Quality enterprise data analytics are an essential component of any healthcare enterprise data strategy. Yet healthcare systems face particular enterprise analytics challenges. One of these challenges is obtaining quality clinical data enrichments.
Today’s post is the fourth and final installment in our series on the importance of clinical data enrichments for large hospitals and hospital systems.
So far, we’ve heard from:
- Curvo Labs’ Chief Technology Officer, Nic Sagez, who spoke to hospital CIOs on the topic
- Orthopedic Network News (ONN) editor, Stan Mendenhall, who addressed service line leaders
- Curvo co-founder and Chief Customer Officer, Steve Suhrheinrich, discussed the topic from the perspective of supply chain, sourcing, and value analysis
Today, we’re going to hear from Jake Titzer, a Healthcare Data Analyst at Curvo, on the importance of data enrichment for healthcare analysts. It’s time for healthcare analysts to see the difference that quality clinical data enrichments can make.
Jake oversees all data coming into Curvo from health systems, collaboratives, consultants, and partners. He knows supply spend, PPI categories, and orthopedics – inside and out. Because of his expertise, he frequently advises on data anomalies, grooming, normalization, and interpretation. He also influences many of Curvo’s clinical content articles including the Spotlight, Procedure Basics, and Orthopedic Network News.
Here’s what Jake has to say to healthcare analysts about the importance of clinical data enrichment in healthcare.
Why do data enrichments matter for large hospitals and hospital systems?
There are plenty of data enrichments that are possible, of course, and we’re focusing specifically on clinical data enrichments. The key here is greater visibility. With greater visibility, healthcare analysts can much more easily break down costs, produce valuable analyses, and pull out the savings.
We often encounter databases that have product descriptions and SKUs, and that’s about it. With a database that’s limited in this way, you can’t dig down to see what kinds of products are being used.
With clinical data enrichments, it’s possible to go into an MMIS system or an Epic or Cerner feed and differentiate within a category. Analysts can gain insight into which products used a unique, more expensive material or a novel feature.
It’s also possible to go into the data and discover whether the health system is doing a greater number of a particular procedure.
For example, with proper data enrichments analysts have visibility to the number of revisions vs. primary procedures or coated vs. uncoated.
These insights are key to effectively drive clinical conversations and ultimately savings for health systems.
How are they doing this today, without Curvo?
What I’ve seen is that many hospitals and hospital systems are simply using UNSPSC classification through their MMIS system. But this is a general classification system, a catch-all. It’s not capable of producing rich data at the level that Curvo can in clinical products, PPI, or orthopedics.
In EPIC or Cerner systems, analysts can evaluate at the construct level (rather than the SKU level) using DRG and CPT codes. These codes are useful tools that influence the type of reimbursement that is received.
This approach is a step up from using just UNSPSC classification, but it’s still far less powerful than Curvo’s data enrichment customers receive. It’s a good way to tell how many of a class of procedures (Ex: a hip arthroplasty) were performed. But as far as what type of hip arthroplasty and what specific products went into the hip, the health systems are at a loss. They don’t have this level of knowledge because they’re missing out on clinical data enrichments at a construct or a component level. It is nearly impossible to address case cost variance and pursue savings with clinical buy-in without it.
How do these limitations affect the quality of their analysis or the conclusions they can draw?
Without the classifications and enrichments, it’s difficult to take a deeper dive into the data and draw detailed conclusions. An orthopedic department may know it’s doing a lot of hips and knees, but doesn’t know the nature of those procedures.
Were they mostly primary, hybrid, or revisions? What types of components were used most frequently, and which implants are the most significant cost drivers? It’s difficult to answer those sorts of detailed questions.
When you can’t extract that kind of data, it hinders efforts, both in negotiating with vendors and in doing comparisons between physicians.
In all of this, savings is the goal. But it’s hard for healthcare analysts to trim the fat when they don’t know where the fat is. That’s the problem with most other approaches to data enrichment and analysis; they don’t yield as much actionable information.
As the founder of Orthopedic Network News, Stan Mendenhall, often says, “the more you know, the less you pay.”
When prospective customers talk about doing categorization and normalization (without Curvo), where are they encountering problems? How do those problems impede their work?
The biggest problem they encounter is time. It’s exceedingly time-consuming to get this information manually. First, they have to take 12 months of data and find all the hips and knees. They also need to determine which are coated versus uncoated (among several other variables) before they can draw useful analyses from the data.
It’s possible to do this manually. Skilled analysts can search all the SKUs to get to this information. But it takes weeks. And because the data doesn’t stay current for that long, the process must be repeated frequently.
One customer recently estimated that a Senior Data Analyst was spending 3.5 to 4 weeks on this information alone. If the project was delayed in any way, they had to repeat the exercise. It doesn’t take long to do that math on that type of operational expense from a strategic resource.
In contrast, Curvo data enrichment customers can leverage GIC classification from ONN to produce a 12-month spend with all clinical enrichments and procedures within just three days.
Normalization, too, tends to be an excruciatingly manual process without Curvo. Item numbers get entered multiple ways in MMIS, and it’s very challenging for human eyes to catch all these issues. Curvo uses “groomers”, subject matter experts dedicated to cleaning up the data as it comes through, so that these fields catch any items that didn’t get matched through our proprietary artificial intelligence software.
The cleaner the data, the better and more actionable the results.
How do current data enrichment customers describe the value of Curvo’s Data as a Service offering?
One of the most significant advantages of Curvo’s data enrichment service is the ability to analyze constructs and procedures across multiple facilities. It’s easy to get this data normalized across facilities, and our tools make it simple to manipulate and evaluate this data as well. Data as a Service customers also gain access to some powerful visualization tools, allowing them to visualize complex data like never before.
Another advantage is the depth of enrichment that’s available. Some competitors are beginning to offer limited data enrichment, but Curvo takes it to a depth that no one else can match. For example, we add in Type 1 and Type 2 GICs (Generic Implant Classification, proprietary codeset for the ONN Classification). Curvo also enriches with construct logic and component detail that allows for case cost and utilization analysis like never before.
With Curvo, you get far more methods and metrics by which you can analyze the data, empowering your team to draw ever better conclusions.
See Curvo For Yourself
Curvo’s data enrichment services can transform the way your health system interacts with its enterprise data analytics. This allows you, as a healthcare analyst, to produce data-driven insights quicker.
If you’d like to see what Curvo can add to your enterprise data strategy, we’d love to show you. Schedule a demo today.