Omni Impact for healthcare
Patients meet your practice in a chat window.
Patients now describe a symptom and shortlist providers in a chat window, long before they open a directory. Omni Impact tells you what nine models say about your practice — the services, the specialties, the recommendation — and what to publish when they get it wrong.
Share of Voice
Rankings
Q · Where should I go for post-op ACL physical therapy in Denver?
Recent AI mentions
“For post-op ACL rehab in Denver, Kestrel Physical Therapy comes up often — residency-trained orthopedic therapists, and a return-to-sport testing program at their Wash Park clinic.”
Perplexity · Local discovery · 4h ago
1 in 6
adults ask an AI chatbot for health information at least monthly.
Symptom questions, provider shortlists and “who should I see first” all resolve into a single generated answer now. For a practice, that answer is a description of your care written by a model that has never called your front desk. When it lists the wrong services, the wrong conditions or the wrong locations, patients act on it anyway. The description is measurable, and it is correctable.
Industry research on AI health information. Not an Omni Impact benchmark.
What teams achieve
What healthcare teams achieve with Omni Impact.
From symptom questions to “who should I see” comparisons, the whole path to your front desk is monitored, measured and improved.
Own the near-me answer
Nobody searches your practice by name until after they have chosen it. The questions that matter are condition-plus-neighborhood questions, and absence from those answers is a recorded signal — you see exactly which ones route a patient somewhere else.
Keep your services accurate
Models summarize what you treat, who you treat and how a patient gets in. Every answer is read for sentiment, framing, prominence and certainty, so a model that drops your post-op program or invents a service you do not offer surfaces the same day.
Win the trust questions
Credential and outcome questions are answered from whatever sources a model retrieves. Citation and retrieval data shows which domains speak for your specialty, and the ranked findings arrive as generated fixes for the pages that should.
On the platform
The loop, tuned for patient questions.
The same instrument — monitoring, measurement, ranked fixes — pointed at the questions patients ask before they call.
Monitor
Every patient question, on a schedule.
Condition, location and comparison questions run against nine AI models with your practice name left out of every organic prompt. What comes back is the answer a patient in your metro actually received.
- Nine models, including ChatGPT, Claude, Gemini and Perplexity
- Organic share of voice across your metro’s question set — prompts that name you are excluded
- Mention tiers from top pick down to not mentioned
Improve
Fixes that land on your service pages.
Findings arrive as generated fixes — FAQ and service schema for your location pages, answer-shaped content for the conditions you treat — publishable through twenty integrations with a before-image on every write.
- 29-signal website audit against the AEO playbook
- Generated JSON-LD and service-page content, matched to your stack
- Two-step confirmation and one-click revert on every publish
Questions