Mental healthcare does not need AI experiments. It needs hours back. Here is what we have delivered, what we have watched fail, and how to tell the difference in advance.
The question is no longer whether AI can do something for mental healthcare, but where the return shows up fastest. In our projects it is almost always the same place: the administrative shell around the treatment. Registration, intake, handover, correspondence and the assignment of e-health modules. Work that is necessary, but that requires no clinical judgement.
In the projects we run, client registrations were typically handled entirely by hand: staff phoned clients for missing information, retyped forms into the EHR and maintained waiting lists manually. After implementing AI-powered registration forms this process runs almost entirely automatically, and internal support tends to grow on its own: on average around fifteen percent of staff are already actively involved in shaping AI use right from the start, a group that quickly grows into a broader movement across the organisation.
1. Intelligent support during registration. Registration forms go out automatically with smart reminders, including a speech-to-text option for clients who struggle to write. Completed data flows into the EHR automatically and waiting lists update in real time across all divisions.
2. Automatic email registration. Registering a sent email often costs a clinician around 30 clicks, or roughly 5 minutes, with the risk of a missed registration. After implementation a pop-up appears automatically with the relevant fields pre-filled: registration is complete in 1 to 3 clicks, with an expected 90% running automatically. The staff member sees what is about to be recorded and confirms it; nothing is stored without that step.
3. The right e-health module from inside the EHR. E-health platforms offer hundreds of specialised modules and no clinician knows them all. An assistant combines client context from the EHR with the characteristics of the catalogue and surfaces a personalised top five or top ten at the moment the care plan is being written.
4. A digital assistant on top of any browser-based EHR. Through a browser extension, the assistant validates input, flags missing or inconsistent fields and automates repetitive actions. It works without your EHR vendor having to build anything.
We see the same patterns fail repeatedly, and it is more useful to name them than to stay quiet about them.
Every mental healthcare organisation gets the same questions from its data protection officer, and rightly so. We therefore build on Microsoft technology in GDPR-compliant configurations, with data minimisation as the starting point: the assistant receives only the context the task requires.
The design principle matters just as much. At every step with clinical or legal weight, the AI prepares and a human decides. For automatic email registration that means a staff member sees what is about to be recorded before anything reaches the record. It costs a few seconds and removes the single biggest objection.
An AI strategy engagement typically runs ten weeks and produces a roadmap that estimates, per opportunity, the expected return, the effort required and the risks involved. After that we build, usually starting with the application that has the shortest path to visible results.
We work to a proprietary framework inspired by MIT research, and we build with reusable components. That means the second mental healthcare organisation does not pay for the first one's learning curve.
The questions executives, clinicians and data protection officers ask us most about AI in mental healthcare.
The largest and fastest-visible return sits in the administrative shell around treatment: after implementation, registrations run almost entirely automatically, and for email registration the expectation is that 90% of incoming email is logged into the EHR automatically, with human review before anything is stored.
No. We build on top of the existing EHR rather than beside it or in place of it. For browser-based EHR systems we work with a digital assistant delivered as a browser extension, so your EHR vendor has to build nothing and you are not dependent on their roadmap.
Yes, provided three things are handled properly: the technical environment, data minimisation and human review. We build on Microsoft technology in GDPR-compliant configurations, give the assistant only the context the task requires, and keep a human decision in the loop at every clinically or legally meaningful step.
No. In our solutions the AI prepares and the professional decides. In e-health matching, for example, the assistant surfaces a reasoned top five or top ten; the clinician reviews, chooses and assigns. Full automation of clinical decision-making is legally untenable and professionally undesirable.
An AI strategy engagement typically runs ten weeks and produces a prioritised roadmap. After that we deliberately pick the application with the shortest path to a visible result, so the organisation feels the effect before internal support fades.
The AI Maturity Assessment is free and shows where your organisation stands. A first advisory conversation is also free of charge. Engagements are then quoted individually based on scope and size.
Yes. We have delivered in twelve sectors including FMCG, manufacturing, engineering and construction, trade, automotive, non-profit, high tech, fundraising, telecom, government and education. Mental healthcare is where our specialisation runs deepest.
In a free consultation we walk through your registration, intake and handover processes and name where AI makes a difference and where it does not.