First, measures have to be defined – and CMS has focused much effort on this, with some 1700 measures across all quality programs floating out there. (Did you know that? Test your knowledge in the “Quality by the Numbers” quiz in our recent blog post.)
Next, each healthcare organization must use what the author calls its “big data” systems to generate information for those reports. According to National Quality Forum President and CEO Christine Cassel, MD, this is problematic since the industry is maintaining systems that are saddled with “poor usability, disappointing data integrity, siloed information, and conflicting standards.”
In our view the root of the problem is the individual patient record itself. Each record is a microcosm of “big data” – shall we call it “little data”? – that is growing in volume at 80 MB per year. Each record is made up of multiple data types (labs, meds, diagnoses codes, images) and up to 80% unstructured, narrative text found in discharge, admit, surgical and other notes.
Jennifer Bresnick, the author of the Health IT Analytics article, notes that:
In healthcare’s new big data world, patient records may be stuffed full of results, statistics, notes, and codes that convey no clear, immediate meaning to a treating physician, producing frustration instead of illumination when it comes to what the patient really requires.
More relevant to quality reporting than the “treating physician” is the clinical data abstractor. It is this person that is responsible for making sense of the record in order to report on quality and is often left frustrated instead of illuminated. Instead of making quality improvements that lead to real value-based care, a highly qualified nurse clinician will spend hours locating relevant patient facts. We’ve seen a recent study that studied two full-time infection control specialists and found that they spent five out of their eight-hour day on reporting alone. This is typical for those responsible for quality reporting. And they are frustrated!
Technology from QPID Health is aimed squarely at reducing that administrative burden and freeing quality resources for quality improvement. We do this by locating and synthesizing the relevant information from the record, and bringing it directly to the nurse abstractor. More here.