By Linda Drumright  

December 28, 2012 | Guest Commentary | Clinical trials (for the most part) are run by talented study managers who rely on experience, anecdotal knowledge, and "gut" instinct to plan and execute them.  While these study managers get trials over the finish line as close as possible to plan, it’s reckless and increasingly difficult to take a cottage-industry approach to managing these multi-million dollar projects.  As trials become more complex, expand globally and extend into new markets, even the most experienced and talented study managers struggle, resulting in highly unpredictable trial costs and timelines across the portfolio.  It’s time to take a fresh approach; it’s time to “work smarter” to enable organizations to predictably complete clinical trials by using real-world data from across the industry to help understand exactly how our current clinical trial processes work, and applying this information to predict the future. By leveraging data and predictive analytics, fueled by technology, to make smarter decisions at strategic points along the clinical trial decision processes, we can dramatically improve the likelihood of success.  

Other industries have made dramatic productivity improvements by applying continuous improvement principles.  They have done this by benchmarking their processes and systematically improving upon them, first by making them repeatable, then by looking for opportunities and squeezing out the white space in between.  In the clinical trial industry, our business processes are long-lived— taking months or years to complete—but they are repeatable processes nevertheless. They can be made significantly more predictable if we utilize a consistent set of business practices that leverage relevant data and predictive insights to validate a more realistic set of planning assumptions at the start. Measure ourselves against plan and then rinse and repeat until we become more predictable. Once that’s been accomplished, we can discover the ideal ways to optimize those processes, monitor and track our performance "in flight", and make additional adjustments along the way.  

This approach is not novel, and many smart people in the pharmaceutical industry are working to achieve these goals, but finding real-world performance data that is relevant and granular, and that has sufficient coverage across the different clinical trial business processes and geographies, can be a struggle.  As a result, we're seeing an increasing number of data and benchmarking providers enter the industry, as well as a number of industry consortia coming together to pool their data. One example is the recently formed Transcelerate BioPharma, a consortium of 10 of the world’s largest pharmaceutical companies who are cooperating on research aimed at accelerating drug development, starting with streamlining clinical trials.  

With real-world performance data in hand, clinical trial organizations can plan more realistically, and execute more accurately. Companies are looking for ways to easily answer such questions as: 

• Is this protocol feasible? Where are patients that meet our I/E criteria? 
• Which criteria are rate limiting to our enrollment goals? 
• Which sites and countries will deliver patients on time and on budget? Which ones should we avoid? 
• How long will it take to enroll one patient? How long to enroll all the patients? 
• How much should we compensate investigators for a particular study in a particular region in a particular phase?  What is the cost of each procedure?  What is the standard of care? 

How to Get Started 

It all comes back to planning. This is where the value of data has its greatest leverage. By using real-world data at the planning stage of clinical trials, we can ensure that our basic assumptions about trial execution are realistic and valid, leading to more predictable studies. Key steps in achieving this include: 

1. Analyze and standardize your planning process. In order to apply a data-driven approach to study planning, you need a consistent, repeatable process.  
2. Apply data to the planning process. Your consistent repeatable process must systematically bring in real-world evidence to support key decision points within the decision process.  
3. Track study performance versus the plan, and adjust when needed. Things happen and not every study will proceed according to plan. But having a detailed, data driven plan in hand, with actual to compare to, variance from the plan can be flagged and corrective actions taken. 
4. Close the loop. Keep track of how you performed against plan across the portfolio and review what you could have done better to achieve stronger outcomes. The value of this approach becomes more significant over time as you are able to analyze the performance of your studies, and systematically refine how you apply real-world data and other approaches to improve the results. 

Every one of these steps requires significant effort from your clinical trials organization. The steps defined above require organizational commitment to evolving your clinical trial processes, IT infrastructure that allows you to put the data into the hands of your key decision-makers at the point in time they need it, and assumes the availability of numerous cross-industry data sources.  

Is the Effort Worth It? 

Is that effort worth it?  Absolutely.  By working with customers to measure and optimize their enrollment processes, clinical trial management vendors can drive dramatic improvements in performance and predictability, including a sharp increase in studies recruiting to plan, a significant reduction in non-performing sites, and a reduction in the average time to enroll patients. Making patient enrollment more predictable can save companies millions of dollars. And this is only one of the business processes in a clinical trial. By applying technology-enabled real-world data to other business processes as well, the industry can make significant improvements in the performance and predictability of clinical trials. Make 2013 the year in which your organization “works smarter” …and takes significant strides toward solving the problem.    

Linda Drumright is General Manager of the Clinical Trial Optimization Solutions business unit of IMS Health, a global provider of data, services and technology for the healthcare industry around the world.