How to Build an EHR That Supports CMS Quality Reporting

For decades, quality reporting has been an integral part of the healthcare landscape in the United States. Its foundation can be traced back to 1965, and it has evolved into what is today known as Centers for Medicare & Medicaid Services (CMS) Quality Reporting, which is essential as it holds healthcare providers accountable for the care they provide, along with driving clinical improvements and transitioning the shift towards value-based care.

Today, EHRs have become a critical part of this quality-reporting ecosystem. The data needed for quality reporting includes diagnoses, medications, laboratory results, procedures, patient demographics, and clinical outcomes.

Now, your EHR system captures everything, laying the foundation for quality measurement and reporting. But the problem is that simply storing clinical data does not make your system ready for CMS quality reporting, right?

The data must be captured in a structured and standardized format, mapped to appropriate clinical terminologies, evaluated against 49 eligible clinical eCQMs, and validated before being presented in a reportable format. Your EHR system needs to have these capabilities so that you don’t rely on manual data extraction, etc.

However, the complexity of CMS reporting is that, out of the 190 Merit-based Incentive Payment System (MIPS) measures, you have to meet 49 eligible clinician eCQMs, where almost 30% of the 2026 MIPS score is based on the quality of care alone. This might give you an idea about the importance of reliable quality-data capture and reporting capabilities.

On that note, in this blog, let’s see how to build EHR CMS quality reporting capabilities and everything that you need to know about it. Along with that, how your HIPAA-compliant EHR architecture can help with CMS-compatible reporting.

So without further ado, let’s get started!

What Does an EHR Need to Support CMS Quality Reporting?

An EHR system that supports CMS quality reporting must capture structured clinical data, use standardized terminologies, apply quality-measure logic, validate results, and generate CMS-compatible reporting outputs.

This is important because CMS quality measures rely on structured and computable data rather than information stored in free-text clinical notes. For instance, your physician may document that blood pressure is elevated, but the EHR cannot reliably use that statement in quality calculations. That is why capturing systolic and diastolic blood pressure as structured observations with date, code, and patient information can make the identification process earlier and evaluate them accordingly.

A modular EHR architecture separating clinical data capture, terminology management, quality-measure calculation, analytics, and reporting allows each component to evolve independently.

Long story short, CMS-ready quality reporting starts with how clinical data is captured, not when the reporting process begins.

Furthermore, there are several CMS quality programs. Here are a few prominent ones and what your EHR must support for them:

How Do You Build CMS Quality Reporting into an EHR?

Building EHR CMS quality reporting can be done by working backward from the measures you need to report and connecting each measure to structured clinical data, calculation logic, validation, and CMS-compatible reporting outcomes.

Here are the six steps in which you can build EHR CMS quality reporting:

• Select the Measures the Organization Will Report: Start by identifying the CMS quality measures applicable to your practice, specialty, reporting program, and performance period. The selected measure determines what clinical data the EHR needs to capture and how that data must be processed.

• Trace Each Measure Back to Its Required Data Elements: Once the measures are selected, break down their requirements into the specific data elements needed for calculation. These may include diagnoses, procedures, laboratory results, medications, encounters, demographics, and clinical observations. This creates a direct connection between the quality measure and the data captured in the EHR.

• Make Data Structured and Coded at the Point of Capture: Configure clinical workflows so required information is captured as structured, standardized data rather than relying entirely on free-text notes. Use the appropriate clinical codes, terminologies, and value sets so the data can be consistently interpreted by the quality-measure engine.

• Build the Measure Calculation Engine: The calculation engine applies the logic defined by each quality measure to the EHR data. It should identify eligible populations, calculate numerator and denominator results, apply exclusions and exceptions, and support measure-version updates.

• Build QRDA Export and Submission: The EHR should transform calculated quality data into the applicable QRDA Category I or Category III format and support the necessary export and submission workflows.

• Validate Against CMS Test Files Before Reporting Opens: Before the reporting period begins, validate the EHR’s output against CMS-provided implementation guides, sample files, validation tools, and test scenarios. This helps identify structural, coding, and calculation errors before they affect actual submissions.

Capturing the Lab Data Quality Measures Depend on

One of the major inputs on which your eCQMs depend is laboratory data. This is why your EHR must capture lab results as structured, standardized, and computable data so that it can rely on them for quality reporting.

