Training course – Generative AI – Healthcare

Generative AI for Healthcare Professionals

Vendor-neutral, hands-on day: how language models work, where they go wrong and how to roll out AI successfully in your team.

Duration
6 hours, one session
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

This course connects Generative AI with the everyday processes of Healthcare. Participants work on realistic scenarios and leave with prompts, workflows and checklists they can use straight away.

Generative AI has reached most organisations, yet teams often lack a shared understanding of what language models can actually do and where their limits lie. In this vendor-neutral course you will learn how large language models generate text, why they hallucinate and how careful prompting, context and source checking lead to reliable results. Rather than relying on any provider's marketing claims, you will work with transparent criteria that help you compare tools and judge their fit for your own tasks.

The second focus is everyday implementation. You will identify concrete use cases in your area of work, weigh benefit, risk and effort, and draft a realistic roll-out plan for your team. This covers ground rules for handling confidential data, a practical prompt library and ways to build acceptance and make results measurable. Throughout the day you work with examples from your own field, so you leave with an actionable roadmap rather than a set of abstract ideas.

Hospitals, practices, care homes and health insurers struggle with staff shortages and a heavy documentation burden that takes valuable time away from patients. Generative AI can provide targeted relief: AI assistants help draft discharge letters and reports, summarise guidelines and studies, write patient information in plain language and support quality reports, rosters and administrative tasks. Automation speeds up appointment management, enquiry handling and internal communication. At the same time, the strictest requirements apply: health data is specially protected, medical confidentiality and professional rules must be observed, and software with a medical purpose may qualify as a medical device. AI in healthcare must therefore distinguish clearly between administrative support and clinical decisions, be operated securely and remain under the responsibility of qualified professionals.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Who is this course for?

  • Physicians in hospitals and practices
  • Nurses and nursing managers
  • Hospital and practice managers
  • Clinical coding and medical controlling staff
  • Quality management and administration in healthcare facilities

Typical challenges we address

  • Coping with heavy documentation alongside patient care
  • Keeping up with current guidelines and evidence
  • Informing patients clearly and in several languages
  • Running administrative, quality and billing processes with limited staff

What you will be able to do

  • You can explain clearly how language models generate text and why hallucinations occur.
  • You can write prompts that consistently deliver useful, verifiable results.
  • You can compare AI tools against transparent criteria covering data protection, features and cost.
  • You can identify and prioritise concrete use cases for your area of work.
  • Use AI to reduce the burden of documentation, patient information and administration.
  • Distinguish confidently between administrative AI applications and regulated medical devices.
  • Protect patient data in line with the GDPR, medical confidentiality and professional rules.

Course outline

Module 1 How large language models work
  • Tokens, context windows and probabilities: what happens when a model generates text
  • Training knowledge versus web search versus your own documents (RAG)
  • Text, images, audio and code: what multimodal models can do today
  • Realistically assessing typical strengths and weaknesses in office work
Module 2 Spotting hallucinations and getting reliable output
  • Why models produce plausible-sounding errors and when the risk increases
  • Prompting techniques: role, context, examples, output format and step-by-step instructions
  • Requesting sources, cross-checking facts and introducing a four-eyes principle
  • Scoping tasks so that AI assists rather than decides
Module 3 Choosing tools without vendor bias
  • Overview of chat assistants, AI built into office suites and specialist tools
  • Criteria: data processing, hosting location, admin features, integrations and pricing model
  • Enterprise licences, API access and locally hosted open-source models compared
  • Building a simple scoring matrix for your own tool selection
Module 4 Identifying use cases in your field
  • Systematically capturing recurring writing, research and analysis tasks
  • Rating benefit, risk and implementation effort for each use case
  • Distinguishing quick wins from strategic initiatives
  • Creating a prioritised use-case backlog for your team
Module 5 Rolling out generative AI in your team
  • Usage rules: which data may go into which tool and who signs off results
  • Building and maintaining a shared prompt library and templates
  • AI champions, training formats and peer exchanges to embed new habits
  • Measuring success: documenting time saved, quality and user satisfaction
Module 6 Practice lab: Healthcare
  • Drafting letters and reports
  • Summarising guidelines and studies
  • Patient information
  • Nursing documentation support
  • Quality and administrative documents
  • Enquiry and appointment management
Module 7 Data protection, quality & transfer to daily work
  • Health data is a special category under Article 9 GDPR and subject to medical confidentiality, in Germany under Section 203 of the Criminal Code; it may only be processed in approved, contractually secured environments. Software with a medical purpose, such as diagnostic or therapy support, can be a medical device under the MDR and is then high-risk AI under the EU AI Act.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

Practical scenarios from your field

Drafting letters and reports

An AI assistant in an approved environment structures findings and progress notes into a draft discharge letter or report, which the responsible physician fully reviews and corrects.

