AI in healthcare: less admin, more time for clients and patients

AI in healthcare is software that processes language, documents and data to take work off care professionals' hands. Today it helps mainly with the work around care: draft reports and registrations, reporting, rosters when staff are absent, new client registrations and practical enquiries, searching protocols and guidelines, and keeping track of market and policy information. Medical and clinical judgement stays with the professional.

Wherever medical or clinical judgement is involved: diagnosis, needs assessment, choice of treatment, medication and risk assessment. In our applications AI supplies a draft, a summary or a source, and the care provider reviews and signs off. We build that human oversight, the human in the loop, into every application. The responsibility stays with people.

Yes, provided you set it up properly. Health data is special category personal data under the GDPR and needs extra protection. That is why we work in a shielded, business-grade environment in which data is not used to train models, set out the processing in a data processing agreement, take the NEN 7510 standard into account and help with a DPIA where one is needed. Client and patient data never belongs in a public chatbot.

Anyone who deploys AI takes measures to support the AI literacy of their staff. That obligation under Article 4 has applied since 2 February 2025, and since 27 July 2026 in the lighter wording of the Digital Omnibus on AI (Regulation (EU) 2026/1744). Since 2 August 2026 it must also be clear when clients are talking to your AI or seeing realistic AI-generated images. The heavy requirements for high-risk AI, such as AI in recruitment or credit assessment, apply from 2 December 2027, and for AI in products from 2 August 2028. For each application you determine your role and the risk category. Innoworks is an implementation partner and does not give legal advice; for a legal review you work with a lawyer.

That varies by process and organisation, so we do not promise a fixed percentage. The biggest gains are usually in documentation and reporting, because there AI produces a first draft that the professional only needs to review. In an AI audit we measure the realistic time saving per process before you build anything.

In mental healthcare AI mainly helps with documentation, reporting and finding information in records and protocols. The practitioner keeps making the decisions. Our hands-on mental healthcare work so far has been market-focused: for Ecare, maker of the PUUR. electronic client record used in mental healthcare among other settings, we built an AI system that maps, enriches and scores more than 5,000 mental healthcare organisations and sends qualified prospects to HubSpot automatically. That case is shown on this page.

Yes, with the work around consulting hours: preparing a draft note of a consultation, sorting administrative questions and messages, and searching protocols and guidelines. Medical triage, diagnosis and treatment stay with the GP and the practice team. Whether an application can connect to the practice's GP information system is something we check up front, because not every system offers an integration.

That is the starting point. We do not replace your records system, but build applications and integrations that connect to it, so information ends up in the right place. In the audit we test up front which integrations are feasible, because not every connection can simply be made.

That is why we never let AI have the last word in care situations. Answers come with the source attached, so a professional can check what something is based on, and critical steps always require human sign-off. That prevents an incorrect answer from being adopted unseen.

Yes. We also work with healthcare software and healthtech companies that want to add AI functionality to their product, for example smart summaries, search functions or automations. With custom AI software and AI consultancy we help as a technical sounding board and build partner, so you go live faster and more safely.

With one process that has a clear owner and measurable time spent on it, such as documentation or searching protocols. In a fourteen-day AI audit we map out your processes and deliver a prioritised opportunity report with the expected return per opportunity, tested for privacy and feasibility. After that you build what delivers most first, and we tell you honestly what is not yet possible.