data visualization

In preparation for this year’s International Clinical Trials Day we have been talking a lot about what will drive the trial of the future. Our consensus:  optimized performance will be achieved by those who make best use of data to support faster more evidence-based decision-making. This data-driven future may enable trial leaders to do more proactive risk-based monitoring which will drive efficiencies while still delivering the same standards of quality or safety.

New models, new insights

Pursuing a Data-driven Trial Execution (DTE) approach to risk-based monitoring (RBM) can alleviate some of the cost and risk associated with trial operations and enable a more proactive approach to risk management across the trial lifecycle. RBM approaches are fueling the evolution of clinical development, allowing sponsors to cut study costs, while improving data quality and safety. The Food and Drug Administration (FDA) and European Medicines Agency (EMA) have both provided guidance highlighting elements of an effective RBM system, including the value of conducting risk assessment, identifying key data points, developing a monitoring plan with multiple considerations, appropriate use of centralized monitoring, and leveraging new technology capabilities.

However, many organizations remain focused on more traditional approaches clinical trial monitoring, which can lack the visibility, access to data, and analytics tools necessary to support timely decisions about trial performance and risk management.  As result they may be wasting resources, and missing opportunities to maximize the value of the data they collect as part of their start-up, project management, clinical monitoring, data management and analytics processes and to optimize trial conduct while meeting regulatory demands and quality requirements for Good Clinical Practice.

The DTE model combines analytics technology with enhanced data review processes so that trial leaders can make faster and better assessments about what’s happening across all sites and what is likely to happen given emerging risk indicators. For example, the analytics can show which sites are missing enrollment targets, have high dropout rates, or whether there is a high rate of protocol deviation. In this way, sponsors can be sure all activities associated with the trial at each site are happening efficiently, and the system can pre-emptively identify potential patient safety and operational performance issues to mitigate trial risk before it becomes detrimental to trial outcomes.

This insight and improved accuracy streamlines the trial process, and ensures sponsors deploy their resources more effectively, which can ultimately help reduce the cost of trial execution. Our studies show this data-driven approach to RBM can lead to as much as a 25% cost reduction over traditional trial execution approaches thanks to improved productivity and resource allocation.

Companies that implement an integrated data driven approach can also harness results of past trials, including which sites performed well and which ones fell short, to inform future site selection processes, and to identify key risks that are likely to occur in certain geographies, patient populations, or site networks. They may also integrate external sources of data into these analyses, including electronic health records, patient registry data, wearable health monitors, and even social media conversations that are pertinent to the trial experience and the patient population. Such external data are becoming increasingly valuable in defining and demonstrating patient outcomes, which are an particularly important part of the value equation for regulators, payer, physicians and patients.

How to be data driven

This transition to a more data driven approach that integrates multiple data streams and complex algorithms won’t be easy. As an industry we need to embrace both the technology and the culture change necessary to make analytics a key component of the trial operation process. But as we develop new tools to integrate isolated data sets and better algorithms and statistical expertise to help us interpret that data, we continue to strive to achieve the goal of building better, faster and more efficient trials that achieve the highest level of quality and safety so we can get therapies to patients as quickly as possible.