Summarising guidelines and studies

AI summarises guideline recommendations, studies and product information and compares key statements, as a starting point for continuing education and with references to the original sources for verification.

Patient information

Clinical content is turned into clear patient information, plain-language supplements to consent materials and leaflets in several languages, which are medically reviewed and approved.

Nursing documentation support

AI helps turn bullet points into structured nursing reports and handovers, draft care plans and harmonise wording, while the nurse remains responsible.

Quality and administrative documents

Procedures, hygiene plans, quality reports and training materials are created and updated with AI support, without the need for any patient data.

Enquiry and appointment management

Automations sort incoming enquiries from patients and referring physicians, answer standard organisational questions and prepare appointment suggestions, while medical questions go to qualified staff.

Prerequisites

No technical background is required. It helps to bring along typical tasks from your day-to-day work that you would like to support with AI. You work in a healthcare organisation, in a clinical, nursing, administrative or management role.

Technical requirements

You will need a laptop with an up-to-date browser and internet access. A free account with a common AI assistant is sufficient; company licences are welcome.

Data protection & compliance

Health data is a special category under Article 9 GDPR and subject to medical confidentiality, in Germany under Section 203 of the Criminal Code; it may only be processed in approved, contractually secured environments. Software with a medical purpose, such as diagnostic or therapy support, can be a medical device under the MDR and is then high-risk AI under the EU AI Act.

Dates & locations

For every location we suggest a date roughly four weeks ahead. Your preferred date is always possible.

  • Munich Fri, 11 December 2026 6 hrs Request
  • Salzburg Wed, 16 December 2026 6 hrs Request
  • Berlin Wed, 16 December 2026 6 hrs Request
  • Augsburg Thu, 10 December 2026 6 hrs Request
  • Hanover Fri, 11 December 2026 6 hrs Request
  • Bremen Mon, 14 December 2026 6 hrs Request
  • Stuttgart Fri, 11 December 2026 6 hrs Request
  • Kaiserslautern Tue, 15 December 2026 6 hrs Request
  • Online Thu, 10 December 2026 6 hrs Request

Frequently asked questions

Is the course tied to a specific AI tool?

No. The training is deliberately vendor-neutral. You work with widely used chat assistants and learn principles that apply to all major language models. That way you can transfer your skills to whichever tool your organisation has approved and evaluate new offerings yourself using clear criteria instead of relying on marketing statements.

How is confidential company data handled during the course?

For the exercises you use anonymised or fictitious examples from your area of work. At the same time you learn which categories of data may be entered into which tools, what to look for regarding data processing agreements and use of inputs for training, and how to set out these rules clearly for your team.

Will we leave with something concrete to implement?

Yes. You leave with a prioritised use-case backlog, a scoring matrix for tool selection, tested prompt templates and a draft set of team usage rules. These materials are designed so you can take them straight into internal discussions with management, IT and your data protection officer.

May patient data be processed in AI tools?

Only in environments approved by your organisation, secured under data protection law and compatible with medical confidentiality. Publicly available consumer tools are unsuitable. Many use cases, such as quality documents or patient information, need no patient data at all. We show you how to get started safely.

When does AI software become a medical device?

The intended purpose is decisive: software intended for diagnosis, monitoring or therapy decisions for individual patients can fall under the MDR. General text and administrative support usually does not. In the course you learn to recognise this boundary and keep clinical decisions with qualified professionals.

How much does “Generative AI for Healthcare Professionals” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 6 hours of live training with a trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. For every location we suggest a date roughly four weeks ahead. In the request form you can always describe your preferred date or period in free text – we check availability and get back to you within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run the course exclusively for your team.

Can the course be tailored to our team?

Yes. Tailor-made courses cost approx. €4,500 more, depending on the topic and by arrangement. We agree content, examples and exercises with you in advance.

More courses for Healthcare Professionals

More Generative AI courses

All 45 Generative AI courses

Generative AI – MunichGenerative AI – SalzburgGenerative AI – BerlinGenerative AI – AugsburgGenerative AI – HanoverGenerative AI – BremenGenerative AI – StuttgartGenerative AI – KaiserslauternGenerative AI – Online