Building Trust in Data
Gene Rondenet
CTO, qrcAnalytics
December 16, 2025
Why Data Integrity Is the Quiet Engine Behind Every Success in Healthcare Analytics
In healthcare, we talk a lot about advanced analytics, risk scoring, quality measurement, and value-based care. But the truth is simple: none of it works unless the data underneath is trustworthy. Claims data is often the starting place for every downstream process, and if it is incomplete, inconsistent, or misaligned – it ripples through everything.
Claims data can underlie everything from HCC-RAF scores to quality measures, yet it comes together from diverse sources—payers, clearinghouses, TPAs, and internal systems—each with their own layout, business rules, and timing. Bringing this all together into a unified clinical repository remains one of the most challenging engineering challenges in healthcare.
Why Claims Data Is So Hard to Trust
Payers receive data from providers in industry-standard formats (e.g., X12 837 transaction sets), yet payers can send their data to analytics platforms in its own non-standard format, at their own timing, and often with their own interpretation of coding rules. Something as small as a missing Member ID, missing modifiers, missing codes, or a date in the wrong format can throw off downstream analytics. Duplicate claims, denied claims, and amended submissions add another layer of complexity, and without the right logic to sort them out, organizations can end up counting the same service multiple times.
Even when files load “successfully,” mismatches in diagnosis codes, incomplete fields, or unsupported values can quietly distort everything that comes after. These small cracks in integrity ripple outward — leading to misclassified conditions, incorrect payments, and unreliable reporting.
The First Step: A Clear Contract for Data
Before a single file is loaded, the most successful organizations establish formal interface file specifications during contract negotiation. During contract negotiations, the provider organization has more leverage than later in the process. In plain terms, it clearly defines what data should include, how it should be structured, how often it is provided, and what rules it must follow. This powerful artifact becomes the backbone of data trust. It ensures that no matter who sends the data, the standards remain consistent.
How Modern ETL Tools Bring Order to Complexity
Import processing should enforce data specifications in real time. They need to validate the data as it arrives, catch errors before they spread, and ensure that every claim ties back to the right member, provider, and service. They keep bad data out, preserve good data, and create the transparency and foundation needed for compliance and performance improvement.
The Payoff
When data integrity becomes a priority, everything gets easier. Analytics are more accurate. Quality scores capture actual quality care provided to the patient and most importantly, organizations gain something priceless: confidence in their data.
And in healthcare, confidence in the data is where real transformation begins.
