::play 01 · SBAR handoff drafter (PHI-scrubbed)
Local Ollama (Llama 3.1 8B or Mistral 7B) — keeps PHI on device. If using cloud, scrub all identifiers first.
Draft a clean SBAR for shift change or on-call handoff in 60 seconds, then verify against the chart before saying it out loud.
You are drafting an SBAR handoff for a registered nurse. Use this raw shift information and produce a tight SBAR. Do NOT invent any clinical detail not provided. Flag anything missing in a 'GAPS' section at the end.
SITUATION (one line): [age range, sex, primary admit reason, hospital day #]
BACKGROUND: [relevant PMH, allergies, code status, baseline mental status, isolation precautions]
ASSESSMENT: [current vitals range, mental status, pain level, lines/drains/airway, last labs of note, last imaging of note, current concerns]
RECOMMENDATION: [what needs done this shift, pending consults, pending results, family situation]
Draft the SBAR. Use clinical shorthand a nurse would use. No fluff. End with 'GAPS:' listing what's missing from the above that would normally be in a handoff (e.g., 'no code status documented', 'no allergy info').
::what to notice · The 'GAPS' section is the whole game. AI is best at noticing what you forgot to mention. Read the gaps list out loud before you give the handoff.
::trap · Don't paste the actual chart. The prompt template uses placeholders for a reason — you fill in scrubbed data, AI structures it. If you cloud-paste real PHI, you've created a breach event. Use local Ollama or a BAA tool.
::play 02 · Plain-language discharge instructions
Claude or ChatGPT (PHI-scrubbed) — this is general translation work, no identifiers needed in the prompt.
Convert clinical discharge instructions into 6th-grade reading level a patient can actually follow at home.
Rewrite the following discharge instructions for a patient at a 6th-grade reading level. Constraints:
- Use short sentences, one idea per sentence.
- Replace medical terms with plain words but include the medical term in parentheses the first time (e.g., 'blood thinner (anticoagulant)').
- Group into sections: 'What you have', 'Medicines to take', 'Things to watch for', 'When to call us', 'When to go to the ER', 'Your follow-up'.
- 'When to go to the ER' must use the exact red-flag symptoms I provide and nothing else.
- Do NOT add medical advice I didn't give you. Do NOT invent symptoms, doses, or follow-up timing.
- At the end, list any medical claim you weren't sure about under 'CLINICIAN REVIEW NEEDED'.
ORIGINAL INSTRUCTIONS: [paste clinical instructions here — no patient identifiers]
RED FLAGS THAT MEAN GO TO ER: [list the specific symptoms]
FOLLOW-UP: [appointment type and timing only — no scheduling specifics]
::what to notice · Reading level matters. The average U.S. adult reads at 8th grade. Patients in stress or pain drop 2-3 grades below baseline. Sixth grade is the target.
::trap · AI will helpfully add 'watch for signs of infection' even if you didn't say so. Read every line and delete any clinical claim you didn't author. The 'CLINICIAN REVIEW NEEDED' section is where it confesses what it guessed at.
::play 03 · Family communication prep (difficult conversation)
Claude or ChatGPT — this is rehearsal, no real patient data needed beyond a sanitized scenario.
Rehearse a family meeting before you walk in. Goals of care, code status changes, bad news, transition to hospice — the conversations that go badly when you wing them.
I'm a [role: RN, NP, MD, etc.] preparing for a family meeting. Help me rehearse. Do NOT script me — give me a framework, anticipated questions, and language options.
SCENARIO (sanitized, no identifiers): [e.g., '78-year-old admitted 6 days ago with sepsis from pneumonia, now intubated and on three pressors, family hasn't accepted prognosis']
MEETING GOAL: [e.g., 'introduce hospice as next step', 'clarify code status', 'deliver new cancer diagnosis']
FAMILY DYNAMIC (what I know): [e.g., 'spouse is primary, two adult kids disagree, one wants 'everything done'']
MY ROLE: [bedside RN, primary nurse, attending, charge, palliative consult, etc.]
