This is the hot topic at the moment. All of the promises and opportunities of AI in healthcare are across everyone’s feeds. But how do you actually implement it into practice management and what do you need to do?
The very first point is to work out what problem are you trying to solve? There are a myriad of ways you can implement AI in medical practices, but without a clear goal of what you’re trying to do, you can easily end up with a patchwork of tools, duplication and inefficiency.
Once you know what you’re trying to solve, the path forward becomes easier.
Let’s look at staff training and onboarding as an example.
Having a dedicated chat interface for a new staff member to ask questions is invaluable. It’s a repository of all of your processes and procedures and the staff member can ask it directly, what do I do when? The answer will come back as any model answers a query, but it is specific to your practice and your practice documents.
Implementing out of the box tools is more successful if all of your processes and procedures are available in one place for the tool to find them. If you’re adding an appointment management tool it will need to know your appointment policy, who gets an appointment and under what conditions. These directives need to be explicit for the tool to work best for your practice.
Before any AI tool can work effectively in your practice, it needs to know what your practice actually does. That sounds obvious. But most practices don’t have it written down. Processes live in the practice manager’s head, in email chains, in the memory of the staff member who’s been there longest. When that person leaves, the knowledge leaves with them.
AI in all its forms, is based on language and data. It processes that language and data back in the form of analysis or answers to direct queries, or orders of function directed by the user. AI systems need to know exactly what you want to do, so it can then go ahead and do it.
For practice integration, nothing can be assumed. Every rule, every exception, every policy needs to be written down before the AI can apply it. The large language models like Claude are already preloaded with information that it draws from. This is what you need to do for your practice, with practice-specific instructions and rules to function.
So where do you start? Pick one problem. Just one. The practice that tries to implement AI scribes, scheduling tools, billing software and a staff training assistant simultaneously will implement none of them well. Choose the single biggest pain point in your practice right now and solve that first.
Let’s look at how to set up a Claude Folder for staff training as an example.
Setting up an AI assistant for staff training
Claude — one of the leading AI models — has a feature called Projects. Think of it as a dedicated folder where you upload your practice documents and then have conversations with an AI that knows only your practice.
Here’s what the setup looks like:
Step 1 — Create a Project
Go to claude.ai and create a new Project. Give it a name — “Practice Assistant” or “Staff Training/Onboarding.
Step 2 — Upload your documents
Upload your process and procedure documents into the Project. Your practice manual. Your billing process. Your referral process. Your phone management guide. Every document you want the AI to know about goes in here.
Step 3 — Give it instructions
Add a brief set of instructions telling the AI its role. Something like — “You are a practice assistant for [Practice Name]. Answer questions using only the documents provided. If the answer isn’t in the documents, say so and suggest the staff member ask their practice manager.”
That last instruction is important. You want the AI to know the limits of its knowledge.
Step 4 — Test it
Ask it questions a new staff member would ask. “What do I do when a patient calls to cancel?” “How do I handle a billing exception?” “What is the DNA process?” If the answers are wrong or incomplete — update your documents.
Step 5 — Share it with your team
Claude allows you to share Projects with team members. Your entire practice can access the same assistant from the same documents.
The result:
A new staff member on day one can ask questions and get consistent answers based on your actual practice procedures, not someone’s memory of them. Your practice manager isn’t interrupted every five minutes. And when staff leave, the knowledge stays.
The only maintenance required will be to update processes and procedures as they occur to keep the model accurate. Staff should be encouraged to follow documented processes rather than working around them. When workarounds occur they become habits and habits that aren’t documented can’t be taught to AI or to new staff.
These are the practical manual steps that lay the foundation for your AI to be effective. The core issue is again, what problem are you solving, how do you want to solve it, how will you measure it?
Implementing AI is not an out of the box proposition. It is very specific to your practice. The more specific, the more helpful and efficient AI can be. The arduous task is to create the documents AI needs to function at its full potential within your practice. If you need something to get started, take a look at my practice procedure and process document package.