Stop Measuring Claims AI by Extraction Accuracy Alone

A tool can extract every field correctly and still create little value for a claims organization. It can capture every diagnosis, treatment date, and billed amount while leaving an adjuster with the same unanswered question: what does this information mean for the claim?

If the output cannot answer a real claims question, show its supporting evidence, or fit into the review process, a high extraction accuracy score says little about whether the investment was worthwhile.

Claims executives need a broader measure of success. Accuracy remains essential, but the evaluation must follow the information through to the work it supports. Does the system help the team find material facts, understand conflicting evidence, and move a claim forward? Does it make verification easier? Can it enable valuable reviews that were previously too expensive to perform?

Those questions reveal far more about the business value of claims AI than a field-level benchmark alone.

That distinction matters because claims AI is increasingly evaluated against high-volume, high-consequence work. A missed exclusion, an unsupported payment, or an incomplete medical chronology can affect reserve decisions, audit findings, customer communication, and litigation strategy. The right benchmark must therefore test whether the technology helps a professional reach a sound, reviewable result, not simply whether it copied text into the correct field.

A useful evaluation begins with the business question. What decision is the claims professional trying to make? Which evidence must be considered? What should the output look like? How quickly can a reviewer validate it? Extraction accuracy remains one component of that evaluation, but it belongs inside a larger framework that measures completeness, reasoning, traceability, consistency, and operational impact.

Why extraction accuracy dominates the conversation

Buyers have good reasons to start with extraction. Many have spent years dealing with handwriting that software cannot read, complex tables that lose their structure, and poor scans that turn straightforward documents into manual work.

A system that misreads an amount or associates a value with the wrong table row can undermine everything that follows. Before considering more ambitious uses of AI, claims teams understandably want confidence that it can interpret the source material.

Extraction accuracy also offers something buyers know how to measure. An expected date can be compared with an extracted date. A set of fields can be checked and scored. The exercise feels familiar and produces a number that is easy to discuss internally.

Evaluating a summary requires more judgment. Reviewers must assess whether it includes the important facts, preserves context, recognizes conflicting accounts, and supports the task at hand. A fluent summary may omit something consequential. A longer summary may contain every detail but create more reading than it saves.

According to Brad Schneider, CEO of Nomad Data:

“Measuring the quality of AI summarization requires more work, and buyers are not as practiced in doing that.”

The danger is allowing the most familiar measurement to define the entire evaluation. Solving the extraction problem should open the door to better claims work. It should not become the finish line.

Claims questions require connected evidence

Consider an illustrative return-to-work review. A provider issues a restriction in a medical note. Another provider later revises it. An employer describes a modified-duty role in an email, and correspondence records the claimant’s response.

An extraction tool could capture every date and statement accurately. The adjuster would still need to establish which restrictions applied at the relevant time, whether the offered role aligned with those restrictions, and whether the records agreed about what happened next.

That work depends on relationships across documents. It requires chronology, context, and an understanding of the review question.

Claims files routinely distribute important evidence across medical records, reports, forms, correspondence, and internal notes. One document may qualify a statement in another. A later assessment may change the interpretation of an earlier finding. Repeated copies of a record may add volume without adding evidence.

The useful output brings those relationships into view. It identifies what the documents support, where they conflict, and what remains unresolved. The claims professional can then apply judgment with a clearer understanding of the file.

Extraction supports that process. Measuring extraction alone leaves the most valuable part of the work unevaluated.

The human answer sheet can be wrong too

Most proofs of concept begin with a reasonable approach: experienced reviewers compare AI outputs with work they have already completed. Their summaries, timelines, and answer sheets become the benchmark.

But that benchmark also needs scrutiny.

Schneider describes a recurring experience in Nomad’s proofs of concept. Buyers begin by assuming their reviewers have produced the correct answers and checking the AI for mistakes. During source verification, they discover an error in the existing answer sheet: an incorrect date, an overlooked fact, or a conclusion that does not account for evidence elsewhere in the file.

The AI’s citation allows the team to inspect the discrepancy directly.

According to Brad Schneider, CEO of Nomad Data:

“We see this all the time. The file was so large or involved so many files.”

That observation is not an argument for accepting AI answers automatically. It is an argument for grounding the evaluation in evidence. When the AI and the answer sheet disagree, the reviewer should examine the source and determine which account is supported. Sometimes the AI will be wrong. Sometimes the benchmark will need correction. Sometimes the evidence will remain ambiguous.

An evaluation that scores every disagreement as an AI error can penalize the system for finding something useful. It can also preserve the very omissions the insurer hoped the technology would help address.

