How does AI in general practice contribute to creating personalized treatment plans?
In general practice, most patient data can be found in the EHR. Some of this information is structured, but a large part lives in free text. Finding the right piece of information as a general practitioner often means clicking through multiple tabs, reading long documents, and piecing things together manually, usually within the time constraints of a consultation.
How AI handles large volumes of clinical data
AI models are trained to recognize (clinical) language and patterns. They can read through large amounts of text and data points in seconds. Where manual reviewing patient information requires selecting and opening individual documents, AI evaluates the dataset in a couple of seconds. It identifies relationships, detects changes, and retrieves specific information when prompted. For example:
A GP wants to check medication interactions before prescribing a new drug. AI can instantly bring up the patient’s current medications, allergies, and any contraindications.
During a routine follow-up, a clinician needs to review trends in lab results. AI highlights changes over time in kidney function, blood pressure, or blood sugar levels, all in one view.
When a patient has a complex history, such as multiple prior episodes of back pain, AI can summarize previous visits and treatment plans, so the GP can see what worked or didn’t work before.
By combining structured and unstructured data across the patient record, AI helps the clinician access exactly the information needed without scrolling through multiple documents.
What this looks like inside the HIS
In practice, this capability is increasingly embedded directly into the EHR through search and summarization functionalities. Instead of searching manually, a GP can ask direct questions, often in a chat layer within the EHR. AI then retrieves relevant information from across the record, labs, notes, medications, and correspondence, and presents it in a concise, clinically relevant view. There’s no need to scroll through long histories or open multiple documents. The system surfaces the relevant context based on the question being asked and includes source attribution.
Connecting this to personalized treatment plans
Personalized care depends on having the right information available at the right moment. When previous responses to medication, side effects, comorbidities, and trends over time are easier to access, they can be more consistently taken into account during decision-making. This affects how treatments are selected, adjusted, or avoided.
AI in general practice is therefore less about adding new data and more about making existing data easier to use. In the context of personalized treatment planning, that shift occurs at the levels of access, context, and timing during the moments when decisions are made.