co-authored by: Eric Parapini
As standards developers and data analysts, we rely on finding patterns in our day to day work. We apply our patented “pattern recognition algorithm” (note: not a real patent) to identify the questions our clients ask most often.
“How can we leverage our healthcare data to perform quality reporting?” Closely followed by, “and can we use C-CDA to import that data into our analytics environment?” Although we’d prefer a simple “yes”, the solution is rarely simple.
C-CDA is a foundational standard for the Quality Reporting Document Architecture (QRDA), which is used extensively in quality reporting. Due to meaningful use, certified EHR vendors should* be capable of producing valid C-CDA documents with relative ease. This is a great starting point, and clients should explore C-CDA for their needs. However, it is not a primrose path. Obtaining C-CDA documents is easy. Determining whether or not the information within the documents can actually be used is not.
There are two distinct goals when it comes to quality reporting. The most commonly desired outcome is to produce results for any of the quality measures specified by Meaningful Use. Said another way, it’s the “here’s our data, what’s the most we can do with it?” goal. Another goal is producing results for a predetermined set of quality measures.
The first goal begins with patient data, and finds the eCQMs that the data supports. The other is based on a given set of eCQMs, and finds patient data to support the reporting criteria. Both come with a warning from your friendly Quality Surgeon General: You need quality data to produce quality reports, and it’s corollary: Not everyone is in possession of quality data.
Thankfully, we come armed with a plan.
If reporting on a set of specific measures, the first step is creating a list of unique data elements used in those particular eCQMs. These data elements, also known as Quality Data Model Data Elements (QDE), are listed under the “QDM Data Elements” section for each eCQM. Without any restriction on eCQMs, we assume all data elements are fair game.
Next, create a model that maps the data elements in the eCQMs to C-CDA data elements (AKA templates). Each data element within a given quality measure is linked to an equivalent data element in C-CDA. The resulting model provides a high-level overview of which C-CDA templates to look for when analyzing patient C-CDA files.
Regardless of your initial goal, this step comes down to analyzing the C-CDA patient files and determining which C-CDA templates are available. When producing quality reports for any eCQM, we look for data elements and templates that match an eCQM’s QDE. If the goal is to produce reports for a specific set of eCQMs, then we are looking for specific data elements and templates. Using the mapping between C-CDA templates and QDE, we can compare the available C-CDA templates to the eCQM data elements. Any eCQM that has all the required data available is calculable.
What happens if the C-CDA files don’t have the information required to calculate an eCQM? Oftentimes, there are one or two missing templates across multiple measures. Identify which missing templates in the C-CDA are required by the largest amount of measures. These templates must be populated in the C-CDA, so work with the data sources to see if this can be exported.
Beware of CDA pitfalls! The biggest focus when assessing a C-CDA document for quality reporting is computable data. C-CDA’s flexibility can work against us here: it allows documents to have the necessary information present in the narrative, and the machine-processable parts nulled out. Beware of the use (and abuse) of Null Flavors. Beware of providers’ in-house terminology codes, which aren’t from a standardized code system (e.g., LOINC, SNOMED-CT) required in Meaningful Use. For an in-depth look at issues found in C-CDA, check out Rick’s continuing series on CDA in The Wild .
While fixing data issues can be daunting, there are ways to make the process easier. Identify data required by common templates used in multiple measures. Work with terminologists to ensure the right value-sets are employed in the C-CDA templates. This is a critical step; calculating eCQMs relies heavily on matching the right terminology codes.
It helps to be flexible in the eCQMs that are targeted for calculation and reporting based on the data available in the C-CDA documents. Employing these strategies will ensure the data meets its maximum potential. While not every scenario will result in quality data, those that do will result in quality reports!
*We say should because, even though certified, EHRs don’t always produce valid CDA in the real world. See Rick Geimer’s CDA in the Wild series.