AI in Healthcare: Real Examples of How Practices Use ChatGPT and Claude

AI in healthcare comes in two very different kinds, and confusing them is where most owners get stuck. The first kind is clinical AI: software that reads imaging, flags risk scores, or supports diagnosis. Clinical AI is real, but it is regulated, expensive, bought by health systems, and evaluated by clinicians—and it is not what this page is about. The second kind is language AI: ChatGPT, Claude, and similar tools. Language AI lives in a browser tab, costs nothing to try, and works on the words a practice already produces every day: emails, letters, instructions, policies, notes. Every example of AI in healthcare that a mid-market practice can run this week belongs to the second kind.

The examples of AI in healthcare that work in a real practice all share the same shape. A staff member pastes in real material—a payer's coverage policy, the practice's own phone protocol, rough notes from an operations meeting—and asks for a draft, a summary, or a list of questions. The tool does the typing and the reading. The person keeps the judgment: the clinical wording in a referral letter, the tone of a patient message, the decision about what gets sent. And one rule sits above every example on this page: nothing that identifies a patient goes into a consumer AI tool. That division of labor—the tool drafts, the person decides, patient information stays out—runs through everything below.

None of the examples above touches a diagnosis or a treatment decision. Every one lives on the office side of the practice, and every one follows the same pattern: a person pastes material the practice already has—templates, policies, protocols, notes—the tool returns a draft or a checklist, and a person checks it before anything leaves. The judgment in each example stays with the person who owns it: the clinician owns the clinical wording, the manager owns what gets sent, the biller owns the appeal. That is not a limitation of the technology. It is the correct way to use the technology in a business where trust is the product.

Notice also what these examples of AI in healthcare do not require: no integration with your EHR, no software purchase, no IT project, no consultant to learn from. Every prompt on this page runs on the free plans at chatgpt.com or claude.ai (Anthropic's pricing page lists Claude's free tier), in a browser on the front-desk computer. The barrier is not budget or infrastructure. The barrier is that somebody has to sit down for twenty minutes with a real task and try it—and somebody has to write down the rule about patient information before the habit spreads on its own.

The rule is absolute: protected health information never goes into a consumer AI tool. The consumer versions of ChatGPT and Claude are not covered by a business associate agreement, and anything pasted into them should be treated as shared with an outside vendor. That means no patient names, no dates of birth, no member or claim IDs, no appointment details tied to a person, and no case description specific enough that someone could recognize the patient from the story. The prompts on this page are built around templates, public payer documents, and internal protocols for exactly that reason. Where a real document is involved, strip every identifier first—or do the work inside your own systems instead. When in doubt, the test is simple: if the text would identify a patient to a stranger, it does not get pasted.

I am a teacher, not a healthcare attorney, and nothing on this page is legal advice. HIPAA obligations belong to your practice and its compliance advisors; enterprise AI arrangements that include business associate agreements exist (Anthropic's BAA guidance for commercial customers is one example), and whether one fits your practice is a decision for those advisors, not something to improvise. What I tell every client is simpler: write your rule down before the tools spread—and they will spread the first time a draft saves someone an hour. This site publishes a plain-English AI usage policy template you can adapt in an afternoon, and a plain-English guide to ChatGPT privacy that explains what happens to the text you paste. A one-page policy the whole team has read beats an unwritten understanding.

Accuracy is the second line. ChatGPT and Claude write with the same calm fluency when they are wrong as when they are right, and in a healthcare office the wrongness has teeth: an invented billing code, a plausible-sounding regulation, a clinical claim that is almost right. The habit that keeps a practice safe is built into the prompts above—paste the source and say “based only on this.” Never let the tool supply a code, a rule, or a clinical fact from memory, and have a clinician or manager review anything patient-facing before it ships. The tool does not know your payer contracts, your state's rules, or your patients—and it will not warn you when it guesses.

Everything on this page is yours to run this week, alone, at no cost—and for many practice owners, self-teaching is the right plan for a while. The pattern I see in coaching is consistent: month one is delight, month two is a plateau. The generic prompts work, and the open questions become specific to your practice—which workflows to rebuild first, how to get the front desk, billers, and clinicians working under the same written rules, and how to make the tools earn their keep without cutting the corner that matters.

That is the point where a human teacher—not an AI coach bot, not another webinar—earns the fee. In a 1-on-1 session we open your actual workload, find the hours these tools can hand back to your staff, and build the prompts and habits around your practice, your patients, and your risk tolerance. This page is the map. Coaching is walking it together in your office.