Training course – Prompt Engineering – Healthcare

Prompt Engineering for Healthcare Professionals

Vendor-neutral prompt engineering: roles, context, few-shot examples, prompt chains, prompt libraries and systematic quality checks.

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 Prompt Engineering with the everyday processes of Healthcare. Participants work on realistic scenarios and leave with prompts, workflows and checklists they can use straight away.

Whether you use ChatGPT, Claude, Copilot, Gemini or Le Chat, the quality of the output depends largely on how the task is framed. Prompt engineering is the skill of conveying goal, context, constraints and desired format to a language model so that it reliably produces usable results. In this vendor-neutral practical day you learn proven techniques that work across all mainstream AI assistants: defining roles and audiences, selecting relevant context, working with examples and breaking complex tasks down into traceable steps. You try out every technique directly on tasks from your own working day.

The second part focuses on repeatability and quality across the team. You develop prompt templates with placeholders, build multi-step prompt chains for analysis, writing and review processes, and set up a shared prompt library. You learn to evaluate output systematically – with review criteria, test cases and targeted self-checks by the model – and to avoid typical pitfalls such as hallucinations, ambiguity and missing context. By the end you have a structured approach that works regardless of the tool you use and continues to pay off as new models arrive.

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 understand how language models process prompts and derive effective wording from this.
  • You structure prompts with role, context, task, constraints and output format.
  • You use few-shot examples and prompt chains for complex, multi-step tasks.
  • You create reusable templates and build a shared prompt library.
  • 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 language models process prompts
  • Core principles of language models: probabilities, context window, limitations
  • Why wording, order and examples change the result
  • What causes hallucinations and how prompts reduce them
  • Differences and common ground between mainstream AI assistants
Module 2 Building blocks of an effective prompt
  • Defining role, audience, task, context and output format
  • Stating constraints, exclusions and quality criteria
  • Structured input with sections, delimiters and tags
  • Asking the model to raise questions instead of making assumptions
Module 3 Advanced techniques
  • Zero-shot, one-shot and few-shot: setting style and structure with examples
  • Requesting step-by-step reasoning for complex tasks
  • Prompt chains: separating research, drafting, review and revision
  • Using change of perspective, counter-arguments and model self-critique
Module 4 Templates and prompt libraries
  • Creating reusable templates with placeholders and variables
  • Storing persistent instructions in projects, custom assistants and agents
  • Building, naming, maintaining and sharing a team prompt library
  • Documenting templates: purpose, inputs, expected output, owner
Module 5 Quality assurance and responsible use
  • Defining review criteria and scoring rubrics for AI output
  • Setting up test cases and systematically comparing and improving prompts
  • Verifying facts, figures and sources; establishing the four-eyes principle
  • Data protection, confidential content and transparency under GDPR and the EU AI Act
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

Some initial experience with at least one AI assistant such as ChatGPT, Claude, Copilot or Gemini is helpful; no technical background is required. You work in a healthcare organisation, in a clinical, nursing, administrative or management role.

Technical requirements

A laptop with an up-to-date browser and access to at least one AI assistant, ideally the tool approved within your organisation.

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

Frequently asked questions

Do these techniques work with every AI assistant?

Yes, the course is deliberately vendor-neutral. The principles – a clear task, relevant context, examples, structured output and review steps – work equally well in ChatGPT, Claude, Copilot, Gemini, Le Chat and other tools. Where assistants differ in detail, for example with projects or custom assistants, we show the corresponding features in each.

Won't better models soon make prompt engineering obsolete?

Models are getting better at interpreting vague requests, but they still cannot know what you do not tell them: audience, quality standards, internal requirements or relevant background. Good prompts are therefore mainly good task descriptions. Templates, prompt chains and quality checks also keep their value for teams as new models arrive.

What is a prompt library and how do we maintain it as a team?

A prompt library is an organised collection of proven templates for recurring tasks, each with its purpose, required inputs, a sample result and an owner. It can live in a shared document, a wiki or directly in the AI tool's projects. In the course you set up a first library and agree simple rules for maintenance and approval.

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 “Prompt Engineering 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.

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