Give me:
1) A 4-step framework for opening the meeting.
2) Three things I should NOT say (common landmines for this scenario).
3) Five anticipated questions and language options for responding.
4) Two scripts I can use to redirect if it goes sideways.
5) A closing move that ends the meeting cleanly without forcing a decision the family isn't ready to make.
Use VitalTalk and Ariadne Labs Serious Illness Conversation patterns if you know them. Cite the framework you're drawing from.
::what to notice · Rehearse. Don't recite. The point is to be ready for the three things the family will say that you didn't anticipate. AI is a sparring partner, not a script.
::trap · Do not paste real names, MRNs, or specific timeline detail. Sanitize. And never read AI output verbatim to a family — it sounds scripted, and grieving families notice.
::play 04 · Evidence synthesis before shift
Elicit.org (academic corpus, traceable citations) or Claude with web access — verify every citation.
Get a clean summary of the last 12 months of literature on a clinical question before you see the patient or write the note.
Synthesize the recent peer-reviewed literature on: [specific clinical question, e.g., 'optimal duration of empiric antibiotics for ventilator-associated pneumonia in adults without immunocompromise'].
Constraints:
- Only use peer-reviewed sources from the last 5 years unless landmark older work is essential.
- Cite every claim with author, year, journal, and DOI or PMID.
- Structure: (1) Current standard of care per major guideline (IDSA, ATS, NICE — name which), (2) Areas of recent change or controversy, (3) Strength of evidence (RCT, meta-analysis, observational, expert opinion), (4) Open questions.
- If a citation doesn't exist or you can't verify it, say 'UNVERIFIED' next to it. Do NOT fabricate DOIs.
- End with 'PRACTICAL TAKEAWAY' — three bullets I can use at the bedside today.
Do NOT include patient-specific advice. This is a literature scan, not a treatment plan.
::what to notice · The 'UNVERIFIED' tag is your single best AI hygiene practice for research. Trust nothing without a working DOI or PMID. Hallucinated citations are the #1 way AI gets clinicians embarrassed in M&M.
::trap · AI is famous for inventing journal articles that sound real. Author, year, journal — all fabricated. Always click the DOI before you cite it in anything that leaves your laptop.
::play 05 · Documentation pattern audit (self-review)
Local Ollama only (notes contain PHI). If using cloud, every identifier must be replaced before paste.
Find the patterns in your own charting that are hurting you — undercoded encounters, missed billing modifiers, vague language that triggers denials, omitted required elements.
You are auditing my clinical documentation for completeness and billing optimization. I'm going to paste 5-10 of my recent notes (PHI scrubbed and replaced with placeholders).
For each note, identify:
1) Missing required elements for the documented level of service (e.g., for a 99214, did I document 2/3 of: detailed history, detailed exam, moderate MDM?).
2) Vague language that an auditor or payer would flag (e.g., 'doing well', 'unchanged', 'as previously').
3) Documented work I didn't get credit for (procedures, time-based services, complexity not reflected in the code).
4) Risk patterns (incomplete med reconciliation, missing allergy verification, absent code status, etc.).
Do NOT add clinical detail I didn't document. Only flag what's missing. End with a 'PATTERN' section showing what shows up across multiple notes — that's the habit to fix.
NOTES (all PHI replaced with [PATIENT], [DOB], etc.): [paste]
::what to notice · The PATTERN section is gold. Individual notes can be sloppy; recurring sloppiness is what gets flagged in an audit or costs you money on every encounter.
::trap · PHI in cloud AI for this workflow is a breach. Local Ollama is the only safe path unless your hospital has a BAA-covered tool. Don't shortcut this.
::play 06 · On-call handoff prep (incoming shift)
Local Ollama only (handoff sheets are PHI-dense).
Get yourself oriented to a panel of 20-30 patients you've never seen, in 10 minutes, before you take call.
You are helping me prep for on-call coverage. I'm going to paste the outgoing handoff sheet (PHI scrubbed). For each patient, produce:
1) A one-sentence 'why they're here' summary.
