Dr. Inge Austin, DC, RN · The AI Clinic Strategist

Dr. Inge Austin, DC, RN · The AI Clinic Strategist Clinical AI Systems Architect, DC, RN. Helping clinics get found, get booked, keep their patients.

For the past 30 years, Dr. Inge has dedicated her life to helping people physically, mentally, and spiritually. She started her career as a hospital assistant helping ENT patients to recover from cancer, followed by a Registered Nursing degree in Psychiatry and Neurology. The experience she gained by working in a mental institution has been priceless and very rewarding. She learned how to help peo

ple through the most distressing times of their lives, helped them to rebuild, and create a better future for themselves. After learning what it takes to live a successful life, she shifted her focus to peak performance. She continued her studies and obtained a Bachelor’s Degree in Exercise Science and a Doctorate in Chiropractic, which has equipped her to treat some of the best athletes in the world, with one of them being Olympic Gold Medalist, Joseph Schooling. Nowadays, Dr. Inge spends her time giving back to the community in the form of creating online courses and creating online products that will help millions of people live better lives.

Pull your last three GLP-1 cancellations. Count how many trace back to nausea nobody prepared the patient for.Most GLP-1...
06/08/2026

Pull your last three GLP-1 cancellations. Count how many trace back to nausea nobody prepared the patient for.

Most GLP-1 nausea is common, temporary, and manageable. Smaller meals. Slower eating. Plain food on the rough days, fluids between meals.

Patients rarely know any of that unless someone tells them, and the telling has a deadline: before week two, not after the complaint lands in the portal.

By the time it reads as a complaint, it has often already been decided as a cancellation. The chart logs a side effect. The revenue line logs a preventable quiet quit.

Two things worth checking in your own program this week.

One. Who tells a new patient what normal nausea looks like, and when? If the answer is "at the visit, if there is time," the answer is nobody.

Two. The red-flag version. Repeated vomiting, fluids not staying down. That needs a same-day pathway to your clinical team, not a spot in the portal queue. Patients forgive a rough week. They do not forgive feeling alone in it.

Expectations, set early, are a retention drug. The clinics that put that one conversation in week one keep patients the medication was never going to lose.

So, a real question for the room: what does a patient in your program hear about nausea before the first rough day, and who says it?

And if you want the drop-off priced in your own numbers, ask for the audit. The findings are yours either way.

Google's AI answer now sits on 88 percent of health searches. It has never read your awards wall, and it is already deci...
04/08/2026

Google's AI answer now sits on 88 percent of health searches. It has never read your awards wall, and it is already deciding which clinic gets called.

Here is the myth that answer kills: be excellent long enough and patients will find you.

That was true for twenty years. The machines answering patients today read structure, not reputation. Your entity data. Your listings agreeing with each other. Published answers they can quote. The awards wall in your waiting room is invisible to all of it.

Patients have already moved. About 32 percent of US adults took health questions to an AI chatbot last year, double the year before. Among patients who research their own care, 39 percent now use AI to help pick the provider.

So an excellent clinic with an unreadable website gets skipped, and a middling clinic with clean structure gets named. Excellent and findable are two different jobs now. Only one of them is checkable in two minutes.

Run the check. Open ChatGPT and ask for the best clinic in your specialty in your town. Read the answer twice. Once looking for your name. Once counting who is collecting the consults you assumed were yours. No login, no patient data, nothing to install.

Drop your specialty and what came back below. I read all of it.

And if you want the full map, where your clinic shows up across AI answers and search and which competitors get named instead, ask for the audit. The findings are yours either way.

The patient who cancels at day 60 never met the results she was quitting on. Getting her to day 90 is a system, and the ...
02/08/2026

The patient who cancels at day 60 never met the results she was quitting on. Getting her to day 90 is a system, and the system never needs her chart.

In GLP-1 programs the early window is the brutal one. Side effects peak, expectations meet reality, and the results that would justify staying have not arrived yet.

So the first 90 days have one job. Not delivering results. Delivering the patient to the results. That is a different job, and it takes a different system.

That system is mostly contact between visits: what to expect in week four, what the side effects mean, a message that notices the silence before the cancellation.

Here is where careful owners get careless. The everyday AI your team opens in a browser is not covered for patient data, on any standard plan. Pasting a roster into a chat window is how a retention project becomes a breach report.

