Treating The Causes, Not Just The Symptoms: Using Central Analytics To Determine Errors

By Gareth Adams, Senior Director, Central Analytics at PRA Health Sciences

When a patient enters an emergency department, a team of medical professionals immediately begins treatment based on the signs and symptoms while simultaneously working to identify any underlying conditions. When every lost second impacts a patient’s survival, it is imperative to examine all of the available data and diagnose the underlying cause as quickly as possible.

Some of the same urgency and methodology applies when identifying mistakes in clinical trials. Instead of treating the signs and symptoms (data errors), we need to identify and treat the causes (origins of errors), which is no small task. In the past several years, we have seen the data generated by trials grow exponentially. At the same time, aided by advances in technology that allow us to collect substantial amounts of data, clinical trials are vastly more complex to plan and conduct, making the balance between quality and cost much more difficult.

The significant number of clinical trials occurring at any given time around the world further complicates matters by requiring CROs to cast wider nets for patients. Nearly 180,000 studies in 187 countries were in 1 motion as of 4 December 2014, according to the Food and Drug Administration’s (FDA’s) ClinicalTrials.gov Website. About half of these trials are are being fully or partially conducted in the United States, with studies in progress throughout all 50 states. Even though the drug development model has not changed much in over 50 years, CROs need to modernize their data retrieval and usage methods to simultaneously keep quality up and costs down.

Addressing Trial Complexity...