The benchmark should represent the best verified understanding of the file, with a process for resolving discrepancies. Human expertise is essential to that process, especially when a finding requires interpretation rather than a simple factual correction.

Citations make accuracy usable

A correct answer without a practical route to its source can still create considerable work.

If an AI summary identifies a changed diagnosis or conflicting injury description, the adjuster needs to inspect the supporting language. Without precise references, that means searching through the file again. Some of the promised efficiency disappears during verification.

Useful citations shorten that step. They take the reviewer to the relevant document and passage, where the wording and surrounding context can be checked.

Citation quality therefore belongs in the evaluation alongside answer quality. Does the cited material support the statement? Does the output distinguish a claimant’s report from a provider’s observation? If records conflict, does it show both accounts?

The presence of a citation does not prove a conclusion is correct. Its value is that it makes the conclusion inspectable. Claims teams can challenge an answer, correct a mistake, and document the basis for their judgment without reconstructing the entire search.

This is also how confidence develops. Trust grows through repeated, successful verification on real work.

A stronger evaluation measures the complete task

Claims leaders should define success around a specific review task before comparing vendors. A payment audit, medical chronology, and claim summary serve different purposes and need different acceptance criteria.

The assessment should cover several distinct dimensions:

• Material facts and completeness. Does the system find the information needed for the task across the supplied file? Important omissions should carry more weight than cosmetic differences in presentation.
• Analysis and consistency. Can it answer specific claims questions, connect related facts, identify meaningful conflicts, and flag missing support? Repeated runs should preserve the underlying facts even when wording changes.
• Verification effort. Can reviewers quickly inspect the evidence behind important findings? Measure the time spent checking and correcting outputs, including unsupported statements and misleading citations.
• Workflow performance. Compare the total effort required to prepare documents, produce the output, review it, resolve questions, and record the result. Include manual review hours, rework, and time to the next decision or action.
• Adoption. Do the people doing the work use the system consistently? Can they obtain useful outputs in their required format without extensive prompting or reformatting?

These measures should be considered together. Faster generation has limited value if the output requires extensive repair. A technically accurate response may still fail operationally if it leaves the reviewer to assemble the actual work product.

Human oversight should remain explicit. As performance is demonstrated, the workflow can increasingly direct expert attention toward judgment, material findings, and exceptions. If staff must repeat the entire original review on every file, the expected efficiency has not yet been achieved.

How to build a claims AI scorecard that reflects business value

A practical scorecard translates a broad promise such as faster claims review into observable measures. It should be designed before the pilot begins, using representative files and a clearly defined task. Otherwise, teams risk choosing metrics after seeing the output, which makes comparisons less reliable and encourages attention to whatever the system happens to do well.

Start by selecting a use case with a real decision or work product at the end. Examples include preparing a medical chronology, checking whether a payment is supported, identifying missing documentation, comparing injury descriptions, or drafting a claim summary. The test set should reflect the volume, document types, scan quality, and complexity the production team actually encounters.

Next, create a weighted rubric. A wrong date may be important, but a missed surgery, conflicting diagnosis, unsupported reserve change, or overlooked coverage fact can be much more consequential. Weighting material findings more heavily prevents a long list of correctly extracted low-value fields from masking a critical omission.

Then separate answer quality from evidence quality. Reviewers should score whether the answer is complete and accurate, whether every material statement is supported, whether the citation leads to the right passage, and whether conflicting evidence is represented fairly. This approach also creates a disciplined way to resolve disagreements between the AI output and the original human review.

Finally, measure the entire path from file intake to completed work. Record preparation time, generation time, validation time, corrections, escalation rates, and downstream rework. Compare those measures with the current process. A pilot that saves ten minutes in generation but adds twenty minutes of verification has not improved the workflow.

The scorecard should also include user feedback, but it should ask specific questions. Did the output reduce searching? Did the format match the reviewer’s needs? Which findings required correction? Would the reviewer use the output on a similar file? Specific responses are more actionable than a general satisfaction score.

Better performance changes what buyers ask for

When buyers see AI uncover a documented fact that their own review missed, the conversation often changes.

According to Brad Schneider, CEO of Nomad Data:

“It usually flips a switch in the buyer’s head.”

Buyers begin to recognize that AI can perform substantial parts of the summarization work they already do. They then start asking what else becomes possible. This introduces another measure of value: the useful work an organization can undertake once document review becomes less expensive.

An insurer may already know which checks would improve oversight. The constraint may be the staff time required to apply them. A pilot focused entirely on reproducing the current process can miss that opportunity.

