I had an interesting conversation with clients recently that challenged my thinking about implementing technology solutions for healthcare billing companies. They were looking to grow their business through infrastructure improvements and software implementations to become a technology-centered billing operation. When I advocated for a comprehensive RCM analytics solution as the foundation for automation and artificial intelligence capabilities, we hit an unexpected roadblock.
The RCM Analytics Misconception
The clients pushed back, saying they weren’t ready for full-blown analytics. They had no analysts in-house and lacked billing managers or higher-level operations staff (though they had executives). Without people internally capable of using tools like Tableau, Qlik, or Power BI, they believed they just needed to hire an analyst instead of implementing an analytics platform.
Their plan was to bring in a financial analyst, RCM analyst, or revenue integrity analyst to handle the work they needed done. This led to a crucial realization on my part: they weren’t actually ready for an analyst either.
If an analytics solution were already in place, then yes, an analyst would be tremendously helpful. But without it, what would this person actually do? Most analysts aren’t data extraction engineers—they’re skilled at analyzing data that already exists in a repository.
Reframing the Solution
What I ultimately recommended was a shift in perspective: don’t think about it as an analytics solution. Think about it as a data warehouse or repository that an analyst (once hired) could leverage through presentation-level tools like Power BI.
Even other team members, like billing managers who might not work directly in analytics tools, could easily extract pre-existing reports on charges, collections, or denials—data that updates in real-time. They could review this information themselves, or an analyst could help manipulate and interpret it.
The real value isn’t in the presentation layer or analytics tool itself. The value lies in:
- Data extraction
- Data transformation
- Pre-processing
- Getting data from disparate sources
- Bringing it all together
- Normalizing and joining it
- Creating one centralized repository
When Basic Reporting Gets Mislabeled as RCM Analytics
Once the clients began thinking about it as a data warehousing solution rather than an analytics platform, things clicked. Sure, sophisticated analytics could come later, but their immediate need was simply accessing organized data.
They were trying to answer basic questions like, “What’s in our AR buckets?” “Are things moving in and out of these buckets?” “Who’s working them?” These aren’t complex analytical questions—they’re fundamental reporting needs that often require high-quality dashboards.
People often confuse analytics with basic reporting and data access. Most requests aren’t sophisticated analyses at all—they’re just tables or lists. I think there’s a misnomer in our industry that causes people to overthink and say, “We don’t need analytics, that’s too complex.” In some ways they’re right—they don’t actually need complex analysis. They just need data and basic reporting capabilities.
This is especially true when you’re still trying to develop effective RCM KPIs for your organization. Without accessible data, even the best KPI framework will fail to deliver insights.
Real-World Example: Denial Management
Consider denial management: you want to slice and dice denial data, but that information exists in your clearinghouse in a format that’s not easily usable. You might want to join this with reference data—what does claim adjustment reason code 109 or 253 mean? You need lookup tables to add descriptions.
Once this pre-processing happens, you can generate useful reports like “top denials by payer”—which isn’t complex analysis, just a basic grouping function in a table.
The Bottom Line
Everyone needs data warehousing. Everyone needs centralized data combined from multiple sources where they can get answers and generate tables. According to Gartner’s research on data analytics, organizations that establish strong data infrastructure before implementing advanced analytics tools are 2.5 times more likely to report successful outcomes.
Data first, analytics second—that’s the key to successful RCM analytics implementation.
