5 Reasons Healthcare Data Is Unique and Difficult to Measure

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4. The data is complex.

Healthcare data complexityClaims data has been around for years and thus it has been standardized and scrubbed. But this type of data is incomplete. Clinical data from sources like EMRs give a more complete picture of the patient’s story.

While developing standard processes that improve quality is one of the goals in healthcare, the number of data variables involved makes it far more challenging. You’re not working with a finite number of identical parts to create identical outcomes. Instead, you’re looking at an amalgam of individual systems that are so complex we don’t even begin to profess we understand how they work together (that is to say, the human body). Managing the data related to each of those systems (which is often being captured in disparate applications), and turning it into something usable across a population, requires a far more sophisticated set of tools than is needed for other industries like manufacturing.

5. Changing Regulatory Requirements.

Healthcare regulations and requirementsRegulatory and reporting requirements also continue to increase and evolve. CMS needs quality reports around measures like readmissions, and healthcare reform means more transparent quality and pricing information for the public. The shift to value-based purchasing models will only add to the reporting burden for healthcare organizations.

Healthcare Data Will Only Get More Complex

Healthcare data will not get simpler in the future. If anything, this list will grow. Healthcare faces unique challenges and with that comes unique data challenges.

Because healthcare data is so uniquely complex, it’s clear that traditional approaches to managing data will not work in healthcare. A different approach is needed that can handle the multiple sources, the structured and unstructured data, the inconsistency, the variability, and the complexity within an ever-changing regulatory environment. The solution for this unpredictable change and complexity is an agile approach, tuned for healthcare. As with a professional athlete, the ability to change directions on a dime when the environment around you is in constant flux is a valuable attribute to have. If I start out from point A in direct route to point B and the location of point B suddenly changes or an obstacle arises, I certainly wouldn’t want to have to retrace my steps back to point A, redefine my coordinates, and set off on the new course. Rather, I need to take one step at a time, reevaluate, and pivot inflight when necessary.

Agility Compensates for Complexity and Uncertainty

Those are the core issues with healthcare data, and they are very real. Understanding that, and the fact that some of those issues will never change, the question becomes how you work within those limitations to deliver better information to those who need it.

The generally accepted method of aggregating data from disparate source systems so it can be analyzed is to create an enterprise data warehouse (EDW). It is a method common across many industries. Just as a physical warehouse is used to store all sorts of goods in bulk until they’re needed, an EDW houses data from across the enterprise in a single place.

Yet how you aggregate that data can have a huge impact on your ability to gain maximum value from it. The early-binding methods that are prevalent in manufacturing, retail, and financial services don’t work very well in healthcare, because they depend on making business rule decisions before you know what you want to do with it. It would be expensive to warehouse goods with the thought in mind that you would store everything you could ever want in the future. So you’re paying for all the storage space and the overhead that comes along with it. But you’re not using it.

Traditionally other industries look ahead at what business questions they’ll want to answer. They know exactly what information they’ll need. Their data warehouses, then, store everything they need in the way that they need it.

Healthcare is not like those industries where business rules and definitions are fixed for long periods of time. The volatility of healthcare data means a rule set today may not be a best practice tomorrow. The industry is filled with instances of EDW projects that never deliver results or even come close to completion because the rules and definitions keep changing.

A better approach is to use a Late-Binding™ Data Warehouse. With this schema, data is brought into the EDW from the source applications as-is, and placed into a source data mart. When you need to turn it into information, it is then transformed into exactly what the analysis requires. If there is a change to the business rules or definitions, such as what constitutes an at-risk patient, that change can be applied within the application data mart rather than having to transform and reload all the data from the source.

That is how Late-Binding™ supports the discovery process so important to healthcare. When frontline business users enter into a clinical analysis of the data, you want them to start free of any pre-conceived data models.

Late-Binding™ allows you to aggregate data quickly and develop business rules on the fly so users can develop hypotheses, use the data to prove them right or wrong, and continue the discovery process until they are able to make scientific, evidence-based decisions.

How have you addressed the complications of healthcare data? What do you think is the biggest obstacle to good healthcare data analysis?


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