Why Medical Record Review Shouldn’t Restart When New Records Arrive

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New medical records can come in months or years after a claim opens, so a summary that was written in March may be out of date by July.

Records come from hospitals, treating providers, legal teams and late requests for pre-accident records, and older records are often mixed in with new evidence.

Whether your medical record review runs on AI, people or both, the process should answer one question: What’s new since I last looked?

Say another 300 pages are added three months after the initial review. The adjuster should be able to see what’s new without going back through the whole file and starting over.

For claims managers looking at new technology and existing processes, it’s worth testing how the review holds up after several rounds of new records to see whether earlier work carries forward or the process starts over each time.

The cost of repeated medical record review

Some adjusters rebuild the summary from scratch each time records come in. Others read what looks new and rely on recollection for the rest.

Take a soft-tissue claim after a rear-end collision, for example. The initial records package holds the emergency department visit and three months of family physician notes.

Months later, the claimant’s counsel produces another 300 pages of physiotherapy notes, an MRI report and an orthopedic consultation. The new package also repeats the original emergency department records and many of the physician notes, now copied from the clinic’s chart.

Nothing in the new package marks which pages are repeated. The adjuster has to separate new records from duplicates, then work out where the new evidence fits in the existing timeline and whether it changes the understanding and outcome of the claim.

Generic AI tools vs. purpose-built medical record review

AI can process large volumes of medical documentation, but not every tool is designed for a file that changes over time.

General-purpose AI assistants can help summarize medical records. But a chat-based workflow doesn’t necessarily keep a structured record of which pages have been reviewed from one batch to the next. Even when earlier documents stay accessible, the workflow may not compare each new batch against previously reviewed records or distinguish duplicates from new records.

Large files may also be processed in stages, and a generated summary can read well without keeping an updated chronology by date of service. Repeated runs can differ in wording or context, and material findings need to stay consistent and verifiable against the source document.

Purpose-built medical record review systems may offer capabilities such as cross-batch indexing, duplicate detection, an updated chronology and links from material findings to source pages. What matters is whether those capabilities work together as new records arrive, so relevant work from earlier reviews carries forward.

How to test a claim review process with new records

Start with a claim that has already been reviewed. Add a new batch of records that mixes documents already on file with new evidence, including at least one record that could change how earlier evidence is read.

. Then check five things:

1. Does the tool identify records from earlier batches, including copies that differ in scanning, formatting or source?
2. Can the adjuster identify new records and quickly understand what new information they contain?
3. Does the chronology incorporate the new evidence, and does the updated summary reflect it without losing relevant context?
4. Are earlier notes, corrections and review history retained, with a clear distinction between earlier conclusions and new ones?
5. Can the adjuster trace each material medical finding to its source page and verify it in context?

If any answer depends on the adjuster’s memory, the file isn’t carrying information forward.

When a workflow handles all five, the adjuster can easily see what changed, such as an MRI report or an orthopedic opinion, and revisit earlier records only where the new evidence calls for it.

The tool’s job is to surface that evidence and show where it fits. Deciding whether it changes causation or work capacity stays with the adjuster.

Why this matters for claims leaders

For claims leaders, the risk is that the organization’s understanding of a claim drifts as evidence accumulates and responsibility shifts between people.

When that happens, an incoming adjuster can’t tell which records were already reviewed or why an earlier conclusion was reached. A supervisor can’t separate new evidence from what was already considered.

A consistent review process helps make the file a reliable record of the work, so continuity depends less on the person who last reviewed it and the file is easier to manage when responsibility changes hands.

A repeatable standard also lets claims leaders set the same expectations across adjusters, teams and offices for how new evidence is added and how material findings are documented.

The next time your team evaluates a medical record review tool or its own process, don’t stop at the first upload. Add a batch that mixes duplicates with new evidence, and see whether the adjuster can identify what changed, check it against the source records and update the file without repeating work already done.

On a live claim, the first summary is only a starting point. The real test is whether the fifth batch makes the adjuster’s job easier or sends them back to the beginning.

Authors:

• Rob Forsythe – GM, US Insurance
• Margaret Leibovic – CTO
• Amir Tavafi – CAIO