How to overcome the four common data challenges in clinical diagnostics

Today clinical diagnostic labs are at a crossroad. You face unprecedented demand for availability, flexibility, and scalability. At the same time, you need to maintain the quality of your services and keep your operations affordable.

In the era of next-generation sequencing (NGS), almost all components of the diagnostic workflow have gone digital—but that doesn’t mean things have gotten easier. The workflow can suffer from a lack in standardization, interoperability, and connectivity at all levels. Applications cannot exchange data, systems are not connected, and data is locked in silos.

However, lack of technology is not the problem. There are more technological solutions available to you than ever before. Technologies like cloud, big data, artificial intelligence, and machine learning can help improve health outcomes. And you also want to lower costs, improve diagnostic turnaround time, improve quality, and revolutionize the healthcare experience you provide for both patients and doctors. Interconnectivity and interoperability are essential if you want to access the data needed to speed up diagnoses and make real-time decisions. Therefore, you need a comprehensive plan. Here, we address the four major data challenges in clinical diagnostics and how you can overcome these barriers.