JMIR Publications

ABSTRACT

Background:

Digital technologies are transforming the healthcare system. A large part of the digital information generated is data collected in uncontrolled settings, so-called real-world data (RWD). Data from electronic health records and digital biomarkers have the potential to reveal associations between the benefits and adverse events of medicines, establish new patient stratification principles, expose unknown disease correlations, and inform on preventive measures. The impact for healthcare payors and providers, the biopharmaceutical industry and governments, is massive in terms of health outcomes, quality of care and cost.

Objective:

However, a framework to assess the integrity and quality of RWD is missing, thus hindering the conduct of population-based observational studies to support regulatory decision-making and real-world evidence (RWE). We aim to build a framework that assesses the quality of RWD.

Methods:

We built the RWD-Cockpit web application (http://rwd.aihealth.ch/) which systematically scores datasets based on standard quality metrics and customizable variables chosen by the user.

Results:

Sleep real-world data were generated and used to validate the usability and applicability of the RWD cockpit web application.

Conclusions:

The output scores, Quality Identifiers (QI), provide the first quality assessment and validation for the use of RWD in regulated settings.