Use cases · Specialist clinic
Eye clinic review
Eye clinics see many patients in tight slots, across conditions like cataract, glaucoma, diabetic eye disease, macular degeneration and dry eye. Before the visit, TESSA asks about vision changes, symptoms in each eye, eye drops and how they're used, injections or surgery so far, and diabetes or blood pressure control, so the eye doctor starts with a summary. It tells patients with sudden vision loss, flashes, new floaters or a painful red eye to seek urgent care.
At a glance
- Does
- Information gathering, Summarisation
- Setting
- Specialist clinic
- Specialty
- Ophthalmology
- Who
- Patient-facing
Also searched as
eye clinic history · glaucoma follow-up · diabetic eye screening / retinopathy · macular degeneration review · eye drop technique
Questions
What does TESSA do in eye clinic review?
Eye clinics see many patients in tight slots, across conditions like cataract, glaucoma, diabetic eye disease, macular degeneration and dry eye. Before the visit, TESSA asks about vision changes, symptoms in each eye, eye drops and how they're used, injections or surgery so far, and diabetes or blood pressure control, so the eye doctor starts with a summary. It tells patients with sudden vision loss, flashes, new floaters or a painful red eye to seek urgent care. 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.