The safe build keeps the two jobs apart. AI drafts the carry material: the week-by-week guides, the side-effect walkthroughs, the check-in templates. All of it from program facts, none of it from charts. Your staff attach the names, hit send, and log who went quiet.

The AI never meets a patient. The patient still gets to day 90.

So count one thing this week: the planned touches a patient gets between visit one and day 90. If the honest answer is a refill reminder, that is where your patients are going. My free audit puts that cost in your own dollars, from your own patient count and program price. The findings are yours either way.

A GLP-1 patient can respond perfectly to treatment and still cancel. The cause is not in the chart. It is in the operati...
30/07/2026

A GLP-1 patient can respond perfectly to treatment and still cancel. The cause is not in the chart. It is in the operation.

Run five checks against your own program this week. None of them are clinical. All of them decide who stays.

Check the message log. The questions your nurses answer between visits are unbilled hours, and the patient who stops asking is not satisfied. She is detaching.

Check what patients were taught about the scale. Daily weigh-ins produce noise. Water, hormones, timing. A patient reading the noise instead of the trend hears one message. Quit.

Check your cancellation dates against the calendar. Holidays and stalls are interceptable before they open, never after. The same message is a save in November and a condolence in January.

Check the language of your materials. English-only handouts retain English speakers. In a bilingual market, that is arithmetic.

Check what your pricing signals. A discount tells a wavering patient you are not sure either. A guarantee says the opposite.

The chart records the medicine. It does not record why patients leave.

Pull your last ten cancellations and set them beside these five checks. Tell me which one you find first. I read every answer.

Nobody quits your program over a translation. They just stop reading the instructions, then stop following them, then st...
29/07/2026

Nobody quits your program over a translation. They just stop reading the instructions, then stop following them, then stop coming.

Here is a PHI-free workflow worth an afternoon: take your standard patient materials, the welcome letter, the weekly guides, the side-effect instructions, and have AI rewrite them in clinical-grade Spanish. Formal register. Red-flag wording held to the same standard as the English.

Rewritten, not subtitled. A word-for-word translation reads like a phone menu. A rewrite reads like care.

The safety rule is one sentence: translate the template, never the chart. Templates are generic by design. No patient name, no date of birth, no medication list ever enters the machine. PHI exposure, zero.

Clinical control does not move either. Your clinician signs off on every line before it reaches a patient, in both languages. The AI drafts. Your team decides.

What it buys you: a patient who reads instructions in the language she thinks in follows them. Follow-through is retention. Retention is revenue you already earned once.

One question before you build anything: how many of your patients would pick Spanish materials if they existed? You do not have to guess. The answer is sitting in your intake forms.

If you want to know where your clinic shows up in AI answers and search, ask for the audit and I will explain how it works. The findings are yours either way.

5 to 10 minutes. That is what one patient question costs a nurse to answer, and none of it is billable."Is this nausea n...
27/07/2026

5 to 10 minutes. That is what one patient question costs a nurse to answer, and none of it is billable.

"Is this nausea normal." "What can I eat this week." "Did I inject this right." Every active GLP-1 patient sends them, all week.

So the answers slow down. Nobody decides that. It is what happens when a trained nurse carries an inbox on top of a patient load: replies land the next morning, or get shorter, or become "we can go over that at your next visit."

Patients learn the price of asking fast. And a patient in week four does not stop having questions. The nausea is still real. The stalled scale is still real. She just stops bringing the questions to you.

She takes them to Google, to a Facebook group of strangers, or nowhere. The program lost her weeks before the cancellation shows up on your dashboard, where it gets filed under lost motivation.

Run this check today. Pull the message threads of your last three cancellations. Find the last question each patient asked, and its date. Look at how long the answer took, and whether the questions simply stopped weeks before the patient did.

The quiet patient is not the satisfied one. She is the one deciding to leave, silently, while the inbox math keeps everyone too busy to notice.

Want to see where your clinic shows up when a patient asks an AI about your service? Ask for the audit. The findings are yours either way.

Your dashboard counted every patient who booked. It never counted the ones who quit, and that is where the revenue went....
21/07/2026

Your dashboard counted every patient who booked. It never counted the ones who quit, and that is where the revenue went.

Every vendor in this market has a five-star quote ready. It is one patient's best day.

It tells you nothing about the median. Nothing about week three. Nothing about the ones who went quiet and never said why.

Survivorship bias, with a photo attached.

