How healthcare organizations can reap value from vast stores of data
The healthcare industry is finally nearing the end of the task of digitizing health records. It has been an arduous and necessary journey worth celebrating. Now, however, healthcare organizations are faced with two major challenges.
First, many organizations are struggling to deliver a reasonable payback on their investments. And second, advances in scientific computing and analytics—things like next-generation electronic health records, genomic sciences, precision medicine, predictive analytics and machine learning, the enterprise imaging revolution, advances in electron microscopy (including cryo-EM), exploring unstructured data such as digital notes and more—have resulted in an unbounded data explosion.
The key to monetizing this new data lies in unlocking meaning from this massive quantity of data—applying math, statistics and analytics to understand how to optimize healthcare delivery, make patients and providers happier and result in better patient care and outcomes. This is an extremely intense data management exercise, as are the next-generation of predictive and prescriptive analytics applications. The industry will be generating more data on top of a lot of data, resulting in even more data. And the cycle continues.
To harness the power of this boom, the data infrastructure management model must undergo a fundamental transformation. It must shift from a tactical model—one that is analogous to the processes of procuring surgical gloves and thermometers—to a strategic model in which the data infrastructure is available for day-to-day tasks, but also enables clinicians to focus on proactively applying data to new problems and business models, while innovating and driving value.
With that in mind, the following represents a way that organizations can drive both value and meaning from the massive amounts of EHR data.
In healthcare, all data matters. The short list includes structured data (data in EMRs, OLAP and OLTP databases); exhaust data (data that’s generated as a byproduct of working with other data); unstructured data (data in clinical notes, flat files, images, BLOBs and more); and IoT data (sensors, wearables, trackers and more). It all matters.
The industry is in the early stages of harnessing value from some of this data, but in reality, there’s a lot of yet-to-be-mined value from all of it.
