Healthcare

AI for the day-to-day work of a hospital, without clinical records leaving your network.

The discharge summary gets written at nine at night, and episode coding runs months behind while the hospital's funding depends on it. Inferana puts open models to work on that text without it leaving the hospital.

The rules that apply to you, point by point

What each regulation in your sector requires, and which part of that the deployment solves.

GDPR

Regulation (EU) 2016/679, art. 9
What it requires

Health data is a special category: explicit legal basis and reinforced safeguards to process it.

How we solve it

Prompts and responses are never stored or used to train models, and every access is logged per user and team.

EHDS

Regulation (EU) 2025/327
What it requires

Primary and secondary use of electronic health data, with processing kept inside the European Union.

How we solve it

Inference on European infrastructure or in your own data centre, with clinical text staying inside the hospital's perimeter.

AI Act

Regulation (EU) 2024/1689
What it requires

Traceability, data governance and human oversight for high-risk systems in healthcare settings.

How we solve it

Open models with published model cards and licences, access controls and an auditable record of every query.

NIS2

Directive (EU) 2022/2555
What it requires

Hospitals are essential entities: incident reporting and supply chain security obligations.

How we solve it

A single European provider with deployment documentation for your supplier inventory, and an on-premise option on your own infrastructure.

Your most common use cases

The same ones you already use, specific to your sector.

See all use cases

The discharge report is already being written with AI.
Just in someone else's chatbot.

Ten minutes per patient, a waiting list that keeps growing and reports written after hours. The approved tool has been sitting with the committee for months, so the same thing keeps happening:

What happens today

My consultants write their discharge summaries at nine at night.

Clinical service lead

Coding is five months behind and the funding follows the coding.

Clinical coding office

Ten minutes per patient and half of that goes into typing.

Outpatient clinic coordination

Clinical notes end up in a public chatbot and I cannot see it.

Data protection officer

The HIS is twenty years old and nobody wants to open it.

Health information systems

To start a study I need thousands of reports anonymised.

Clinical research

I cannot sign off a cost that grows with every new user.

Operations management

With Inferana

A draft discharge summary built from the episode's clinical notes, inside the hospital perimeter, for the clinician to review and sign.
Proposed codes for the episode, each with the sentence in the report that justifies it, leaving the coder validating.
Assisted drafting of notes and reports from the text that already exists, so consultation time goes to the patient.
An approved tool inside the hospital perimeter, with an auditable record of every query per user and team, exportable when the DPO asks.
OpenAI-compatible API: it connects to what you already run and the HIS stays exactly as it is.
Report anonymisation on your own infrastructure, with identifiers removed before any analysis begins.
Flat rate: cost does not depend on how many clinicians use it or how many queries they run.

Sector FAQs

Inferana does not carry a CE mark as a medical device under the MDR (Regulation (EU) 2017/745). It is built for documentation, administrative and knowledge work: drafting, summarising, classifying and searching. If your organisation puts it to work supporting clinical decisions, that medical purpose is set by you, and you are the one entering the MDR perimeter with the obligations that come with it.

Wherever you decide: on European infrastructure we manage, or in the hospital's own data centre, on-premise. With an on-premise deployment clinical text never leaves your infrastructure. In both cases prompts and responses are never stored or used to train models.

The model reads the episode and proposes the codes with the sentence in the report that justifies each one. The coder confirms or corrects, so what is typing from scratch today becomes reviewing a proposal with the evidence next to it. The dataset you submit stays your responsibility, and the record of every query is there to audit the process.

Processing stays inside the European Union, and on-premise it stays on your own machines. The access application to the health data access body, the legal basis and the purpose of the secondary use remain yours. We provide the record of who queried what, and anonymisation before the analysis starts.

Access control per user and per team, with a record of every query. The DPO and internal audit can review who asked what and when, and export it for an investigation or a breach notification.

Yes, with the same key. There the query does go out to the model provider, so clinical text and any patient data are handled with the open models inside your perimeter. Access is configured per team.

No. The API is OpenAI-compatible: swap the key and the endpoint and what you already run stays as it is. Anything new connects over the API, with no need to open up the record system.

The models are open and the API is OpenAI-compatible, so your exit plan does not depend on us: you can take the weights with you and point the code elsewhere by changing a key. Your data and your indexes are exportable.

Tell us what you want to deploy and we will show you where Inferana fits.

In the demo we go through your case: which models you need, where they run and what it takes to meet the regulation that applies to you.

We reply within one business day.