The introduction of electronic data capture (EDC) systems in clinical trial data management and analysis triggered the promise of better-managed clinical trials – with greater efficiency, faster access to higher-quality clinical data and better mapping of data flows to business processes. Over time, however, as many organizations transitioned from paper-based to electronic systems, they ended up with multiple EDC systems, legacy clinical data management systems and additional systems for managing different aspects of clinical data management and analysis. As the result of years of dealing with disparate systems, many pharmaceutical organizations are unable to execute timely queries against historical or ongoing clinical trials – or even retrieve details about one particular trial beyond generating some standard reports. The cost and effort to update, maintain and keep these numerous systems in a validated state is prohibitive for many organizations. In addition, some organizations have neglected to dedicate enough detail to robust integration routines that bring diverse sources of clinical data together repeatedly for different trials or therapeutic areas.
Today, the outcome of this electronic Rube Goldberg machine is a set of large libraries of ad hoc integration code that is unusable beyond the specific trial for which it was initially produced. Increasing regulatory scrutiny, combined with pressure from public opinion and patient advocacy groups, is putting pressure on pharmaceutical sponsors to demonstrate the safety and efficacy of new compounds both before approval and long after a compound has been released to the market.
In order to do so, however, pharmaceutical companies will need to do more than make incremental changes to their business processes or information technology environments. Conducting clinical trials with the right balance of time, costs, quality and processes, and ensuring that clinical trials are supported by the right technology environment, will require a revolutionary change – one that replaces silo-based systems with end-to-end clinical data management.