--- title: "Draft physical-therapy patient handouts from your clinic's approved protocols." description: "Build a physical-therapy education file, draft a synthetic patient handout, and check protocol sources, versions, and clinician approval before use." canonical: "https://scalewithsearch.com/articles/ai-memory-for-healthcare" date: "2026-01-28" modified: "2026-10-02" --- ## Site navigation - [Scale With Search](https://scalewithsearch.com/) - Real estate - Real estate - [Real estate](https://scalewithsearch.com/for/real-estate) - Work - Start here - [Send your brief](https://scalewithsearch.com/work#send-your-brief) - [Prepare your six-question brief](https://scalewithsearch.com/work#prepare-your-six-question-brief) - Build - [Site build, content library with SEO, signal desk](https://scalewithsearch.com/work) - For your business - Trades and home services - [Auto body and collision shops](https://scalewithsearch.com/for/auto-body-and-collision-shops) - [Foundation and home repair contractors](https://scalewithsearch.com/for/foundation-and-home-repair) - 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You ask ChatGPT for a patient education email about post-operative care. It returns generic advice that does not match your physical-therapy clinic's protocols. You try again and paste your standard post-op instructions. The draft improves. It still does not know that your patients skew older, need simpler language, and respond poorly to medical jargon. So you teach the assistant your practice again in every session. Then there is the HIPAA question. You cannot paste patient information into an AI tool that has no business associate agreement with your practice. So you strip out all context, get generic output, and spend 20 minutes to rewrite it. A physical therapist needs patient-education drafts that match the clinic's approved protocols, reading level, and handout format. This page shows how to keep those sources in one file and check a draft before a clinician approves it. Patient-specific rehabilitation records follow the [physical-therapist workflow](/articles/ai-for-physical-therapists), including surgeon-specific restrictions. ## What the assistant must know about your practice Physical-therapy education depends on context. A clinic serving post-surgical patients uses different approved handouts from a clinic serving weekend athletes. The assistant needs the current clinic source, not a progression invented from general knowledge. The assistant needs these facts: - your treatment protocols and post-care instructions; - communication patterns that work with your patient population; - insurance pre-authorization language for your common procedures; - referral preferences and specialist contacts; - compliance rules for your state and specialty; - standard answers to common patient questions. None of these facts is protected health information (PHI) when you write them as practice knowledge. Today they live in your head, so you type them into each AI conversation or do without. ## How CLAUDE.md holds your practice context `CLAUDE.md` is a Markdown file on your computer. You write your practice context once: treatment approaches, communication patterns, clinic workflows, and referral preferences. Claude Code reads the file at the start of each session in that folder. The file stays on your machine. The model does not. When Claude Code reads the file and your request, that text goes to the provider for processing. Review the provider terms even for practice knowledge. Patient data stays out of this folder. Here is a synthetic example for a sports medicine physical-therapy clinic. The mix and protocol placeholders are invented: ```markdown ## Practice context - Specialty: sports medicine physical therapy - Patient mix: 60% weekend athletes, 30% post-surgical, 10% chronic pain - Style: conversational, no clinical jargon, focus on activity goals - Payer mix: 70% private insurance, 20% Medicare, 10% self-pay ## Treatment protocols - ACL post-op: [approved surgeon/clinic protocol path, version, review date] - Rotator cuff: [your preferred exercise sequence] - Runner's knee: [your assessment and treatment approach] ## Patient education - Post-visit summaries: 3 to 4 bullet points, focus on next-session goals - Home exercise programs: video links first, written backup - Pain guidance: normal soreness vs. warning signs that need a call ## Referral network - Orthopedic surgery: SURGEON-A (knee and shoulder), SURGEON-B (spine) - Imaging: IMAGING-1 (accepts our main payers, fast turnaround) - Pain management: do not refer to CLINIC-X (poor patient feedback) ``` Now ask Claude for a post-visit summary template for an ACL patient. It follows your protocol progression, uses your communication style, and refers to your standard home exercise format. You do not paste the protocol again. ## Four clinic jobs the file supports ### Patient communication templates You see 15 patients a day. Half of them need a follow-up email: exercise reminders, scheduling steps, or answers to questions they forgot to ask. Your file stores your communication patterns once: tone, structure, common questions, and clinic policies. Claude knows that you prefer short paragraphs. It knows that you always include your direct line for urgent concerns and link to your exercise videos. The assistant does not need a patient's name or diagnosis to write in your voice. It needs to know how you talk to patients. You add the patient details inside your patient portal or email system, not in the AI session. ### Insurance pre-authorization language You request approval for eight more PT sessions. The payer wants medical necessity documentation in its preferred format. Your file includes: - objective measure improvements that meet the payer's criteria; - language patterns