How do you decide when a patient in a clinical trial has suffered an internal bleed or in another situation a heart attack? The answer to this question has historically been surprisingly elusive.1
Research shows that clinical site investigators often differ significantly in their interpretation of even the most common of clinical events.2 This is especially the case when clinical endpoints are subjective, image-based, or complex to assess. Examples of such endpoints include: cardiovascular events; pain, fatigue and depression; neurological scans; genetic information; incidence of infection or disease; disease severity and progression; and determination of cause of death.3 A number of studies have documented that disagreement between investigators in the interpretation of endpoint data can run as high as 10%.4
The ramifications can be serious: the larger the variation in clinical event data, the more difficult it is to identify treatment effects5. Inconsistent event data can jeopardize drug safety evaluations and significantly delay or even imperil regulatory approval of new drugs and medical devices.6 Delays are costly—each additional month on a trial can cost hundreds of thousands of dollars.7 And keeping clinical trial costs in check is a top priority for most sponsors at a time when political pressure is growing to make healthcare and medicines more affordable and accessible.8
Many may not know that cloud platforms can help streamline the clinical endpoint adjudication. Read below to see how these play a role in cutting costs, improving data quality, smoothing regulatory approval, and speeding time to market.