bbv - AI Impact Forum

On 28 August 2024 I co-presented a panel discussion with Ms Stephanie Wyler-Marti of FMH under the title "AI in Action at FMH: AI as the Key to a Successful Tariff Structure Transition from Tarmed to Tardoc". FMH, the Foederatio Medicorum Helveticorum, is the umbrella organisation of Swiss physicians and brings together more than 70 medical organisations with over 45,000 individual members. In this capacity it carries out central functions including tariff development, quality assurance, and continuing medical education. The transition from Tarmed to Tardoc means a new nomenclature, new rules, and the introduction of additional chapters and tariffs for all outpatient services. This creates an enormous need for information and training for medical practices and for FMH itself. The joint discussion aimed to examine how modern AI technologies can help address these new challenges efficiently.

The Jump from TARMED to TARDOC

The switch from Tarmed to Tardoc represents a major step for the entire Swiss healthcare system. Numerous new services, split billing, and a more complex structure increase the workload for all involved. Practice owners need to adapt their billing systems, train staff, and ensure that all new requirements are correctly implemented. At the same time FMH faces the task of providing competent information about the tariff change, answering individual questions, and identifying tariff gaps quickly. The transition affects every outpatient physician in Switzerland, so the volume of enquiries is correspondingly high.

Why AI?

In my presentation I explained the reasons why FMH is seriously considering AI in the context of the tariff change. On one hand there are modern options such as chatbots or GPT solutions that are ideally suited to answering frequently recurring questions. On the other hand an AI-assisted solution offers the opportunity to prepare FMH's accumulated tariff knowledge in such a way that many requests can be handled in an automated yet high-quality manner. Such an approach not only improves service quality but also relieves staff, allowing them to focus on more complex requests and individual consultations. Not least, FMH gained experience with a high volume of enquiries as far back as 2018, which motivated them to explore new technologies to increase member satisfaction.

Technical and Organisational Challenges

The presentation addressed in detail how an AI solution could look that accesses only internal information and does not "invent" incorrect answers. This is a core challenge: the AI must be fed from a reliable database and may only reproduce content that has been validated beforehand. An agent concept is suited to this, in which specialised agents draw on different data sources depending on the question. For example, a "handbook agent" could provide general information from the FMH handbook, while a "rules agent" has access to position-specific billing rules. Technical restrictions would also be needed to prevent the AI from drawing on unsecured information from external sources. An agent log that records all questions and answers is useful for tracing how an answer was reached and whether knowledge gaps exist. This also allows potential error sources to be quickly identified and the knowledge base to be continually improved.

Experiences and Outlook

At the end of the presentation I outlined the possible impact of AI use on FMH's working practices. The relief provided by automated responses would free up capacity for demanding and more personal consultations. At the same time FMH could draw valuable insights from the enquiries to identify tariff gaps and close them more quickly if needed. AI technology can also be integrated into further areas of FMH over time, from continuing education to quality assurance. The tariff change has shown that the healthcare sector faces increasingly complex tasks that are difficult to manage with traditional methods. AI offers a modern approach to making processes more efficient and meeting the high expectations of members. The potential remains large going forward: ongoing optimisation and responsible data handling can help to support the tariff transition sustainably and at the same time pave the way for further digital innovation.