AI for Veterinary Practices

Ambient session capture during the consultation with structured SOAP note generation from the recorded encounter. Species-specific clinical fields auto-populate from the conversation. The system recognises that a canine orthopaedic examination and a feline respiratory consultation require different structured fields and populates accordingly. A vet review and edit interface allows sign-off before the note is finalised and committed to the patient record. The system doesn't finalise any note without vet confirmation: the AI produces a draft, the vet approves it. This reduces post-consultation documentation time in high-volume practices and removes the need to transcribe from memory after the client has left. Integration with the patient record means the approved note appears in the clinical history immediately, without a separate data entry step.

Age, species, breed, weight, and clinical history combine into a risk profile for common conditions relevant to the patient's demographics. A dental disease severity score draws from previous examination findings and time since last dental. An obesity risk flag uses body condition score history and species-appropriate weight range. Chronic disease progression monitoring for diabetic patients tracks glucose trend, insulin dose change history, and compliance with monitoring appointments. Cardiac patient monitoring frequency recommendations use disease stage and last echocardiogram date. A senior patient flag shows the recommended monitoring interval by species and age. Risk scores surface at the point of appointment booking so the reception team can offer appropriate services. They also appear in the patient record header so the consulting vet has the flags before the examination begins.

Overdue patient prioritisation by species, age band, time since last visit, and service type due. This replaces a flat list of every patient overdue for anything. Optimal recall timing per patient draws from vaccination schedule, wellness plan entitlements, and the patient's historical appointment adherence. Clients who've previously responded to SMS recalls get contacted by SMS first; clients who've historically responded to email get contacted by email. Channel selection uses behaviour rather than a practice-wide default. Personalised recall message content generates by species and due service rather than a single template sent to the entire overdue list. Campaign performance tracking by patient segment shows which recall strategies convert to booked appointments.

Radiograph pre-screening for common findings across the clinical areas where the practice has the highest volume. Bone density assessment is flagged against species and age-appropriate reference ranges. Soft tissue mass identification on abdominal and thoracic films records location and size in the triage output. Pneumothorax and pleural effusion indicators on thoracic films route for immediate vet review. Spinal assessment flags intervertebral disc disease indicators on lateral views. The triage output presents as a flag for vet review with the specific finding and location noted. It's not a diagnosis, and it's not presented to the client. The purpose is to reduce the time spent reviewing normal films and to surface abnormal findings for priority review.

Client-facing chatbot for appointment booking, post-visit care instruction queries, medication refill requests, and symptom triage. Species and breed context is captured at the start of every conversation so the triage logic and responses are specific to the patient being discussed. Symptom triage output classifies as urgent (contact the practice immediately), routine (book in the next few days), or monitor at home with a specific list of signs that should prompt escalation. Medication refill requests route to the practice for prescribing vet review with the patient record and current medication list attached. Complex queries and upset clients escalate to practice staff with the full conversation context so staff don't need to ask the client to repeat themselves. Appointment booking works directly in the scheduling system from within the chatbot conversation.

Procedure and drug code suggestion generates from the content of the completed and approved SOAP note. The AI reads the documented procedures, medications administered, and diagnostic tests ordered and suggests the corresponding billing codes from the practice's price list. A controlled substance flag triggers when a dispensed drug appears in the DEA schedule list: the dispensing vet gets a prompt to complete the controlled substance log entry before the invoice is closed. Invoice pre-population from the closed visit note means the billing step starts from a near-complete invoice rather than a blank form. Missed charge identification catches any documented procedure or product that doesn't appear in the draft invoice. This reduces manual coding time per consultation and cuts missed charges in high-volume practices where invoicing happens under time pressure at checkout.