Training course – Make – Healthcare

Make for Healthcare Professionals

Automation with Make (formerly Integromat) and AI modules: build scenarios, routers and AI steps while keeping data protection in check.

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

Make, formerly known as Integromat, is a visual automation platform that lets you connect applications and automate recurring processes without writing code. In this compact course you will learn to build scenarios systematically, from triggers and modules through routers and filters to iterators, aggregators and data stores. You will understand how data flows through a scenario, how to reshape it with functions and how to keep an eye on operations consumption.

The second focus is AI. You connect language models through Make’s AI modules and provider integrations to classify text, extract information from emails and documents, draft replies or enrich content. You learn to catch errors with error handlers, test scenarios and document them clearly. Finally, you address data protection topics such as hosting region, data processing agreements and data minimisation, so that your automations can be run safely within your organisation.

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 build Make scenarios independently using triggers, routers, filters and data transformations.
  • You can process lists and records with iterators, aggregators and data stores.
  • You can integrate AI modules for classification, extraction and text generation into your processes.
  • You can safeguard scenarios with error handlers, validation and human approval.
  • 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 Fundamentals: scenarios, modules and data flow
  • Interface layout, scenario editor and execution history
  • Triggers, actions and searches: module types and their role in a scenario
  • Understanding bundles, mapping and data types and using them deliberately
  • Choosing between schedules, webhooks and instant triggers
Module 2 Logic: routers, filters, iterators and aggregators
  • Using routers and filters for branching and conditional flows
  • Splitting lists with iterators and combining them with aggregators
  • Applying text, date and maths functions to transform data
  • Using data stores and variables for interim values and duplicate checks
Module 3 AI building blocks in Make
  • Connecting language models via AI modules and provider integrations
  • Automating classification, structured data extraction and text generation
  • Building prompts from scenario variables and processing output as JSON
  • Combining AI agents in Make with tools and scenarios as actions
Module 4 Integrations, error handling and testing
  • Connecting common business applications and any API via the HTTP module
  • Applying error handlers such as Resume, Ignore, Break and Rollback
  • Validating AI output and routing unusable results to a person
  • Testing scenarios, optimising operations and documenting them for the team
Module 5 Data protection, governance and operations
  • Assessing hosting region, data processing agreements and data flows to AI providers
  • Applying data minimisation and pseudonymisation within scenarios
  • Managing connections, teams and permissions across the organisation
  • Defining approval steps, monitoring and ownership for live operation
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

You should be comfortable with common office and cloud applications and have some experience with AI assistants. No programming skills are required. 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 a Make account; the free plan covers most exercises. API access for a language model is set up together during the course.

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

Frequently asked questions

Is Make the same as Integromat?

Yes. Integromat was renamed Make and substantially developed along the way. The core principle of visual scenarios made of connected modules remains, while a redesigned interface, many new integrations and AI building blocks have been added. If you used Integromat in the past, you will quickly feel at home and discover the new capabilities on the course.

How do I keep operation costs under control?

Every module that runs consumes operations or credits, and AI calls also use quota with the respective model provider. On the course you learn to design scenarios so that filters apply early, unnecessary runs are avoided and data is processed in batches. This keeps your automations economical and predictable as volumes grow.

Can Make be used in a GDPR-compliant way?

In principle yes, provided you set up the framework deliberately. This includes choosing the hosting region, signing a data processing agreement, reviewing connected AI services and applying data minimisation. On the course you work through these points with a checklist and learn to filter or pseudonymise sensitive data within scenarios.

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 “Make 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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