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Savings model

Solve each EHR problem once, for the whole country

Every ACO moving to FHIR-based quality reporting will hit the same EHR data problems. Today each ACO, or its vendor, discovers and fixes them on its own, on every EHR its practices use. If each problem is solved once per EHR product and shared, most of that cost goes away.

This model makes those assumptions explicit so anyone can test them. Change any value and the results update.

—Every organization fixes problems on its own
—With a shared dQMGrid library of fixes
—Avoided cost, net of running the community
Five-year cost by source, $ millions
SourceWithout shared fixesWith dQMGrid
Scale
Effort and cost
Further assumptions

How the model works

Section titled “How the model works”

The base case covers ACOs that each extract and submit their own data, over five years.

  • Connecting practices — practices on an EHR with FHIR access × hours to connect one × cost per hour. Shared connection guides reduce the effort.
  • EHR-wide problems — without sharing, every ACO meets every problem on every EHR it uses: ACOs × EHR products per ACO × measures × problems × hours × cost per hour. With dQMGrid, each problem is solved once per EHR product, and each ACO spends a fraction of that effort applying the shared fix.
  • Practice problems — problems specific to one practice’s build. Shared site-level patterns reduce the effort.
  • Rework, later years — EHRs and measures change, so a share of the problem-solving recurs each year after the first.
  • Data pulls — the recurring work of extracting data, reduced by reusable extraction recipes.
  • dQMGrid community — the cost of maintaining the shared library, counted against the savings.

SI is not the expense. Model tokens cost about $1M of the $309M base case. The saving comes from not repeating the investigation.

Who does the work changes the size of the saving

Section titled “Who does the work changes the size of the saving”

The base case above is the first row. The other scenarios were presented at CMS QualTech 2026 and are shown here for reference; they are not yet adjustable on this page.

Who extracts / who calculates Without With dQMGrid Net saving
ACOs / each submitter $309M $62M $247M (80%)
Registries / each submitter $118M $43M $75M (64%)
ACOs / CMS calculates $486M $161M $326M (67%)
Registries / CMS calculates $215M $107M $108M (50%)
ACOs / submitter, cautious $144M $61M $84M (58%)

A CMS calculation platform adds cost, and adds savings. Submitters still pull data from every EHR, and the platform must ingest, validate and reconcile it. Shared rules let submitters catch problems before they send.

What a cost evaluation would need

Section titled “What a cost evaluation would need”
  1. Direct technology savings — engineering and testing hours avoided, measured in a pilot.
  2. Better measurement — fewer data gaps and consistent results across EHRs.
  3. Program impact — only with an evaluation that links data quality to Medicare outlays.
Proposed next step: a measured pilot with a group of ACOs and their EHRs, tracking real costs with and without shared fixes.

Published estimates, for context

Section titled “Published estimates, for context”
  • CMS estimated about $15 billion in savings over ten years from FHIR-based data exchange in its Interoperability and Prior Authorization final rule (CMS-0057-F, January 2024).
  • The House Ways and Means Committee (H.R. 5347, June 2026) notes that digital quality measures could be up to 95% less expensive to report than traditional measures, with potential national savings of up to $14 billion.

The model above is our own illustrative scenario; these published figures are context, not inputs to it.