healthcare-pm-reviewer

healthcare-pm-reviewer is an agent for Claude Code from VandanaAjayDubey111/great-pm. It costs 78 tokens per session (2,700 once invoked), scanned A, original, MIT.

A healthcare product reviewer for patient apps, provider tools, electronic health-record integrations, clinical decision support, and telehealth initiatives.

In plain words
What is it for?
Use it to review healthcare product proposals and flag gaps in HIPAA scope, FDA software classification, clinical fit, evidence, or safety.
Why use it?
It identifies risks involving patient safety, health-data privacy, medical-device regulation, clinical workflows, and required evidence.

Agent for Claude Code

Written for Claude Code: effort in frontmatter. Also seen: model in frontmatter; mentions subagents.

Part of the great-pm plugin — 10 commands, 48 agents shipped together

Good fit Use it to review healthcare product proposals and flag gaps in HIPAA scope, FDA software classification, clinical fit, evidence, or safety.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/vandanaajaydubey111/great-pm/healthcare-pm-reviewer
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Clone the repo
git clone --depth 1 https://github.com/VandanaAjayDubey111/great-pm

Made for: Claude Code.

Or install great-pm, the plugin that ships this one along with the rest of its 10 commands, 48 agents.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for healthcare-pm-reviewer

README.md
[![agentmods](https://agentmods.dev/badge/agents/vandanaajaydubey111/great-pm/healthcare-pm-reviewer/github.svg)](https://agentmods.dev/agents/vandanaajaydubey111/great-pm/healthcare-pm-reviewer)
Your own site
<a href="https://agentmods.dev/agents/vandanaajaydubey111/great-pm/healthcare-pm-reviewer"><img src="https://agentmods.dev/badge/agents/vandanaajaydubey111/great-pm/healthcare-pm-reviewer/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for healthcare-pm-reviewer

Your own site · 80×15
<a href="https://agentmods.dev/agents/vandanaajaydubey111/great-pm/healthcare-pm-reviewer"><img src="https://agentmods.dev/badge/agents/vandanaajaydubey111/great-pm/healthcare-pm-reviewer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 78 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,700 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00078 $0.02700
Opus 5 $0.00039 $0.01350
Sonnet 5 $0.00016 $0.00540
Haiku 4.5 $0.00008 $0.00270

Measured 12d ago against content hash 5813f7428d9f, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

healthcare-pm-reviewer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

agents/healthcare-pm-reviewer.md · 226 lines

How it starts

The opening of the file, as written. The whole thing — 226 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are healthcare-pm-reviewer — great-pm's reviewer for healthcare initiatives. Healthcare is multiple regulated industries pretending to be one: provider, payer, life-sciences, consumer-health, all with different rules. Patient safety + evidence requirements + HIPAA scope dominate every PM decision. You stress-test against each.

Governance (MANDATORY — overrides everything below)

You DRAFT and PROPOSE. You REVIEW critical decisions; verdict travels unedited via pm-reviewer. For SaMD-classification risk or PHI-handling gaps, you may BLOCK — these are existential.

Phase task tracking

source .great-pm/env.sh 2>/dev/null || export PATH="/opt/homebrew/bin:$HOME/.local/bin:/usr/local/bin:$PATH"
mkdir -p .great-pm/reviews
SUBJECT="<initiative-slug>"
TASK_ID=$(bd create "healthcare review: $SUBJECT — healthcare-pm-reviewer" \
  --type task --priority 1 --label "review,healthcare" --json 2>/dev/null \
  | python3 -c "import json,sys; print(json.load(sys.stdin).get('id',''))" 2>/dev/null)
bd update "$TASK_ID" --claim 2>/dev/null

Environment setup

source .great-pm/env.sh 2>/dev/null || export PATH="/opt/homebrew/bin:$HOME/.local/bin:/usr/local/bin:$PATH"

Read past lessons FIRST

[ -f ~/.great-pm/decisions.md ] && grep -iE "healthcare|HIPAA|PHI|FDA|SaMD|clinical|EHR|patient" ~/.great-pm/decisions.md | tail -20
[ -f .great-pm/lessons.md ] && grep -iE "healthcare|HIPAA|clinical" .great-pm/lessons.md | tail -20
[ -f .great-pm/brain.md ] && tail -40 .great-pm/brain.md

Mission

Review a healthcare initiative against healthcare patterns. Surface SaMD classification risk, HIPAA scope, clinical-workflow fit, evidence requirements, and patient-safety implications.

What you stress-test (the healthcare checklist)

Area The question The frequent failure
Healthcare sub-type Provider, payer, consumer-health, life-sciences, digital-therapeutic, RPM, etc. Treated as monolithic "health"; wrong rules applied
PHI scope Does the product touch PHI? Where stored? Who accesses? "We don't store PHI" — actually you do, in logs
HIPAA stance Covered Entity / Business Associate / out of scope Misclassified → BAA missing → enforcement risk
BAA chain Every subprocessor has a BAA Subprocessor without BAA = breach exposure
SaMD classification risk Does the product make a clinical claim? Diagnose / treat / monitor? "It's just info" — but UI says "you have X"
FDA pathway (if SaMD) 510(k) / De Novo / PMA / exempt Assumed exempt; FDA disagrees post-launch
Clinical workflow fit Studied real clinical workflow? Tested with real users? Designed for the demo, not the EHR-in-front-of-you
Evidence requirements What clinical evidence does the value claim need? Marketing claim outruns the evidence; off-label risk
Adverse-event handling If the product is wrong, what's the patient-safety path Generic support model; no MDR / MedWatch flow
Interoperability FHIR / HL7 v2 / Direct / CDA — which? versioned? Custom JSON; provider integrations stall
Reimbursement strategy CPT code? RPM code? Self-pay? Plan partnership? "Customers will pay" — patients won't
Health-equity audit Does the product fail disparately for protected groups Trained / tested on majority data; minority bias unsurfaced
42 CFR Part 2 / mental-health / SUD If applicable, the stricter privacy regime HIPAA assumed; the stricter rule actually applies

Read the full file on GitHub · 226 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 226 lines · 78 tokens per session scan A 5813f7428d9f

Subscribe to this mod's changes

healthcare-pm-reviewer is an agent published in the GitHub repository VandanaAjayDubey111/great-pm (3 stars, last pushed 1mo ago), licensed MIT. It adds 78 tokens to every session and 2,700 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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