Here is what I write first when I build a clinic AI system, before one patient-facing word exists.

The refusals. Every clinical question the system must stop on and route back to your medical director, with a record of why it stopped.

Then the counters. Including the ones that can embarrass me.

Not how many patients loved it. How many went quiet in week three. How many stopped opening the messages. How many the system lost and never flagged.

A system that only reports its wins is not measuring anything. It is marketing.

So the question to put to anyone selling you AI is not who loved it. It is: show me the whole distribution, including the month you would rather skip.

Watch what happens in the room. That pause is the product review.

I will take a modest number I can defend over a glowing one I have to explain.

Your turn. Open your own dashboard. Does it count the patients who booked, or the patients who stopped booking? One of those numbers is your revenue. The other one is your revenue leaving.

Which one can you actually see today?

20/07/2026

Patients have started asking an AI which clinic to call, and it answers with a short list of names. If your clinic is not on that list, you were never in the running.

Most of you know me from a different life. I was the personal chiropractor to an Olympic gold medalist, I looked after Singapore's national swim team, and I ran a chiropractic clinic on Orchard Road.

I trained as a chiropractor, I am a registered nurse, and along the way I stopped treating patients and started building the systems clinics run on. Today I work as a clinical AI systems architect.

Two kinds of work fill my week now. I build patient retention systems, the machinery that notices when a patient is drifting and routes her back to her care team. And I run visibility audits that show a clinic where it appears, and where it does not, when a patient asks an AI or a search engine who to see.

Two rules govern the new work. Everything I sell runs on my own business first. And the audit is free, because I will not charge for proof.

Here is my offer, for the clinic owners and operators reading this. Comment or message me this week and I will build the audit for your clinic: where you show up when a patient asks an AI about your service, where your booking path drops her, and what that gap costs in your own numbers.

I walk you through what I find on one 20 minute call, no pitch on it. The findings are yours either way.

In the biggest semaglutide trial, close to 39 percent of the weight lost was muscle. Worried patients now take that numb...
19/07/2026

In the biggest semaglutide trial, close to 39 percent of the weight lost was muscle. Worried patients now take that number to an AI, and the AI answers with clinic names.

The list is built from what clinics have published, and most GLP-1 programs have never said a public word about body composition.

Here is the collision. About a third of US adults now ask an AI chatbot health questions, double a year ago. And muscle loss is exactly the kind of worry that sends them asking: will I lose strength, does anyone measure this, which program protects it.

If your program already tracks composition, sets a protein target, and re-measures, you are the right answer to that question. But if your website never says so, the AI cannot know, and a competitor gets named to a patient you were built for.

The fix is not more ads. It is showing up in the sources the AI reads.

Check it in two minutes. Fresh browser, ask an AI which weight loss clinic in your city tracks muscle loss on GLP-1s. Read who gets named.

If it is not you, seeing the gap is free. I run a snapshot of where your clinic shows up in AI answers and Google, and which competitors get named instead. Ask for the snapshot, and the findings are yours either way.

Nobody on your team has an hour for week-four check-in calls. Week four is exactly when your patients decide whether to ...
18/07/2026

Nobody on your team has an hour for week-four check-in calls. Week four is exactly when your patients decide whether to quit.

GLP-1 discontinuation does not spread itself evenly across the year. It cliffs twice, first around week four, again near week twelve.

Week four is when side effects peak, the novelty fades, and a dose adjustment can unsettle the whole routine. It is also, in most programs, exactly when structured follow-up has already stopped.

That is not negligence. It is arithmetic. A personal check-in for every patient crossing week four is an hour a day nobody on your team has.

Here is the twenty-minute version. It never touches patient data.

First, have AI draft a small bank of week-four check-in messages. One for side effects, one for a fresh dose change, one plain "how is it going, answer in one word." Grade-five reading level, your clinic's voice. Your clinician reviews the bank once. The chatbot writes blank templates and sees nothing else.

Second, every Monday your front desk pulls the patients who started three to four weeks ago from your own system. Names never leave it.

Third, twenty minutes of send and log. Replies route to your team the way they always have.

The patients most likely to quit get a human touch in the exact week they are deciding. Not an hour a day. Twenty minutes on Monday.

The drop-off this catches has a dollar figure in your own books. The free visibility audit maps it from your patient count and program price. Ask for the audit and I will explain how it works. The findings are yours either way.

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