Healthcare · Clinics and healthcare centres
AI for clinics and healthcare centres
In a clinic, the critical information already flows through WhatsApp: nursing handovers, medical orders, what happened during the shift. AI automation takes those messages exactly as they are written, identifies which patient they refer to, and files them into the management system without asking anyone to change how they work.
What this service covers
The real problem: nobody has time to enter data
A nurse on shift doesn't have time to open a system, find the patient, select them, and type. They have time to send a message. A doctor who has to record progress notes for twenty patients loses to data entry the time they need for care. That's why clinical management systems end up outdated: not because staff don't want to use them, but because entering data competes with caring for patients.
The design principle: don't change how people work
Resistance to change in healthcare isn't stubbornness, it's a consequence of caseload. When a tool demands the team work differently, it gets abandoned and the investment is lost. The approach here is the opposite: take the channel the team already uses — the WhatsApp group where updates are passed along — and turn it into the system's intake. Staff work doesn't change; what changes is that the data ends up structured.
What is already running
- Nursing handovers synced per patient, three times a day, from conversations that follow no fixed format.
- Medical orders entered automatically into the central system, with no manual transcription by the professional.
- Patient identification even when the name is written any which way, abbreviated or without accents.
- Supply intake with a photo: OCR reads up to five documents in a single image and files them against the right patient.
- Traceability of what was recorded, when, and who reported it.
What can be added
These modules are designed and can be implemented on the same workflow, but it's worth being precise: they are not part of what is already in production.
- Centralised communications: one number for families and another for specialists, with intent detection and automatic routing to the right area. At an institution with eighty patients and three monthly contacts per family, that's around two hundred and forty conversations a month currently scattered across personal phones.
- Stock control tied to medical orders: if the order sets the consumption frequency, stock movement can be calculated rather than estimated.
- Shift scheduling: an automatic proposed roster that the supervisor validates, instead of building it from scratch every week.
- Voice progress notes: the professional sends an audio message, the system transcribes it, identifies the patient, and adds the note to their record after the doctor validates it.
Health data: what it requires and how it's handled
- Information stays on the infrastructure the institution defines, its own or its cloud.
- Role-based access: each profile sees only what corresponds to their function.
- Auditable record of who consulted each file and when.
- Processing in line with Argentina's Personal Data Protection Act 25.326, which classifies health data as sensitive.
- Images containing personal data are anonymised before any publication or demonstration.
What changes in the operation
Doctors stop transcribing patient by patient. The nursing team keeps reporting as always, but their handovers end up structured. When a family member asks, the institution answers with the latest recorded handover instead of tracking down whoever was on shift. And supply intake goes from desk work to a photo.
Manual entry, management system, and an AI workflow
Most institutions already have a management system. The problem isn't the system: it's how the information reaches it.
| Criterion | Manual entry | Management system alone | AI workflow |
|---|---|---|---|
| Requires staff to enter data | Yes | Yes | No |
| Takes information from the channel already in use | No | No | Yes |
| Identifies the patient in informal text | Not applicable | No | Yes |
| Keeps the record current during the shift | Depends on staff | Depends on staff | Yes, three times a day |
| Leaves an auditable record | Partly | Yes | Yes |
| Changes how the team works | No | Yes | No |
The AI workflow doesn't replace the management system: it feeds it. It integrates with whatever the institution already uses.
Real use cases
Implementation process
- 1
Diagnostic
We analyze your process and tell you what to automate, how, and what return to expect.
- 2
Implementation
We build the solution connected to your systems, with working deliveries and real tests.
- 3
Operation and improvement
We leave it running, train your team, and measure results.
The full detail, with deliverables per stage, is in how we work.
Frequently asked questions
Does staff have to learn a new system?
No. Updates keep flowing through the usual channel. The agent is what classifies, identifies the patient, and builds the record. That's precisely the design criterion: if a tool demands changing how people work, it gets abandoned.
How does it identify the patient when messages are written any which way?
Language models interpret the intent of a message, not a fixed format. They recognise the patient even when the name is abbreviated, misspelled, or missing accents, and also record the time and who reported it.
What if the system misreads a message?
It's recorded with a confidence level, and anything below the threshold goes to human review before being filed. With clinical information the criterion is conservative: when in doubt, a person steps in.
Does it integrate with our patient management system?
Yes, by API or import files. If the system is closed and allows neither, that surfaces during mapping and alternatives are assessed before committing to anything.
How is patient data protected?
Deployment runs on the infrastructure the institution defines, with role-based access and a log of who consulted each file. Health data is sensitive data under Act 25.326 and the workflow is designed within that framework.
Does it suit a small institution?
What defines the case is the volume of daily updates and how many areas need the same information, not the number of beds. The workflow is implemented by area and grows with the institution.
Does it replace nursing or administrative staff?
No. It removes data entry, which is work nobody claims as their own. Staff keep doing the same and get back the time currently spent transcribing.
How long does implementation take?
The handover and orders workflow can be running in weeks for one area. Additional modules are added in stages on the same foundation.
How much clinical information currently lives only in a WhatsApp group?
Tell us how updates flow at your institution and we'll assess which part can be brought into the system without changing how the team works.