Two areas make the difference especially clear: claims auditing and fraud screening.

For teams evaluating document summarization AI for claims review, this is the point where a pilot can move beyond a generic summary. Ask the system to produce the actual chronology, comparison, or finding the claims team needs, then score the result against the evidence and the workflow requirements.

Broader audits and fraud screening expand the opportunity

Claims audits are often constrained by the economics of review. Reading the file, checking decisions against defined criteria, examining payment support, and documenting findings can require substantial expert time. Teams may consequently review a limited sample even when they would prefer broader coverage.

AI can make it practical to apply defined checks across a much larger claim population, potentially every claim within a configured workflow. An insurer could examine documentation completeness, payment support, decision consistency, and other criteria across the same files, then route findings for human validation.

The business benefit includes both lower effort per review and greater coverage. Leadership can ask how many more claims were checked, which material issues were found, and whether validated findings led to corrections or process improvements.

Fraud screening presents a similar opportunity. Schneider describes a familiar referral pattern: a handler notices something questionable and escalates the claim to a special investigation unit. The time and expense of deeper investigation limit how many files receive that attention.

AI can help apply an insurer’s relevant red-flag checks across a broader population before a handler has independently identified a concern. It can surface conflicting narratives, unusual documentation patterns, or discrepancies that warrant follow-up, with evidence attached for review.

A flag is not proof of fraud. Investigation and judgment remain essential. The operational question is whether broader screening produces more useful referrals without overwhelming investigators with low-value alerts.

For both audit and SIU teams, success should include the quality and usefulness of the additional review. Counting more flags is insufficient. The insurer needs to measure which findings survive validation, what action follows, and how much effort the expanded process requires.

None of those outcomes is captured by the percentage of fields extracted correctly.

Run a pilot that tests real claims work

The most credible claims AI pilot uses a controlled sample of real, appropriately handled files and asks the system to complete a defined task. A balanced sample should include straightforward files, difficult scans, long multi-document matters, conflicting records, and examples with known exceptions. If the test contains only clean, predictable documents, the results will say little about production readiness.

Create the review team before running the files. Include experienced claims professionals, operational leaders, and any legal, compliance, security, or data stakeholders who will influence adoption. Agree on the rubric and the escalation process in advance. When reviewers disagree, they should inspect the cited source, document the resolution, and update the benchmark when necessary.

For medical-heavy claims, compare whether the output captures diagnoses, treatment events, restrictions, gaps, and changes over time. A strong medical records summary should reduce the effort required to understand the clinical story while preserving the evidence a reviewer needs to verify each material point.

For quality assurance, test whether the system can apply consistent criteria across a broader population. A well-designed claims audit evaluation should measure the validity and actionability of findings, not merely the number of issues flagged. For special investigations, examine whether insurance claims red flags are supported by the file, prioritized appropriately, and useful enough to justify follow-up.

Report pilot results in business terms. Useful measures include review hours per file, percentage of material findings supported by precise citations, correction rate, time to completed work product, number of additional files reviewed, and percentage of validated findings that led to action. Present the extraction score as one diagnostic measure within that broader result.

A pilot should also define what happens next. Set acceptance thresholds, identify workflow changes, assign owners for exceptions, and determine which file types need continued human review. This turns the evaluation from a product demonstration into an operating decision.

Evaluate the claims workflow the organization wants to achieve

At Nomad Data, we approach document analysis through the work the claims team needs to complete. That includes analyzing complete files, answering specific questions with citations, and producing outputs tailored to the team’s review process.

We use Doc Chat to connect document interpretation with summaries, comparisons, timelines, and structured findings that professionals can verify and use. The value should be demonstrated on the insurer’s own files and measured through the resulting workflow.

Claims leaders should keep demanding strong extraction performance. Handwriting, tables, and poor scans remain real obstacles. They should also require vendors to demonstrate what happens after those obstacles are addressed.

Can the system improve a claims decision or workflow? Can the team verify the evidence behind its answer? Can it make worthwhile review possible across more of the business?

Extraction accuracy is table stakes. The better measure of claims AI is whether it turns complex files into faster, more consistent, and defensible decisions, while expanding the organization’s ability to examine the claims that need attention.

With Doc Chat for Insurance, teams can ask questions across complete claim files, generate structured outputs, and trace material findings back to cited evidence. That combination helps organizations evaluate the work their professionals actually need to complete, not only the accuracy of isolated fields.

Explore Doc Chat for Insurance to see how complete-file analysis and cited answers can support faster, more consistent, and defensible claims workflows.