AI for Inquests and Coroners’ Work: Chronologies and Bundles

Inquests and coronial investigations often turn on two things:

a clear, defensible timeline of what happened; and
a bundle that lets the coroner and, where relevant, a jury move through the evidence without getting lost.

In practice, that means months or years of records:

medical notes,
999 calls and logs,
internal incident reports,
policies and protocols,
correspondence between multiple agencies.

AI will not tell you what the conclusion of an inquest should be. It can, however, help you get from “boxes of unstructured documents” to organised chronologies and navigable bundles more quickly — as long as you keep lawyers firmly in charge of judgment, relevance and tone.

This article looks at practical ways UK practitioners can use AI around inquests and coroners’ work, with a focus on chronologies, bundles and governance.

1. Start with the questions the coroner will care about

Before you think about tools, remind yourself what the inquest is trying to establish:

who the deceased was;
when and where they died; and
how — and in some cases in what circumstances — they came by their death.

In potential Article 2 or jury inquests, there may also be systemic issues around:

policies,
training,
communication between services, or
adequacy of risk assessments and responses.

A useful way to frame AI’s role is:

“Help me organise evidence so I can answer these questions clearly and fairly.”

Everything else — document clustering, timelines, draft bundle indices — should serve that aim.

2. Using AI to build and refine chronologies

Chronologies for inquests can be particularly demanding because:

clinical records may be long, repetitive and inconsistent;
multiple agencies keep separate records in different formats; and
small timing differences (minutes or hours) can matter a great deal.

AI can assist by:

scanning selected documents and pulling out dated events (admissions, handovers, risk assessments, observations, calls, decisions);
grouping them into a first‑pass event list with timestamps, locations and sources;
highlighting apparent gaps or conflicts, such as overlapping but inconsistent records.

A sensible pattern is:

Choose a controlled set of documents (for example, observations chart + A&E records + handover notes).
Ask AI to extract events into a table with columns such as:
- date/time,
- source document and page,
- who was involved,
- brief description,
- “confidence” or ambiguity flag.
Review and correct that event list manually before adding it to your master chronology.

The key is that you own the chronology. AI is a fast reader and note‑taker, not the author of your case theory.

3. Distinguish “working chronologies” from “hearing chronologies”

Most teams end up with at least two versions of the timeline:

a detailed internal working chronology, and
a cleaner version for the coroner, jury or other interested persons.

AI can help with both, but in different ways.

For the internal chronology, AI can:

merge event lists from different sources;
flag inconsistencies (“observation recorded at 03:15 but handover note says patient was in theatre”);
link events back to the underlying document with anchors or references.

For the hearing chronology, AI can:

help you collapse repetitive events (“15-minute observations from 01:00 to 03:00 – all recorded as stable”);
ensure consistent language across sections (“The Deceased”, “Nurse A”, “Paramedic B”);
format the document so it is actually usable in court.

But decisions about weight, emphasis and what to include should remain with those responsible for the case.

4. Preparing bundles with AI as a paralegal, not a judge

Inquest bundles often contain:

statements and depositions,
medical and social care records,
policies and protocols,
expert reports,
correspondence and minutes.

AI can be helpful in:

classifying documents by type and source (trust, GP, ambulance, prison, police, social services);
pulling out titles, dates and authors for an index;
suggesting logical sections (background, events leading to death, post‑incident investigation, policies).

A typical workflow might be:

feed a batch of documents into an approved AI tool;
ask for a draft index with neutral, accurate descriptions (“Nurse A statement dated…”, “Policy on ligature risk, version…”);
review and tidy the index, adding page ranges once the bundle is paginated.

Guardrails to keep in mind:

descriptions must remain neutral — no loaded language in the index;
AI should not decide which documents are in or out of the bundle;
you still need to check pagination and hyperlinks manually or with specialist bundling software.

Think of AI as a tireless assistant helping with description and sorting, not as someone who understands evidential rules.

5. Sensitive content, Article 2 issues and confidentiality

Inquests frequently involve:

medical confidentiality;
mental health records;
prison or custody records;
material about third parties who are not formally represented.

When using AI around these materials, you need to be very clear about:

where processing occurs (within your firm’s systems, in a segregated cloud environment, etc.);
what, if anything, is retained by the AI provider;
who can access prompts and outputs.

Sensible limits include:

use only AI tooling that sits within your case management or DMS environment, or under appropriately robust DPAs;
avoid using consumer‑grade chatbots for inquest materials;
keep a record of which documents were fed into AI for each chronology or index, so you can check and, if necessary, explain later.

Remember that Article 2 or other sensitive inquests may be scrutinised for years. Your data handling needs to be able to withstand that.

6. Working with clients, trusts and insurers

Many instructing clients — NHS trusts, police forces, local authorities, insurers — are themselves cautious about new technology.

It can help to be ready to explain, in plain terms:

what role AI is playing (“helping us summarise records and organise documents”);
what it is not doing (“not deciding the case, not drafting evidence, not changing witness wording”);
how you are keeping data safe (approved systems, logging, human review).

Some clients may welcome AI‑assisted chronologies that they can re‑use in internal investigations or learning reviews. Others will prefer you to keep tooling largely invisible. Either way, clarity helps.

Where OrdoLux fits

OrdoLux is being designed with document‑heavy, sensitive work like inquests very much in mind:

emails, records and correspondence can be linked to a single matter and structured chronology;
AI can help extract events and timelines from selected documents inside that secure environment;
draft bundle indices and document descriptions can be generated from metadata and file contents, ready for human checking;
all AI activity is recorded at matter level, so you can see who did what, with which documents, and when.

The aim is simple: use AI to reduce the mechanical pain of reading and organising vast quantities of material, while keeping coronial judgment, sensitivity and strategy where they belong — with experienced practitioners.

This article is general information for practitioners — not legal advice or guidance on any particular inquest or coronial investigation.

Looking for legal case management software?

OrdoLux is legal case management software for UK solicitors, designed to make matter management, documents, time recording and AI assistance feel like one joined‑up system. Learn more on the OrdoLux website.