Ask Adele: August questions
Monday, August 18, 2014
Posted by: Joy Ingram
Adele Allison is the National Director of Government Affairs, SuccessEHS, a Division of Greenway. SuccessEHS is a non-voting member of NWRPCA
Big Data’s Role in Healthcare
I read an article that spoke to the uptick in adoption of health IT by healthcare professionals through programs like meaningful use. More and more we are seeing integration of such things as lab results, vitals, and encounter notes into a digital format. How will all this new data impact providers and does it play a role in healthcare reform?
What a great question and actually the subject of data is one of my favorite topics simply because it lends itself to measurable improvement!
Big data is making its debut in healthcare. As the country moves through reform, data is playing a growing role in a national movement from transaction-based to performance-based payment models. Public transparency is also beginning to directly link providers to data supporting treatment benchmarks, outcomes and patient experience scores. Formulating a strategy to advance with the industry becomes essential at a time when medicine faces a barrage of changing healthcare policies; and, strong data captured at the point-of-care will lead to clear-cut, measured performance under various federal and commercial programs. What can an organization do to position for advancing "Big Data?"
In 2012, Gartner, Inc., an information technology research and advisory firm, established the 3 V's of Big Data: Volume, Velocity and Variety. Recently, organizations have added an additional element to defining big data – Veracity. To create value from data, drive a cultural shift and move into ongoing process improvement, organizations need to maintain an actionable, evolving plan that squares off against the 4 V's of big data. So, let us look at each of these components.
Volume is about having large stores of data. Medical practices have this inherently to some degree in such areas as claims management, scheduling and accounts receivable. Most of this information is transactional requiring small amounts of storage. As organizations strive for larger volumes of data, conventional IT networks may have to be expanded and distributed for useful queries to occur. This is where hosting or other alternatives to traditional infrastructures may make more business sense; networks designed to cope with big data analytics like data warehouses or those with parallel processing architectures such as used with Apache Hadoop-based solutions.
Velocity is a measure of just how quickly the data comes into an organization and is delivered to a decision-point in a workflow. The rule of thumb: You want timely delivery of data from the point of entry to the point of decision-making. Mobile technologies and the Internet are doing much to address streaming of data to an end-user on the move – the clinician. However, a cautionary note is important. Mobile devices must be void of any stored protected health information (PHI) to mitigate the risk of breaching HIPAA. Velocity is also a measure of the speed with which clean data moves into your bulk storage network for processing and use at a later date, a process discussed more fully under veracity below.
Variety can directly impact value. Yet data diversity means data complexity in normalizing sources of information into neat and useable relational structures. For instance, a simple medical concept like blood pressure can be stated 3 separate ways, all having the same finding: high blood pressure, elevated blood pressure, and hypertension. But how is a computer to know without indexing that these elements are the same? While there are emerging technologies that can process unstructured data into order and meaning, structured data entry is your friend in the world of big data in healthcare. This is available today from automated practice management (demographics, ICD, CPT, payer-related data), but now is the time to progress your data capture as your clinic becomes more fully digitized through electronic health records (EHRs). This means your data treasure trove can be increased with clinical information from structured sources such as lab results (LOINC), medications (RxNORM), encounter documentation (SNOMED CT), and patient reported data (patient questionnaires, CAHPS). The real value will come in blending these native sources with data that are not intrinsic to healthcare for example social media (Facebook, Twitter), retail (purchases), and search engines (Google).
Veracity being the most recently added "V," is of the upmost importance. Without data integrity, decision-making is flawed. Can the data be trusted? Is it accurate? Vigilant, consistent data capture in a structured format for key pieces of information will go a long way to helping with data veracity. We see this call to action under the CMS Meaningful Use program requiring such data elements as smoking status, height, weight and blood pressure. Structure can come in the form of user-defined fields (E.g., appointment types) or standardized, codified vocabularies (E.g., ICD, SNOMED CT). In the former, organizations can aggregate internally while the latter is conducive to universal, large-scale data aggregation. As discussed above, structured input becomes a critical component of data veracity, but another critical piece of the equation is validation of results when reporting. Without data validation to determine whether your results are "clean," you may be solving some of your biggest problems with entry errors, duplicated information and corrupt data. Veracity is by far the toughest of the V's to master. It can also be the biggest barrier to delivering a fifth "V" – Value.
Healthcare is just beginning to enter the big data fray and the possibilities that meaningful data can have on your organization and patient care are endless. As healthcare reform matures, contemplating your data needs and uses will allow you to thrive. Identify your data essentials today. Then start planning and designing your care delivery system to incorporate measurement and oversight. When developing your strategy, be sure to maintain an eye towards impacting 5 key areas of the clinical practice: quality improvement, patient engagement, operational efficiency and revenue, clinical research, and reducing risk and liability.
Those groups that apply ongoing data metrics and analytics to process improvement will quickly learn that it is a great time to be in healthcare!
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