GNS Healthcare Blog
By Patrick Getzen, Chief Data and Analytics Officer of Blue Cross and Blue Shield of North Carolina
Clashes between the traditional and the novel are as old as time itself. Change is rarely welcomed, so anything new that comes along often faces a rough road to acceptance. However, that is not the case when it comes to the types of data that are now becoming available in the world of healthcare. The merging of new and traditional healthcare data promises to lead to a new era of discovery and offers significant benefits for patients, providers and payers.
Imagine if we could approach healthcare with the precision that retailers like Amazon and Netflix use to reach their customers. Imagine if we could grasp the holy grail of precision medicine and harness the ability to match patients with the specific treatment or intervention that is most effective for them as individuals.
How can we scale precision medicine so that every patient is provided with a personally optimized treatment plan? How can we turn data into models of disease progression and drug response, so we can discover novel biomarkers and accelerate drug discovery and development?
While artificial intelligence and machine learning technologies hold plenty of promise in helping to improve patient outcomes and lower costs in health care, making effective use of these technologies requires expertise and experience in handling massive data sets and the tools that extract the right information to answer healthcare’s most difficult questions.
Julie Slezak, EVP of Clinical Analytics at GNS, highlights the key stumbling blocks she sees most often and how to overcome them. Following are five key challenges. Consider this a giant heads-up to anyone employing artificial...