from requests that the payer approved; - CPT code justifications for your common treatments; - functional limitation phrases that the payer accepts. You give Claude de-identified progress metrics: no name, no date of birth, no record number. It drafts the request in the payer's format. You add the identifiers in your own system before you submit. ### Documentation structure You have used the same SOAP note structure for six years: subjective report, objective measures, assessment of progress, and the plan for next session. Your file stores that pattern: - your standard objective measures: range of motion, strength grades, functional tests; - assessment language that meets documentation requirements; - plan format: exercise progression, manual therapy, modalities; - billing code justifications. Session notes are PHI. Before you dictate notes into any AI session, confirm two conditions. First, your practice has a business associate agreement with the vendor for the product you use. Second, your compliance officer approves the workflow. With both in place, you speak your session notes aloud, and Claude structures them into your template with your terms. Without the agreement, use the template for structure only and write the patient content in your EHR. ### A synthetic clinician-approved handout This handout and its approval record are invented examples. They do not provide a rehabilitation protocol or replace a patient's plan. ```text source:: patient-education/after-visit-v3.md version:: 3; dated 2026.09.18 approval:: clinic PT reviewed 2026.09.20 (synthetic) protocol:: protocols/post-op-current.md; clinician verifies surgeon version After your physical-therapy visit Keep the home-exercise plan your therapist reviewed with you. Bring that plan and your questions to your next appointment. This handout adds no exercises, dosage, or progression to your plan. For questions, use the contact method printed on your clinic plan. ``` Ask the assistant to draft from version 3 only. The clinician checks each sentence against the approved source, including contact instructions. If a newer protocol replaces that version, hold the draft until the clinic reviews it again. The approval date in this example does not approve a real handout. Course notes may inform a clinician's source review. They must not become new treatment instructions through an AI draft. Keep exercise selection and progression with the treating clinician. ## Keep protected health information out of the file Do not put these items in `CLAUDE.md`: - patient names, dates of birth, and medical record numbers; - diagnoses tied to individuals; - treatment outcomes for specific people. Those items are PHI. They stay in your EHR. These items belong in the file: treatment protocols, communication templates, referral network, pre-authorization language, and clinic policies. That is practice knowledge. [What client data belongs in AI agent memory](/articles/client-data-in-ai-agent-memory) gives the general test for any personal record. The file is not the only exposure point. Anything you type into a session also goes to the provider. [HHS cloud-computing guidance](https://www.hhs.gov/hipaa/for-professionals/special-topics/health-information-technology/cloud-computing/index.html), checked 2026.10.02, addresses business associate agreements and safeguards when a vendor handles ePHI for a covered entity. Check whether the vendor offers one for the plan you use, and get it in writing. Check the vendor's data retention and training terms, and record what you found and when. Local controls still help. Encrypt the disk. Review every line in the file. They protect the file at rest. They do not change what the provider receives during a session. [An AI memory retention and deletion policy](/articles/ai-memory-retention-and-deletion-policy) helps you set how long drafts and logs stay on your machine. ## Test the file with synthetic cases Run this check before the file enters daily use: 1. Start a new Claude Code session in the practice folder. 2. Ask for the synthetic handout from the approved source and its current version. 3. Pass: every line matches that source. Fail: an invented exercise, dosage, or progression. Change the protocol version and start a fresh session; it must flag the old approval for review. 4. Ask which imaging center to use, and compare the answer with the referral list. 5. Search the file for any patient name, date of birth, or record number, and remove each one. The test passes when the output follows your protocols and the file holds no PHI. Use invented cases for every test. [What context an agent should read](/articles/what-context-should-an-agent-read) explains how to limit the files a session may open. ## What changes when the assistant knows your practice Without a practice file, some clinicians avoid AI because of compliance concerns. Others strip out so much context that the output barely helps. A patient education email that should take two minutes takes 15. Pre-authorization requests get rewritten from scratch. Documentation style gets explained to the tool for the hundredth time. With a practice file, the assistant starts each session with your protocols, voice, and formats. It drafts the reusable parts. Patient-specific content stays in systems that your compliance program covers. ## Approval and stopping boundary The assistant drafts templates, pre-authorization language, documentation formats, and education materials. A licensed clinician approves every clinical statement before it reaches a patient or a payer. The assistant does not contact patients, submit claims, or write to the EHR. [Who approves what an AI agent sends](/articles/who-approves-what-an-ai-agent-sends) sets out the approval roles. 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