Use cases · Specialist clinic
Sleep clinic assessment
Sleep problems have many possible causes, and a first visit can be spent working through long questionnaires. Before the visit, TESSA asks about snoring, breathing pauses, daytime sleepiness, insomnia, restless legs, shift work and drowsy driving, using standard tools like the Epworth Sleepiness Scale and STOP-Bang, and can include what a bed partner has noticed, so the doctor starts with a summary.
At a glance
- Does
- Information gathering, Summarisation
- Setting
- Specialist clinic
- Specialty
- Sleep medicine
- Who
- Patient-facing
Also searched as
sleep clinic questionnaire · sleep apnoea / apnea screening · snoring assessment
Questions
What does TESSA do in sleep clinic assessment?
Sleep problems have many possible causes, and a first visit can be spent working through long questionnaires. Before the visit, TESSA asks about snoring, breathing pauses, daytime sleepiness, insomnia, restless legs, shift work and drowsy driving, using standard tools like the Epworth Sleepiness Scale and STOP-Bang, and can include what a bed partner has noticed, so the doctor starts with a 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.
