Big data has come to healthcare in a big way. Over the past several years hospitals have adopted a variety of digital systems like electronic health records and PACS imaging systems that are generating large amounts of structured and unstructured data. In many cases the penetration of these systems into the market is over 90 percent.2 The cost of these implementations can be massive, with both Mayo Clinic and Partners Healthcare estimating that their Epic EHR implementation costs at close to $1 billion.3
One question that inevitably arises is how to extract the maximum value from these investments? A key vector for value creation lies in extracting operational, financial, and clinical insights from the data that these systems capture. To achieve it and build the foundations for future success, it is important to know how to use the data to generate new revenue streams, improve operational efficiencies, or improve clinical outcomes.
However, executing on an analytics strategy to capitalize on this data is often not straightforward. It’s telling that in a recent survey, 94 percent of hospitals4 said they're not capturing the information necessary for population health analytics. We’ll see in this article that there are several common challenges to implementing a sustainable advanced analytics program in healthcare, but that none of them are insurmountable.