored and evaluated. While precision in our ability to estimate performance is important, we also understand processes are constantly changing, such as new country-specific regulations, technology improvements, or enhanced staff capability.

After completing a project – whether a database lock, a clinical evaluation report or even an electronic trial master file – it is critical for our teams to evaluate the program to better understand any positive or negative performance deviations vs. the target. We do this analysis by answering the questions: How large was the variation to target? What was the root cause of the variation? Was it due to a special cause, such as a one-time outlier, or common cause, something we have noticed occurring across multiple projects? Finally, is it something we can control or influence?

For items out of our direct control, we often reset expectations and establish a new baseline target. For example, if a protocol amendment occurs we may need to re-establish our site activation timeline given an expanded country and patient mix.

Capturing Knowledge for Future Use

As we evaluate future opportunities with our customers, we tap into this historic process knowledge to ensure we establish appropriate targets and set appropriate expectations. Having this detailed information also allows us to continuously update and evaluate our capabilities, improve delivery, and become more precise in being able to predict future performance.

In the final installment, we will explore how we use analytics and process data to target improvement opportunities and continually grow our capabilities.

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