Use cases · Primary care
Cardiovascular risk review
Risk factors and test results are scattered across visits. TESSA collects blood pressure, cholesterol, diabetes, smoking, family history, diet and activity, explains tests such as a cholesterol check or an ECG, then brings the results together with the patient's long-term condition history, so the doctor does the risk assessment and plans prevention with everything in one summary.
At a glance
- Does
- Information gathering, Summarisation
- Setting
- Primary care
- Specialty
- GP / Family medicine / Primary care
- Who
- Patient-facing
Also searched as
cardiovascular risk assessment · heart disease risk check · blood pressure and cholesterol review
Questions
What does TESSA do in cardiovascular risk review?
Risk factors and test results are scattered across visits. TESSA collects blood pressure, cholesterol, diabetes, smoking, family history, diet and activity, explains tests such as a cholesterol check or an ECG, then brings the results together with the patient's long-term condition history, so the doctor does the risk assessment and plans prevention with everything in one summary. It does this through information gathering, summarisation.
Does TESSA diagnose the patient?
No, TESSA never gives a patient a diagnosis. It collects and collates the information the patient provides, asks probing questions and performs risk assessment; a clinician reviews the summary and decides, and no diagnosis, summary or management plan reaches the patient until the clinician signs.
What happens if the patient mentions a warning sign?
Warning or worrying signs that require immediate attention or action go straight to the care team without alarming the patient. For time-critical presentations, TESSA supports the clinician only and is not currently intended for patient use.
How is patient information protected?
The patient surface runs on session codes, not names, and each institution's data is kept apart with a full audit trail. Our own Anonesia by TINYHealth is a proprietary software and code that removes PIIs, e.g. names, ID numbers and contact details from text before it reaches an AI model.
How do we start with this use case?
Each new use case is built and validated with clinical teams, on your own pathways and guidance. Book a meeting with us to start.
