The chronology looked clean. Every entry had a date, a time, a provider, and a tidy one-line summary. The software had worked through 6,400 pages of hospital records overnight, and the attorney was pleased with how fast it came back.

Then the legal nurse consultant opened the nursing flow sheets for the night of the fall.

The chronology said the patient was “resting comfortably” at 0200. The next entry jumped to the morning physician rounds. Missing from the summary was a handwritten, poorly scanned nursing note at 0215: “Patient found on the floor beside the bed, bed alarm not activated.” The single most important event in the case was not in the chronology at all.

That scenario is a composite, but every LNC who has worked with machine-generated chronologies will recognize it.

How AI is Being Used

AI-assisted review of thousands of medical-record pages is an increasingly practical tool for malpractice firms. That matches what many of you are hearing from attorneys. Some firms already run records through these tools before an LNC ever sees the file.

The speed is real. A tool that sorts, dates, and summarizes a sixty-five hundred-page record in hours has obvious appeal to a firm with a heavy caseload.

But speed and accuracy are different things. A chronology is only useful if the attorney can rely on it when preparing an expert, outlining a deposition, or deciding whether a case is worth taking. When a generated chronology leaves out events, invents details, or strips away the context around a clinical decision, the error travels. It shows up in the expert’s report who unwisely relies on it rather than reviewing the records. It shows up in a demand letter. Sometimes it shows up in front of opposing counsel.

As the efficiencies of AI directly impact people’s jobs, both positively and negatively, AI offers some advantages in handling medical records. AI software proliferated in the last few years. I’ve seen several programs that can sort through extensive medical records to assemble records in a precise order, integrating the records of various providers into one flowing chronology. The ability to find keywords, assemble data into visual displays, and hyperlink the output to the original medical record is useful.

And yet, we encounter attorneys who think that they do not need the analytical skills of a nurse. Using a machine to replace a legal nurse consultant is shortsighted. AI has never put on a uniform and cared for a patient in a hospital. The analytical thinking of a human, which is where legal nurse consultants excel, enables us to spot discrepancies, note missing information, and understand the implications of chart entries. In a world where AI has replaced some jobs, there are risks in assuming AI can replace the insights an experienced LNC offers.

Medical issues can be too complex to reduce to a software-generated summary. With our specialized training and perspectives, LNCs bring medical insights to help the attorney understand the medical records and what occurs in healthcare environments. We understand how the healthcare system works, making us invaluable adjuncts for attorneys.

AI-based software can generate medical record review errors. Here are five errors to watch for, and how to find them.

1. It leaves events out

Omission is the error attorneys are least likely to notice, because you can’t see what isn’t there. A chronology that skips an event looks just as polished as one that doesn’t.

AI tools tend to miss content that is hard to read or hard to parse:

• Handwritten notes, faxed pages, and low-quality scans
• Rotated or partially cut-off pages
• Grid-format documents such as flow sheets, medication administration records, and intake and output records
• Records split across several productions or facilities
• Pages the tool flagged as duplicates when they were actually different versions

Summaries also compress. “Uneventful night” may stand in for a shift that included three calls to the on-call physician.

How LNCs catch it

Start by asking what should be in the record. You know that an inpatient admission produces nursing notes, flow sheets, a MAR, physician orders, progress notes, and lab results. A surgical case adds anesthesia records, operative notes, and PACU documentation. If the chronology has no data from a record type you’d expect, find out why.

Then compare page counts against the production, and read the critical timeframe yourself, line by line. The hours around the alleged incident deserve your eyes, not a summary.

2. It invents details

Large language models can produce statements that sound right but don’t appear anywhere in the record. A blood pressure that was never charted. A medication dose that differs from the order. A provider’s name attached to a note someone else signed. A line stating “patient education provided” because that phrase commonly appears in similar records.

These medical record review errors and fabrications are dangerous precisely because they read like real documentation. Nothing about them looks wrong. If an invented detail supports the attorney’s theory, nobody has a reason to question it until the defense does.

How LNCs catch it

• Require a page or Bates citation for every entry. An entry without a source is an entry you can’t trust.
• Verify every entry that bears on liability, causation, or damages against the original page.
• Be wary of entries that sound like textbook language rather than the way clinicians actually chart.
• Check provider names and credentials against the signatures on the source documents.

