​Most Healthcare institutions continue to lack comprehensive electronic patient records, especially for the vulnerable populations who are uninsured or underinsured. Even Institutions with funding and resources to collect “Big Data'' fail to determine how to best manage and incorporate analytics into their operations.
Despite the development and implementation of Electronic Health Records to better utilise the data, they lack uniformity and raise alarming concerns with keeping the records secure from misuse, identity theft and hacking.Public policy hasn’t kept up with the demand for interoperability, information exchange and data sharing, creating an obstruction for innovation. Moreover, in innovative research practices that integrate EHR, Consumer and Genomic data for developing individualized treatment models in learning health systems, questions arise about reliability, feasibility, replication, scaling, regulation, manufacturing, and costs. Further, technological vulnerabilities like privacy, security, inadvertent algorithmic glitches and data acquisition overreach concerns put these innovations at risk. Data deficiency poses a challenge for the training of these models against samples that fully represent the biological, genetic and ethnic diversity of the populations they are designed to serve.