Calls, reports, requests to handle: in a medical practice, administrative workload is taking up an increasingly large share of your daily routine. When it builds up, it is not only your organization that comes under strain, but also the time available for your patients.
In this context, AI is attracting growing interest. However, one question comes up again and again: can it really reduce the administrative burden without compromising the quality of care?
Is AI really everywhere?
Today, a lot of things are being grouped under the term AI. Because it is a trendy term, it is often associated with all kinds of solutions and developments. In many cases, however, it is simply traditional automation: a rule triggers an action. That is not the same as a tool that can understand a request, summarize an exchange, or generate text.
In a medical context, however, this is exactly where AI becomes interesting. Artificial intelligence becomes valuable when it helps process less structured information: a phone call, a consultation note, a draft reply, or a report that needs to be rewritten.
How AI can help reduce your administrative workload
Reduce the need for daily writing
One of the first uses of AI, and probably the best known, is writing assistance. In a medical context, you can use LLMs (conversational AI models such as ChatGPT or Claude) to:
- Prepare an email draft
- Rewrite practical information
- Structure a message
These tasks quickly become time-consuming and offer little added value. Artificial intelligence allows you to complete them more quickly. Of course, AI does not replace human validation. It prepares a starting point and saves you time on the form, without replacing the substance that you provide.
Manage incoming requests more efficiently
At the front desk, a major source of lost time comes from phone calls that interrupt ongoing work:
- Appointment changes
- New bookings
- Prescription requests
- Questions about documents to send or prepare
These requests are not always complex, but each one interrupts the task currently in progress. On top of that, they create constant context switching, which is tiring, slows work down, and increases the risk of mistakes.
In this area, some tools can answer the phone during busy periods, when the practice is closed, or simply to handle these simple requests.
Be among the first users of Emma, your medical practice’s virtual assistant.
Emma can help you with:
- Appointment booking
- Prescription renewals
- Sharing practical information
Help with documentation and note-taking
In some situations, artificial intelligence tools can help format a note, prepare a draft report, or make transcription work easier.
During the consultation, this allows you to focus fully on the patient relationship, your interactions, and all the nonverbal cues that are useful to your assessment, without having to focus on note-taking. The FMH cites the optimization of administrative processes among the practical uses of AI in healthcare.
How to choose the right AI solution for your medical practice
Start with your problem, not the technology
Many tools already exist, and many more will certainly arrive soon. To make sense of this jungle of AI solutions, the first step is to clearly identify your needs. Which repetitive, time-consuming, low-value tasks are making you lose time every day? Which tasks or actions do you think could be optimized? From there, you can define whether you want to:
- Reduce missed calls
- Limit interruptions at the front desk
- Save time writing reports
- Better structure incoming requests
Choose a solution that fits into your practice without adding to the workload
A solution may seem very promising, but in the end it can add more work to your daily routine.
If it forces the team to manually reprocess every request, or if it is not compatible with your existing software or processes, the benefit will be limited. The right choice is not always the most comprehensive solution. It is the one that offers the best balance between usefulness and simplicity.
Make sure data is secure
Health data is considered sensitive personal data under Swiss law. It therefore requires a higher level of protection. Whatever solution you use, you need to make sure that you:
- Comply with professional confidentiality / medical confidentiality
- Restrict access to data to what is strictly necessary
- Check who receives the data and where it is sent
- Ensure that the data is not transferred abroad without appropriate safeguards
- Ensure that the use of the tool remains compatible with medical ethics
Keep human oversight
Whatever tool you use, it is essential to always be able to verify what the AI produces. The FMH also stresses this point: tools of this kind should be used in situations where you and your team are able to spot an error, assess the quality of the result, and remain in control.
How can you measure the impact of AI in your medical practice on your medical front desk?
Implementing an AI tool can come at a cost, which is why it is important to measure whether it brings real value to your practice. Here are the indicators you can monitor:
| Indicator | Objective | Measure / Impact |
|---|---|---|
| Missed calls | Reduction or even elimination of unanswered calls | Fewer lost calls, better accessibility |
| Time before first response | Immediate handling of calls | Shorter response time |
| Interruptions at the front desk | Significant decrease in interruptions during patient interactions | Fewer interruptions, better front-desk experience |
| Satisfaction with accessibility | Improved patient experience | Patient feedback or a short survey |
Finding the right balance without losing the human touch
AI is not meant to replace a front desk team, nor to come between caregivers and patients.
However, it can become a valuable support when it is used where it is most useful: for repetitive administrative tasks, structuring requests, first-level drafting, and information flow.
The benefit is not only organizational. It is also human. Less repetitive workload means more time for situations that require attention, coordination, and listening.
Sources
- FMH. Understanding large language models
- FMH. Legal considerations
- FMH. Examples of practical applications in healthcare
- FMH. FMH recommendations on the use of large language models (LLMs) in everyday medical practice
- FMH, & Swiss Academy of Medical Sciences. 7.1 Professional secrecy. In Legal foundations for everyday medical practice: Practical guide
- FMH. (2024). Swiss Medical Journal, Issue 21/2024. EMH Swiss Medical Publishers.
- Graf et al. (2021). Breakthrough, potential, and responsibility: On the future of legal education. Dike Verlag.
- Qin et al. (2025). Customer service representative’s perception of the AI assistant in an organization’s call center
- Federal Data Protection and Information Commissioner. Transfer of data abroad
- Rosenfluh Publikationen AG. (2025). Ars Medici, 15/2025: Swiss Journal of General Practice