3. It strips the context from clinical decisions

Consider an entry that reads: “Heparin held at 1400.” Out of context, it looks like a missed dose. Two pages later, the hospitalist’s progress note documents a falling hemoglobin and dark, tarry stools. The decision to hold the heparin was sound clinical judgment.

The reverse happens too. A tool may faithfully summarize a physician’s rationale while missing nursing notes from the same hour that contradict it.

LNCs use clinical judgment to inspect the relationships between entries: the assessment, the notification, the order, and the patient’s response. AI tools tend to summarize one entry at a time. They report what was done without capturing why, or what the clinician knew at that moment.

How LNCs catch it

For each pivotal decision in the case, trace the chain. What did the nurse or physician know at that point? What was documented before the decision, and what happened afterward? Was the provider notified, and did the response match the urgency of the finding?

This is the work software does poorly and experienced nurses do well. You have made these decisions yourself at the bedside. You know what a reasonable clinician would have been weighing.

4. It gets the sequence wrong

Timing drives many malpractice cases. When did the patient’s condition change? When was the physician called? How long before anyone acted? A chronology that puts the notification before the deterioration tells a different story than the record does.

Sequence and medical record review errors creep in from several directions:

• Charting time that differs from the time the event occurred
• Late entries and addenda written hours or days later
• Confusion between 12-hour and 24-hour time
• EMS, emergency department, and inpatient records kept on different clocks
• Monitors and devices whose internal clocks were never synchronized with the EHR

How LNCs catch it

Identify late entries and addenda by their labels and note both the documented time and the time of the event. Compare timing across sources: the EMS run sheet, triage note, monitor strips, and nursing notes. When sources disagree, flag the conflict for the attorney rather than  picking one. A timing discrepancy may turn out to be one of the most useful findings in the file.

5. It treats copied or templated text as fresh findings

Electronic health records make it easy to carry text forward. You’ve seen it: “Lungs clear bilaterally” documented for five consecutive days while the patient was on BiPAP. Default checkboxes, normal-findings templates, and smart phrases fill notes with language that may not reflect an actual assessment.

An AI tool has no way to know the difference. It reads copied text as fact, and the chronology reports a stable patient. When nursing and physician documentation conflict, the tool may simply pick one version and move on, establishing a medical record review error.

Dig deeper into medical records with our free report, which will give you practical tips. Get our free report, 9 Secrets of Analyzing Medical Records.

Get tips in our free report on analyzing medical records at this link. 

How LNCs catch it

• Watch for identical wording repeated across shifts or days.
• Compare narrative findings against objective data: vital signs, oxygen requirements, labs, and imaging.
• Report documentation conflicts to the attorney in plain terms. Conflicts are often where the legal team can prove the validity of the case.

What this means for your LNC business

These tools change what attorneys need from us, and the need is growing. It is your job to educate your clients that medical record review errors can occur with AI, that they cannot happily rely on the accuracy of the summaries, and that you can verify them for your client.

A firm that runs records through software still needs someone who can tell whether the output is accurate. That calls for clinical knowledge, an understanding of how records are created, and the judgment to know which entries matter. Those are LNC skills.

Consider offering chronology verification as a defined service. You might describe it to attorneys this way:

“I review AI-generated chronologies against the source records and flag omissions, unsupported entries, timing conflicts, and missing clinical context before you rely on them.”

That is a clear, specific offer. It addresses a risk attorneys may not have thought about yet, and it positions you as the professional who protects the quality of their work product and the attorney’s decisions.

If you use AI tools in your own practice, hold yourself to the same standard. Verify what the tool produces before it reaches the attorney. Know where the records go when you upload them and confirm that the vendor’s privacy and security terms are appropriate for protected health information. Be prepared to explain your process if an attorney asks how a chronology was built.

The bottom line

AI can sort and summarize records faster than any of us. It can’t sit with a flow sheet and notice that the bed alarm wasn’t on. It can’t recognize that a withheld medication was the right call. It can’t tell that “lungs clear” was copied forward from Tuesday.

You can. Your clinical eye is what attorneys pay for, and it becomes more valuable as more records pass through software first.

Keep building the skills attorneys rely on

The 15th LNC Success Online Conference runs March 11–13, 2027. You’ll spend three days learning from experienced legal nurse consultants about building a business attorneys trust and the services that keep them calling. Join us. Register here.

Pat Iyer MSN RN LNCC is president of The Pat Iyer Group. She has almost forty years of experience developing resources to assist LNCs in obtaining more clients, making more money, and achieving their business goals and dreams.

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