Grappling with the Future Use of Big Data for Translational Medicine and Clinical Care

Abstract

<b>Objectives:</b> Although patients may have a wealth of imaging, genomic, monitoring, and personal device data, it has yet to be fully integrated into clinical care. <b>Methods:</b> We identify three reasons for the lack of integration. The first is that "Big Data" is poorly managed by most Electronic Medical Record Systems (EMRS). The data is mostly available on "cloud-native" platforms that are outside the scope of most EMRs, and even checking if such data is available on a patient often must be done outside the EMRS. The second reason is that extracting features from the Big Data that are relevant to healthcare often requires complex machine learning algorithms, such as determining if a genomic variant is protein-altering. The third reason is that applications that present Big Data need to be modified constantly to reflect the current state of knowledge, such as instructing when to order a new set of genomic tests. In some cases, applications need to be updated nightly. <b>Results:</b> A new architecture for EMRS is evolving which could unite Big Data, machine learning, and clinical care through a microservice-based architecture which can host applications focused on quite specific aspects of clinical care, such as managing cancer immunotherapy. <b>Conclusion:</b> Informatics innovation, medical research, and clinical care go hand in hand as we look to infuse science-based practice into healthcare. Innovative methods will lead to a new ecosystem of applications (Apps) interacting with healthcare providers to fulfill a promise that is still to be determined.

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