By Mark Coetzer, VP of Business Development, IMAT Solutions
LinkedIn: Mark Coetzer
LinkedIn: IMAT Solutions
As healthcare organizations prepare for the future of quality reporting, much of the conversation around digital quality measures (dQMs) has focused on implementation costs. Questions about technology investments, interoperability requirements, staffing needs, and reporting workflows are all valid considerations as providers and payers evaluate what it will take to support a more digital approach to quality measurement.
In a recent article from Firely, highlighted the hidden costs of maintaining legacy quality measurement infrastructure, from ongoing manual abstraction and reconciliation efforts to repeated development cycles and data mapping challenges. Their point is an important one, which is that the status quo is not free.
I would take that conversation a step further. Healthcare organizations should also consider the broader operational and strategic costs of delaying dQM readiness. Fragmented data environments, disconnected reporting processes, and limited visibility into quality performance can slow quality improvement efforts, hinder decision making, and make it harder for organizations to realize value from their healthcare data investments. As digital quality measurement continues to evolve, organizations are increasingly investing in stronger data foundations that support reporting, analytics, and quality improvement initiatives.
The Hidden Expense of Manual Quality Reporting
Most healthcare organizations don’t think of manual quality reporting as a major cost center because it’s simply how things have always been done. Yet teams still spend significant time gathering data, reconciling discrepancies, and preparing submissions across multiple systems. Every hour spent managing manual reporting processes is an hour that cannot be spent improving quality performance or supporting patient care. As reporting requirements continue to evolve, maintaining these workflows may become increasingly costly and difficult to sustain.
Data Quality Challenges Are Already Creating Costs
One of the biggest misconceptions about dQM readiness is that it’s a future problem. Yes, HEDIS 2030 is coming, but many of the challenges organizations are trying to solve for tomorrow are already affecting them today. Incomplete data, inconsistent documentation, fragmented systems, and limited visibility across care settings create obstacles that extend well beyond quality reporting.
These issues can impact everything from care coordination and provider engagement to operational efficiency and quality performance. They can also make it harder for leaders to trust the data they’re using to make decisions. That’s why dQM readiness shouldn’t be viewed solely as preparation for a future requirement. At its core, it’s about addressing data challenges that are already creating friction across healthcare organizations today.
dQM Investments Support More Than Compliance
Another common misconception is that dQM readiness is simply about meeting future reporting requirements. In reality, many of the investments needed to support digital quality measurement also strengthen other strategic priorities. Improvements in interoperability, data quality, and reporting capabilities can support population health programs, value-based care initiatives, performance analytics, and even emerging AI efforts. That is why many healthcare leaders are starting to view dQM readiness as more than a compliance project. The same foundation that supports quality measurement can also help organizations operate more efficiently and gain greater value from their healthcare data.
The Opportunity Cost of Waiting
When organizations delay investments in data readiness, they are often delaying benefits they could be realizing today. Better data quality, stronger interoperability, and more efficient reporting processes can reduce administrative burden, improve decision making, and support quality improvement efforts. Those benefits don’t start in 2030. They start as organizations improve the way they manage and use data.
The longer organizations wait, the more likely they are to face higher costs and greater disruption when the transition to digital quality measurement accelerates.
The Real Question
Healthcare organizations are steadily moving toward a more digital approach to quality measurement, making data readiness an increasingly important priority. Much of the conversation focuses on the cost of dQM readiness, but fragmented data environments, manual reporting processes, and inefficient workflows carry costs of their own. Those costs often show up as administrative burden, operational inefficiencies, and missed opportunities to make better use of healthcare data.
Organizations that strengthen their data foundations today can support digital quality measurement while also improving quality performance, operational efficiency, and decision making along the way.