In healthcare, it’s imperative that decision-making is based on sound information. And when those decisions will influence large groups of people – like the ones that drive public health reporting or clinical decision support recommendations – the stakes are even higher.
In these large-scale endeavors, the importance of data aggregation comes into play. Indeed, this data is central to a variety of use cases – it can drive more reliable research; assist in meaningful reporting efforts; help with identification of precise cohorts; and support more accurate healthcare data analytics. What’s more, 95% of hospitals rely on this type of information when making key decisions.
Three of the numerous players working to aggregate and ensure data quality in healthcare are integrated delivery networks (IDNs), health information exchanges (HIEs), and clinical data registries (CDRs). And, despite working in different sectors of the medical world, each of them face the same overarching challenge.
That main struggle? Figuring out the best way to manage the sheer volume of data generated within the healthcare ecosystem, especially given that a diverse range of data sources – like electronic health records (EHRs), state and federal data registries, and even wearable technologies – generate important material, but the information they contribute is rarely, if ever, in a consistent or standardized format.
Overall, that means that the process of aggregation is just the beginning. These organizations then must do more work to achieve the needed standardization – a process known as data normalization – before it can be used in a variety of healthcare data analytics endeavors.
