AWS leader talks about technologies needed to take precision medicine to the next level

One of the most significant challenges to the advancement of precision medicine has been the lack of an infrastructure to support translational bioinformatics, supporting organizations as they work to uncover unique datasets to find novel associations and signals. By supporting greater interoperability and collaboration, data scientists, developers, clinicians and pharmaceutical partners have the opportunity to leverage machine learning to reduce the time it takes to move from insight to discovery, ultimately leading to the right patients receiving the right care, with the right therapeutic at the right time. To get a better understanding of challenges surrounding precision medicine and its future, Healthcare IT News sat down with Taha Kass-Hout, director of machine learning at AWS. Q: You've said that one of the most significant challenges to the advancement of precision medicine has been the lack of an infrastructure to support translational bioinformatics. Please explain this challenge in detail. A: One of the challenges in developing and utilizing storage, analytics and interpretive methods is the sheer volume of biomedical data that needs to be transformed that often resides on multiple systems and in multiple formats.