AI medical chronologies for personal-injury solicitors
Prepare an AI-assisted medical chronology with source pages, event and note dates, attributed observations and a clear record of solicitor corrections.
A medical-record chronology can help a personal-injury team locate relevant material. Its value depends on preserving what each source says, including uncertainty, rather than turning a collection of records into an apparently settled clinical account.
A proposed AI task is to index the records and extract dated entries for review. Diagnosis, prognosis, causation, valuation and legal advice remain matters for the relevant professionals.
Register the records before summarising them
Record the provider, document identifier, version, page count, receipt date and reported coverage period. Keep the original files. An apparent gap in a date range is a question for review, not proof that treatment did not occur.
Health information is special category personal data. The ICO describes this broadly, including medical history, opinions, diagnosis and treatment. Its guidance is under review following the Data (Use and Access) Act. The firm must determine the applicable processing conditions and safeguards for the proposed use. ICO: special category data.
Use the supplier confidentiality review before processing live records in a new service. A synthetic demonstration should contain invented material, not a real patient's record with only the name removed.
Preserve the distinction between an event and a note
| Source feature | Proposed extraction | Reviewer checks |
|---|---|---|
| Appointment and later transcription dates | Separate date fields with original wording | Which date describes the event |
| Reported symptom | Attributed account with page reference | Who reported it and in what context |
| Clinical opinion | Exact or clearly attributed wording | Meaning and whether qualifications remain visible |
| Unreadable handwriting | Exception without invented text | Whether manual transcription is needed |
| Repeated or corrected record | Linked versions retained | Which version is appropriate for the task |
These are suggested review fields, not a medical interpretation method. Do not let the model expand an unclear abbreviation or resolve a conflicting entry without making the uncertainty visible.
A chronology entry must retain qualification and attribution
In a fictional record, a note made on 8 June says the patient reported pain beginning on 4 June. Store the note date and reported onset date separately, link to the page and identify the statement as patient-reported. Do not convert it into a clinician-confirmed onset date.
Use separate fields for source wording, proposed summary and reviewer correction. “No fracture seen” must not become “fracture”; “possible” must not disappear when a summary is shortened. Unclear abbreviations and handwriting remain unresolved until checked.
This output specification gives a personal-injury team a practical demonstration task for an AI medical chronology product. It does not evaluate any named supplier or make a clinical finding.
Build a chronology with a review trail
- 01Original record
- 02Attributed extraction
- 03Uncertainty retained
- 04Professional review
- 05Correction recorded
- 06Reviewed chronology
Each row should open the original page. Keep extracted wording separate from the proposed summary and any staff correction. Show whether the entry is pending, corrected, accepted or rejected.
If the source cannot be opened, hold the entry. If the source is replaced, show which reviewed outputs depend on the old version. A corrected record should not silently alter a chronology already used elsewhere.
Rehearse the misleading cases
Prepare fictional examples containing an event recorded later, a negative finding, an uncertain date and two records that disagree. Check whether the proposed chronology preserves those distinctions. Test that a reviewer without matter access cannot retrieve the records or their summaries.
Measure omitted entries, unsupported statements, wrong page references and review time. Inspect consequential errors individually rather than averaging them into a score. Include failed extraction and manual correction in the comparison with the current process.
The synthetic evaluation guide provides the method. The legal hub connects this document-preparation workflow with intake and the firm's wider systems. No performance result or client outcome is claimed by this proposed design.