What shared fixes could save

Every organisation that moves quality reporting to FHIR meets the same data problems: a code with the wrong label, a result stored as free text, a search that silently returns half the data. Today each one discovers and fixes them on its own. If every ACO, registry and vendor solves the same eClinicalWorks problem separately, the country pays for that fix hundreds of times.

OpenDQM’s idea is simple: solve a problem once, for each EHR product, and share the fix. This model estimates what that is worth. Move the sliders to test your own assumptions.

Who pulls the data from the EHRs?

Each ACO Registries and vendors

Where are the measures calculated?

By each submitter On a national platform

Without shared fixes–

With OpenDQM–

Net avoided cost–

Problem investigations

AI tokens used

FHIR requests per year

Practices connected

Cost per ACO, per year

Who pays, over the period

Who	Without	With OpenDQM

Scope

ACOs

Participating practices (TINs)

Practices on an EHR with FHIR access

Distinct EHR products in use

EHR products per ACO (average)

Quality measures reported

Patients in scope (millions)

Years modelled

Data problems

EHR-wide problems per EHR, per measure

Practice-specific problems per practice, per measure

Hours to solve an EHR-wide problem

Hours to solve a practice-specific problem

Hours to connect one practice

Blended hourly cost (engineering, informatics, QA)

Rework each later year (EHR and measure updates)

AI and data pulls

AI input tokens per problem (millions)

AI output tokens per problem (millions)

Price per million input tokens

Price per million output tokens

FHIR requests per patient, per run

Data pulls per year

Cost per 1,000 FHIR requests

With OpenDQM

Effort to apply a shared fix, vs solving it

Less effort on practice-specific problems

Less effort to connect a practice

Fewer wasted data pulls (paging, retries)

Cost to run the OpenDQM community, per year ($M)

Central calculation (used only when measures are calculated centrally)

Build a national platform, one-time ($M)

Run it: patient-level ingest, validation, support, per year ($M)

Less to build and run, using open engines and shared rules

Rejected-submission problems per submitter, per EHR, per measure, per year

Hours per rejected-submission problem (both sides)

Fewer rejections when submitters pre-check with the same rules

Reconciliation and support hours per submitter, per year

Less reconciliation when everyone uses the same rules

Reset to defaultsCopy link to this scenario

An illustrative scenario, not a forecast

The defaults are assumptions drawn from our own integration work and from public program figures (511 ACOs, 15,353 participant practices and 12.6 million assigned patients in 2026). They have not been measured across the industry. The results are avoided costs across the healthcare system: engineering, informatics, testing and data pulls. They are not savings in program or claims spending.

How the model works

Where the cost comes from

Cost	Without shared fixes	With OpenDQM
Connecting practices	Each practice’s EHR connection is set up from scratch	Shared playbooks per EHR cut the effort
EHR-wide problems	Rediscovered by every organisation, on every EHR it uses	Solved once per EHR product, then applied by everyone else at a fraction of the effort
Practice-specific problems	Each one investigated from scratch	Shared patterns and tools make them faster to solve
Rework in later years	EHR upgrades and yearly measure updates repeat a share of the work	The same share, of a much smaller base
Data pulls	Every run pays for paging mistakes and retries	Known paging and search fixes cut wasted pulls
National platform (central calculation only)	Built and run from scratch	Reuses open engines and shared rules
Rejected submissions and reconciliation (central calculation only)	Each problem found at the platform, sent back, fixed and resubmitted	Submitters pre-check with the same rules; fewer rejections and disputes
OpenDQM community	—	Governance, review, signing and maintenance, counted as a cost

The key difference: per EHR, not per practice

An EHR-wide problem, such as how one EHR labels a code, is the same for every practice on that EHR. Without sharing, it is solved once per organisation per EHR:

ACOs × EHR products per ACO × measures × problems per EHR and measure

With OpenDQM it is solved once per EHR product:

EHR products × measures × problems per EHR and measure

Every other organisation only has to apply and check the shared fix.

Who pulls the data, and where measures are calculated

Two separate choices:

• Who pulls the data from the EHRs. Either each ACO connects to every EHR its practices use, or registries and vendors do it for many ACOs at once (roughly one for every twelve ACOs, each covering up to 20 EHR products). Sharing saves the most when every ACO does it alone.
• Where the measures are calculated. Either each submitter calculates its own results and sends totals, or submitters send patient-level data to a national platform that calculates the measures for everyone.

A national platform does not connect to every EHR itself: the ACOs, registries and vendors still pull the data, so all of their EHR work remains. The platform adds costs of its own: it has to be built and run for millions of patients, it rejects submissions with data problems, and it has to reconcile results with every submitter. Shared, open rules help here too: submitters can check their data with the same rules the platform uses before they send it, and the platform can reuse open engines and rules instead of building its own.

Who pays

The total is a cost to the whole system, not one organisation’s bill. The model splits it:

• ACOs and their practices: setting up each practice’s EHR connection, chasing credentials and approvals, fixing practice-level data problems, and helping clinicians record data in a way the measures can read. When each ACO also pulls the data itself, the EHR-wide work lands here too.
• Registries and vendors: when they pull the data, the EHR-wide problems, the yearly rework and the data pulls.
• The national platform: building and running it, and its half of handling rejected submissions.

Cost per ACO, per year divides the total across ACOs, so it can be compared with what ACOs pay today for quality reporting.

AI tokens

Each problem investigation uses an assumed number of AI tokens for searching, testing and retrying. At today’s prices that is a few dollars a problem, compared with hundreds or thousands of dollars of people’s time. The saving does not come from cheaper AI. It comes from not running the same investigation again.

What would make the numbers stronger

• Counting distinct EHR products and connection points across ACO practices, not just practices.
• Measuring the hours actually spent on each data problem, and how often the same problem recurs across organisations.
• Measuring what share of problems can be fixed safely with a shared, reviewed rule.
• A pilot that tracks real costs, with and without the shared library, across a group of ACOs.