From the October 2016 issue of HealthCare Business News magazine
By Neil Smiley
It was a joke in the early days of electronic health records that EHR stood for “Empty Health Record.”
We’ve come a long way in filling in those empty records, but unfortunately, some practitioners still feel the time they spend entering data yields very little in return. Of course, the promise of health care analytics is that this EHR data has the potential to advance care, to help practitioners better predict and manage population risk and aid in prevention. With faster computers, ever-growing data sets and sophisticated algorithms, there should be no limit to our ability to glean insights and drive new innovation, right?
However, even with the billions of dollars invested in EHRs, meaningful analytics have been slow in coming, and some practitioners are beginning to wonder if the promise of big data has been overhyped. The truth is that the path to success doesn’t lie only in the data, but also in the human insight to make sense of the analysis.
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There’s no doubt the potential of health care analytics is huge. However, we are falling short when it comes to bridging the gap between the practitioners using EHRs in daily patient encounters and the data scientists tasked with unlocking these analytical powers. In order to make good on the potential, we must recognize, and effectively capitalize on, the important roles that both practitioners and data scientists play in this collaborative effort. To that end, here are the key challenges thwarting health data analytics, and how practitioners and data scientists can collaborate to advance patient care and improve health outcomes.
Data can be dirty
Much of the data from which insights could be derived is unstructured, inconsistent and mismatched. The old cliché, “garbage in, garbage out,” certainly applies here. Much of the work associated with data analytics involves cleaning up the data before you can even begin the fun stuff — exploring and arriving at insights. Using technology to harmonize the data can help, but practitioners also play a large role in helping data scientists identify anomalies that might waste time or send analysts down a path toward misleading results.
Data can be vast
Asking data scientists to comb through terabytes of health care data and surface insights without a practitioner’s guidance is like sending them on a far-flung journey without a map or even a specific destination. There are simply too many blind alleys and rabbit holes along the way. It requires an informed guide to narrow the range of potential paths for exploration.