Why Your Abstractors Spend More Time Searching Than Reviewing (And What to Do About It)

If you’ve ever submitted a quality measure and then wondered later whether your abstraction would hold up under a CMS audit, you already understand the problem.

It’s not that the evidence isn’t there. It’s that finding it takes too long. And by the time your team surfaces it, the window to act has already closed.

After working with more than 1,800 healthcare organizations, we’ve seen this pattern repeat regardless of hospital size, EHR system, or staffing model. Quality teams don’t struggle most with understanding measure logic. They struggle with finding the evidence required to apply it.

Why This Problem Is Getting Harder Right Now

Chart-abstracted measures have always been expensive. But the stakes have changed.

Four forces are making the evidence problem harder than anything quality teams have dealt with before:

1. Validation scrutiny is rising. CMS runs quarterly re-abstraction audits. When results diverge from submitted data, consequences range from score adjustments to payment impacts to mandatory remediation plans.
2. Reimbursement exposure is growing. SEP-1 is now in the HVBP safety domain. Abstraction accuracy is no longer just a quality team concern. It’s a CFO concern.
3. The workforce isn’t recovering. Experienced abstractors are hard to hire and harder to keep. When someone leaves, they take institutional knowledge with them.
4. Executive scrutiny is intensifying. Quality performance is visible at the board level. Leaders need to explain score movements with faster, more defensible data than manual abstraction can produce.

The evidence problem was manageable when quality was primarily a compliance exercise. It’s much harder when quality determines reimbursement, accreditation, and public reputation all at the same time.

What Retrospective Abstraction Actually Costs

A 2023 JAMA study at Johns Hopkins found that chart-abstracted measures cost $33,871 per metric per year in personnel alone. Electronic measures? $1,902. That’s an 18-fold difference, and it has nothing to do with measure complexity. It’s about where the evidence lives and how hard it is to reach.

But the personnel cost is only the visible part.

The invisible costs are just as significant:

Why Structured Data Can’t Solve This

eCQMs delivered on their promise, for the measures they cover. Labor dropped. Cost per metric fell dramatically.

But some questions can’t be answered with structured data. Consider the difference:

Was a lactate drawn? Structured data answers that in seconds.

Was a lactate drawn within three hours of the moment a patient first met severe sepsis criteria, on a case where three physician notes contradicted each other over six hours? That requires reading the chart.

The first question is an eCQM. The second is SEP-1, the only chart-abstracted clinical process measure required for CMS IQR in 2026, now in the HVBP safety domain. Trained abstractors agree on SEP-1 time zero in fewer than 40% of cases. That one disagreement cascades to every downstream bundle element. Two abstractors. Same chart. Wildly different compliance outcomes.

The measures staying chart-abstracted longest are exactly the ones where clinical narrative can’t be replaced. The evidence problem isn’t going away. It’s concentrating.

Does This Sound Familiar?

We hear a version of the same story from quality teams across the country.

A team of two or three people. Abstracting only the measures required for CMS reporting. Sampling where regulations allow it. Staying current, barely, by accepting every accommodation available. And calling that a success, because by the standards the field has set, it is.

But consider what stays hidden. The documentation patterns that won’t surface until a validation request arrives. The institutional knowledge that walks out the door when someone leaves. The bar that has quietly dropped to clearing the minimum.

That’s what a decade of unsolved retrospective quality looks like from the inside.

What Changes When Evidence Isn’t Retrospective

AI-assisted abstraction separates the retrieval problem from the judgment problem.

1. AI handles the search.
2. The abstractor handles the evaluation.
3. Evidence surfaces before the reviewer opens the chart.
4. Ambiguous findings get flagged, not buried.
5. Documentation gaps become visible while there’s still time to act.

The result isn’t just faster abstraction. It’s earlier visibility into performance, more consistent results across reviewers, less validation risk, and abstractors doing the work only they can do.

One thing matters more than speed: defensibility. An AI-suggested value you can’t trace back to its source document isn’t an improvement over manual abstraction. It’s a different kind of risk, one that goes undetected until CMS or TJC decides to check.

The right model is AI that surfaces evidence, links every suggestion to its source document, flags uncertainty rather than papering it over, and keeps the abstractor in control of the final decision. The audit trail gets built before submission, not assembled after a challenge arrives.

How Medisolv Is Building This Into Your Workflow

Medisolv has supported quality reporting across more than 1,800 healthcare organizations for more than 25 years. Through our acquisition of Health Elements AI, we’re building AI-assisted abstraction directly into the workflows quality teams already use.

Give Your Abstractors More Time to Apply Their Expertise

Your abstractors know what to look for and how to interpret it. Finding the right documentation and preparing responses can still consume time they need for complex cases, validation, and improvement work.

Medisolv’s AI-assisted chart abstraction tools bring relevant clinical evidence and suggested responses forward, helping your team reduce repetitive preparation while keeping expert judgment central.

ENCOR for Chart Abstraction

Add AI-assisted chart abstraction to supported hospital abstraction workflows in ENCOR. Abstractors review relevant evidence and suggested answers, make corrections, and finalize responses within an established abstraction and reporting environment. Learn more.

ENCOR for Clinical Registries

Help registry teams work more efficiently with AI-suggested field values and supporting documentation ready for review. Experts confirm or correct the information before finalizing records for applicable reporting and submission workflows. Learn more.

Expert Abstraction Services

Strengthen your team’s capacity with expert support for agreed abstraction work. Explore help with staffing gaps, ongoing workloads, or an alternative to your current outsourced arrangement. Learn more.

Across these offerings, Medisolv connects abstraction tools and support with experience in quality measurement, reporting, and improvement. AI assistance applies to supported measures and data elements; your experts remain responsible for final abstraction decisions.

Put Your Team’s Expertise to Work

Less time searching creates room for the work that needs professional judgment: investigating discrepancies, resolving documentation gaps, and working with clinicians on improvement.

Give your abstractors the tools and support to spend more of their day applying their expertise.

Schedule a time to chat to see how Medisolv can help your team reduce abstraction burden and support confident review.