2) The three things most likely to need a call overnight (based on the documented issues).
3) The one thing I should verify before the outgoing team leaves (a gap, an ambiguity, a pending result).
4) A risk tier (LOW / MED / HIGH) for overnight instability.
Group the HIGH-tier patients at the top. Be terse. This is for memorization, not narrative.
Do NOT invent clinical detail. If the handoff sheet is incomplete, flag it. End with a 'CALL OUT' section listing the 3-5 patients I should physically lay eyes on within the first hour of the shift.
HANDOFF SHEET (PHI scrubbed): [paste]
::what to notice · The 'CALL OUT' section turns a 30-patient panel into a 5-patient priority list. That's the whole point of this workflow — directing your scarce attention.
::trap · Don't trust the risk tier blindly. AI ranks based on documented complexity, not on clinical instinct. A patient that looks 'LOW' on paper but the outgoing nurse pulled you aside about — believe the nurse.
::play 07 · Patient question triage (within scope)
Claude or ChatGPT (PHI-scrubbed in prompt — replace patient identifiers with [PATIENT]).
Draft responses to patient portal messages or callback questions, scope-bounded and reviewed before send.
Draft a response to a patient portal message. Constraints:
- Stay strictly within the scope of [your role: RN, MA, NP, MD].
- If the question requires a clinical decision outside my scope, the response should be 'I need to forward this to [provider role] — they'll respond within [timeframe].'
- Use 6th-grade reading level.
- Acknowledge the patient's concern in the first sentence.
- Give one clear action step.
- Include red-flag symptoms that mean call back or go to ER (only if clinically relevant).
- Sign-off appropriate to my role.
- Length: under 150 words.
PATIENT MESSAGE (identifiers removed): [paste]
KNOWN CONTEXT (from chart, identifiers removed): [paste relevant clinical context — diagnoses, current meds class only, recent visits, allergies]
MY ROLE: [RN, MA, NP, etc.]
MY SCOPE LIMITS HERE: [e.g., 'cannot adjust meds', 'cannot order labs', 'can advise on home care for existing conditions']
::what to notice · The scope-limit section is what keeps you legal. AI will happily draft a response that exceeds your scope of practice. You enforce the scope; AI fills in the language.
::trap · Never use this to draft a response to a symptom complaint that could be serious without escalation. 'Chest pain' is not a portal message — that's a phone call from a clinician. AI doesn't triage acuity.
::play 08 · Insurance appeal letter scaffold
Claude or ChatGPT (PHI-scrubbed) — final letter is reviewed and signed by clinician.
Draft a denial appeal that cites the clinical basis, the policy, and the relevant evidence — in the format the payer expects.
Draft an insurance appeal letter for a denied [service: prior auth, claim, level of care, etc.]. Format per standard payer expectations.
DENIAL REASON (from EOB or denial letter, identifiers removed): [paste]
SERVICE BEING APPEALED: [e.g., 'inpatient admission days 4-7', 'MRI lumbar spine', 'GLP-1 agonist for T2DM']
CLINICAL JUSTIFICATION (deidentified): [the actual clinical story]
GUIDELINE OR EVIDENCE SUPPORTING: [name the guideline — e.g., MCG, InterQual, IDSA, ACC/AHA — and any relevant studies]
PATIENT-SPECIFIC FACTORS: [comorbidities, failed prior treatments, contraindications — all deidentified]
Produce:
1) A formal appeal letter with: (a) policy citation if you have one, (b) clinical narrative, (c) evidence/guideline citation, (d) requested resolution.
2) Flag any claim where the citation needs verification.
3) Suggest the two strongest arguments to lead with based on the denial reason.
Tone: firm, factual, no hostility. Length: under one page.
::what to notice · Lead with the strongest argument. Payers skim. The first paragraph either gets you reconsidered or gets you a second denial.
::trap · Verify every guideline citation. AI invents guideline numbers and policy versions. A wrong cite torpedoes the whole appeal and damages your credibility on the next one.