Many quality measures depend on laboratory results to determine whether a patient meets a measure’s denominator or numerator criteria. This includes values like blood pressure, HbA1c, cholesterol, kidney function, and other clinical observations.

Now, for your EHR system to use these results in automated calculations, the data needs to be captured with the relevant test, result, unit, date, patient, and standardized code. Simply storing a laboratory report as a document or free-text result isn’t enough.

For instance, if the result is not structured or mapped to the appropriate terminology and value set, the quality-measure engine may not be able to recognize it as the required clinical data. This often leads to incomplete patient populations, inaccurate performance calculations, and missed opportunities to identify care gaps.

That is why integration with the laboratory system must be designed as a part of the EHR’s quality-reporting architecture. You see, the goal is not to just store lab results, but to make that result computable and traceable from the original test through the final quality threshold.

Scheduling as a Driver of Preventive Care Measures

Receiving the right care at the right time or within a defined time frame is a crucial part of quality reporting. You see, preventive and follow-up care is what can be measured in this, and for that, your EHR’s scheduling system has a huge role to play.

Many preventive care measures require a patient to complete a specific visit, screening assessment, or follow-up within the applicable reporting period. In simple terms, your EHR system needs to track more than just appointments. It should capture whether the appointment was scheduled, completed, canceled, rescheduled, or missed, along with the date of encounter and relevant clinical information.

For instance, if a report requires a follow-up visit within a specific period, scheduling and encounter data can help the EHR identify patients who are due for care. On the basis of this, you can determine whether the required visit occurred and flag outstanding care gaps.

That is why scheduling data can be just as important as clinical data when calculating quality measures. A quality-ready EHR system should connect scheduling, encounters, and clinical documentation so that the system can trace whether a required care event was actually completed and count it appropriately towards reporting.

Reporting Consistently Across Multiple Locations

A multi-location EHR should standardize how quality measures are defined and calculated while allowing individual locations to maintain workflows suited to their operations.

Let’s try to understand this with an example. For instance, a practice has five clinics, and each location has different appointment workflows, providers, or documentation practices. Now, each site calculates the same quality measure differently; combining the results can lead to misleading performance data. That is why your solution needs separate local operations from centralized quality logic.

Here is how it would look:

The architecture shown above allows each location to operate independently and send standardized clinical data to a centralized quality layer. Now, the organization can then compare site-level performance, identify care gaps, and generate consolidated CMS reports using the same measure definitions across all locations.

Long story short, you don’t want every location to operate the same way; instead, generate quality data by making these locations comparable.

APIs for Exchanging and Submitting Quality Data

APIs are an essential component for your EHR system. They provide the connectivity needed to move standardized quality data between your EHR, external healthcare systems, reporting applications, and other components of the quality-reporting workflow.

Now, CMS quality reporting rarely operates within a single application. You see, your EHR system needs to exchange clinical data with laboratories, pharmacies, health information exchanges, registries, analytics platforms, or other healthcare systems before the data reaches the reporting layer. So, without reliable integrations, data can become fragmented or require manual extraction and reconciliation.

Standardized APIs, especially FHIR-based APIs, can help different systems exchange structured healthcare data consistently. Now, the EHR can use these integrations to collect relevant clinical information, synchronize updates, and make standardized data available to downstream quality-reporting components.

A reliable integration layer should include authentication, error handling, data validation, monitoring, and auditability. This helps you ensure that data moving through the reporting pipeline remains complete and traceable.

Certification and Compliance Requirements for Quality Reporting

Building a CMS-ready EHR requires more than quality-measure calculations; the system must also align itself with certification requirements, privacy policies, interoperability requirements, and ongoing regulatory requirements.

Here are some of these certifications and compliance requirements that you need to align with:

Build for Change, Not Just Compliance

CMS quality reporting is not static in nature. Measure specifications, reporting requirements, and interoperability standards can evolve from one reporting year to the next. Now, hard-coding these requirements into the core EHR makes future updates expensive and disruptive.

Here, instead of having separate clinical workflows, terminology mappings, measure logic, and reporting configurations, they are organized into maintainable components. This allows the EHR to adapt to new CMS specifications while preserving the underlying clinical systems.

So remember, your quality-reporting EHR should be compliant today, but architected to accommodate tomorrow’s requirements.

Frequently Asked Questions