Medical Chronology Template: The Field Schema, the Columns Most Templates Omit, and How to Handle the Records

Every medical chronology template on the internet gives you the same seven columns. Date, provider, facility, complaint, findings, treatment, page reference. Those columns are correct, and they are not where chronologies fail. Chronologies fail on the things the standard template has no column for: the record that is missing, the date that two providers report differently, the gap in treatment nobody flagged, and the page you cannot cite because the production was scanned at an angle.

This is the format we use, the reason each field exists, and the four columns we added after watching what reviewers actually annotate in the margins.

Key takeaways

The medical chronology template

Copy this structure directly into a spreadsheet. Column widths matter less than column discipline: every row is one dated event from one source document, and nothing goes in the table that cannot be pointed at.

Fields 1–7 are the standard template. Fields 8–12 are the four we added, plus the citation field promoted out of the footnotes where most templates leave it. Every one of them exists because a reviewer was writing it in the margin anyway.

A worked excerpt

Two rows from a rear-end collision matter, with identifying details removed. Note that row 2 does not agree with row 1 about the onset date, and that the disagreement is recorded rather than resolved.

Three things are now visible that a seven-column template would have hidden: the onset conflict, the fact that the conflicting entry is handwritten and therefore worth verifying by eye, and a 22-day gap against a 7-day follow-up instruction. None of those is an opinion. All three will come up.

Facts here, opinions elsewhere

The chronology is a factual index. The analysis — causation theory, damages narrative, criticism of care — belongs in a separate memo that cites the chronology by row number. We are strict about this for a practical reason: a chronology containing argument becomes attackable as advocacy, and once one row is characterized as slanted, every row is in play. Keep the table boring. Let the memo be persuasive.

This is also why field 5 is verbatim. “Patient reports severe, unremitting pain” and “patient reports pain” are different exhibits, and the reviewer does not get to decide which one the record said.

How to build one from a production

What changes by matter type

The part nobody puts in a chronology template: where the records live

A medical record in a law firm's possession is protected health information. Depending on the posture, a firm may be a HIPAA business associate — that is squarely the case when the firm works for a covered entity — and where records arrive under a patient authorization or a qualified protective order under 45 CFR § 164.512(e), the obligations arrive through that order, state privacy law, and professional-responsibility rules instead. The label changes. The sensitivity of the file does not.

Which means the tool you use to build the chronology is a data-handling decision, not just a productivity decision. Three questions worth asking any vendor, in writing:

Hathr.AI answers those three the same way for a two-person firm as for a national practice: a signed BAA on every HIPAA-compliant plan, accepted electronically at signup; Anthropic's Claude models running inside AWS GovCloud under a FedRAMP High authorization boundary; and customer data never used for model training or product development, and never routed across the commercial internet. Record sets of up to 100,000 pages are processed in a single workflow, and the OCR reads scanned and handwritten pages that text-only tools skip — which is exactly the material that ends up flagged “partial” in field 10.

Hathr surfaces what the record says. A human decides what it means. Nothing in this template, and nothing in the platform, substitutes for the reviewer's judgment or an expert's opinion.

Build a chronology from one record production in Hathr.AI — free for 7 days →

Methodology and measured accuracy

This template is the format we use and recommend; the four added fields come from watching what reviewers annotate manually when a seven-column template is all they have. That is a design rationale, not a study.

What the measurement shows. Hathr.AI's record-review output has been measured as high as 97% accuracy — not in a lab, but in production, across live records-review work running at more than 100,000 records per month. The remaining 3% is not a set of wrong answers. Those are the cases where Hathr stopped and asked for more information before completing the task, rather than filling the gap itself.

On a chronology, that distinction is the entire point. A tool that quietly guesses at an illegible onset date is more dangerous than one that flags it and asks — which is why field 10 exists, and why the 3% belongs in the same sentence as the 97%.

What this figure does not do is compare Hathr against another vendor's output on the same production. It is a measurement of our own work in our own production environment, and we report it as exactly that.

Accuracy measured in Hathr.AI production environments during live records-review work at a volume exceeding 100,000 records per month. Last verified: August 2026.

Frequently asked questions

What is a medical chronology?

A medical chronology is a dated, cited index of every treatment event in a set of medical records, built so that any entry can be traced back to a specific produced page. It is a factual document. Analysis of what the treatment means belongs in a separate memo that references the chronology by row.

What should a medical chronology template include?

At minimum: date of service, provider, facility and setting, presenting complaint, objective findings, assessment, treatment plan, and a page citation. The four fields most templates omit are source document ID, legibility confidence, conflict flag, and gap to next treatment.

How do you prepare a medical chronology?

Inventory the production first, extract dates and page citations in a single early pass, summarize provider by provider, sort into date order, verify only the entries flagged as partial or illegible, then compute treatment gaps after supplemental records have arrived.

How accurate is AI medical record review?

Hathr.AI's record-review output has been measured at 97% accuracy in production, across live records-review work running at more than 100,000 records per month. The remaining 3% is not wrong answers — those are cases where Hathr asked for more information before completing the task rather than filling the gap itself.

Is it safe to use AI to summarize medical records for litigation?

It depends entirely on the vendor's data handling. Medical records are protected health information regardless of who holds them. Ask whether the vendor signs a BAA, where the records are processed, and whether your data is used for model training. Hathr.AI signs a BAA on every HIPAA-compliant plan and processes records inside AWS GovCloud under a FedRAMP High boundary.

Should the chronology include the reviewer's opinion?

No. A chronology containing argument becomes attackable as advocacy, and once one entry is characterized as slanted the whole table is in play. Keep interpretation in a separate memo that cites chronology rows by number.

What is the difference between a medical chronology and a medical summary?

A chronology is ordered by date and every row is cited to a page. A summary is ordered by theme and is written to be read. Most matters need both, and the summary should be built from the chronology rather than from the records directly.