What is AI sentiment scoring?
Sentiment scoring is a natural language processing technique that reads text and determines whether the writer's emotional tone is positive, negative, or neutral. Traditional sentiment scoring used keyword matching — look for positive words, look for negative words, count them up. Modern systems use large language models (LLMs) that understand context, sarcasm, understatement, and domain-specific language.
For dental patient feedback, SuiteGrowth uses Anthropic's Claude Haiku to read every patient reply and assign a score from 1 to 5. The score reflects the patient's overall satisfaction with their appointment — taking into account what they said, how they said it, and any specific concerns they raised.
The scoring decision happens in real time — typically under 2 seconds from when the patient sends their reply. The result determines what the patient receives next: a Google review link for scores 4–5, or a personal follow-up from the practice for scores 1–3.
Very positive
"Absolutely brilliant, felt nothing and everyone was so kind!"
Google review link sent immediately
Positive
"Good appointment, all sorted now, thank you"
Google review link sent
Neutral / mixed
"Was okay, had to wait a bit but the dentist was nice"
Practice team alert, personal follow-up
Negative
"Not happy with how it went, still in pain"
Practice alert (urgent), warm follow-up
Very negative
"Terrible experience, want to make a complaint"
Practice alert (immediate), escalation flag
The 7 dental safety flags
Beyond the 1–5 score, every reply is checked against 7 safety flags. These are not review routing decisions — they are patient care signals that require specific responses from different members of your team.
Pain
Patient mentions pain, discomfort, sensitivity, or soreness that is unexpected or severe. This is a clinical flag — not a review routing decision. The patient needs a clinical follow-up from the dentist, not a review request.
"Still quite sore and it's been two days now" → Pain flag triggered
Complaint
Patient expresses dissatisfaction with the treatment received, the staff attitude, the outcome of a procedure, or the practice experience. Requires attention from a practice manager or principal dentist.
"The filling doesn't feel right and my bite is different now" → Complaint flag
Anxiety
Patient expresses dental anxiety, fear, distress, or nervousness — either about their recent appointment or about attending future appointments. Requires an empathetic outreach from the practice, not an automated review flow.
"I was really anxious the whole time, I nearly left" → Anxiety flag
Confusion
Patient is uncertain about their aftercare instructions, medication dosage, what to do about a symptom, or their next appointment. This is a clinical safety flag — confusion about post-operative care can lead to harm if not addressed quickly.
"Not sure if I'm supposed to take more ibuprofen or not" → Confusion flag
Booking intent
Patient expresses a desire to book a follow-up appointment, ask about a new treatment, or enquire about availability. This is an opportunity flag — your front desk should follow up before the patient calls another practice.
"Actually been thinking about getting my teeth whitened" → Booking intent
Escalation
Patient threatens to make a formal complaint to the GDC, NHS, or CQC, mentions taking legal action, or intends to post on social media or review sites. Requires immediate response from practice management.
"I'm going to report this to the GDC if it's not sorted" → Escalation flag
Balance query
Patient questions their account balance, a charge they don't recognise, a payment plan, or an outstanding invoice. A finance or admin team flag — not clinical, but requires prompt resolution to prevent escalation.
"There's a charge on my account I don't remember agreeing to" → Balance query
Why keyword matching fails in dental contexts
Keyword-based sentiment systems break on the kind of natural language dental patients actually use. Consider these examples that would produce completely wrong results with keyword matching:
"No pain at all — couldn't be better"
Keyword matching
pain → negative flag
LLM scoring
Score 5 — emphatic positive, pain mentioned only to negate it
"Couldn't be happier with how it went"
Keyword matching
couldn't → negative flag
LLM scoring
Score 5 — clearly positive expression
"Bit sore but that's normal right?"
Keyword matching
sore → pain flag
LLM scoring
Score 3 + confusion flag — needs reassurance, mild discomfort, uncertainty about aftercare
"Fine"
Keyword matching
No flags
LLM scoring
Score 3 — monosyllabic, neutral at best, worth a follow-up
Dental language is context-heavy. The same word means different things in different sentences. An LLM reads the full message, understands intent, and makes a routing decision based on what the patient actually means — not which individual words are present.
The interception gate
The sentiment score is an input to a decision, not the decision itself. The interception gate is a separate layer of code that evaluates the score output and determines what action to take. This separation is deliberate and architecturally significant.
Because the gate is server-side code — not an AI instruction or prompt — it cannot be bypassed by a patient reply that attempts to manipulate the AI. No matter what a patient writes, a score of 1–3 always triggers a practice alert. A score of 4–5 and no active safety flags always triggers the review link. The logic is enforced at code level.
Score 4–5, no flags
Patient receives Google review link. Happy path. Practice team notified of new incoming review.
Score 1–3 or any flag
Practice team receives alert with full reply + flag context. Patient receives warm personal follow-up. Review link never sent.
Related: Is this the same as review gating? No — here's the legal difference.