Checking Hipaa Compliance logo

Checking Hipaa Compliance

CommunityPopular
maziyarpanahi
checking-hipaa-compliance

Runs a HIPAA Privacy and Security Rule checklist over a data pipeline and produces a gap report before deploying OpenMed on PHI. Use when the user is about to process protected health information, needs a pre-deployment compliance review, wants to know which administrative, physical, and technical safeguards apply, is scoping a Business Associate Agreement, or must document minimum-necessary and de-identification controls. Trigger keywords: HIPAA, Privacy Rule, Security Rule, 45 CFR 164, PHI, BAA, business associate, minimum necessary, safeguards, Safe Harbor, Expert Determination, gap analysis, compliance review. Pairs adjacent to OpenMed: the checklist shows where openmed.deidentify and signed audit reports satisfy the de-identification and audit-control requirements. The control list lives in references/hipaa-checklist.md. This is a structured self-assessment aid, not legal advice.

Overview

Publishermaziyarpanahi
Repositoryopenmed
Skill namechecking-hipaa-compliance
Stars
5.3K
Forks
677
Bundled files
1
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by maziyarpanahi on GitHub. Read the source before you install it.

Installation

Install the Checking Hipaa Compliance AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/maziyarpanahi/openmed.git /tmp/openmed
mkdir -p .claude/skills
cp -r /tmp/openmed/skills/checking-hipaa-compliance .claude/skills/checking-hipaa-compliance
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Checking Hipaa Compliance in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Checking Hipaa Compliance on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Checking Hipaa Compliance is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Checking HIPAA compliance before deploying on PHI

Before any pipeline touches protected health information (PHI), the operating entity (a covered entity or its business associate) must have the HIPAA Privacy Rule and Security Rule safeguards in place. This skill walks a concrete pipeline against those requirements and emits a gap report: which controls are met, which are missing, and where OpenMed's on-device de-identification and signed audit trail satisfy a requirement.

The full control list — administrative, physical, and technical safeguards with their 45 CFR citations — is in references/hipaa-checklist.md. This skill is a self-assessment aid, not legal advice; a Privacy Officer signs off on compliance.

When to use

  • You are about to deploy OpenMed (or any pipeline) on real PHI and need a go/no-go compliance review.
  • You must document safeguards and minimum-necessary controls for an audit, a BAA, or a security questionnaire.
  • You want to decide between Safe Harbor and Expert Determination de-identification and record the rationale.
  • You need a reproducible gap report you can hand to a Privacy/Security Officer.

The two paths to "no longer PHI"

HIPAA recognizes two de-identification methods (45 CFR 164.514):

  1. Safe Harbor — remove all 18 identifier categories and have no actual knowledge the result can re-identify. Deterministic, the common path.
  2. Expert Determination — a qualified statistician certifies "very small" re-identification risk. Used when you must keep some quasi-identifiers.

OpenMed's deidentify(..., policy="hipaa_safe_harbor") targets the Safe Harbor identifier set on-device, and deidentify(..., audit=True) produces a signed, PHI-free AuditReport that documents what was removed — the evidence a Safe Harbor attestation and a Security Rule audit control both want.

Quick start

python
import openmed

# A representative record from the pipeline (synthetic — never log real PHI).
sample = "John Doe (MRN 1234567), DOB 1970-01-15, seen 2024-03-02 in Boston."

# 1) De-identify on-device under the Safe Harbor policy.
result = openmed.deidentify(sample, method="replace", policy="hipaa_safe_harbor")
print(result.deidentified_text)        # identifiers removed/surrogated

# 2) Produce the signed, no-PHI audit record for the compliance file.
report = openmed.deidentify(sample, policy="hipaa_safe_harbor", audit=True)
report.sign(b"<release-hmac-key-from-vault>", key_id="hipaa-2026")

# 3) Walk the checklist (see references/hipaa-checklist.md) and record gaps.
controls = {
    "encryption_at_rest": True,
    "encryption_in_transit": True,
    "access_controls_rbac": True,
    "audit_logging": True,          # satisfied in part by the signed AuditReport
    "minimum_necessary": False,     # <-- gap: pipeline pulls full notes
    "baa_in_place": True,
    "deidentification_method": "safe_harbor",
}
gaps = [name for name, ok in controls.items() if not ok]
print("GAPS:", gaps)

Workflow

  1. Map the data flow. Diagram every place PHI is created, received, maintained, or transmitted — including model caches, temp files, and logs.
  2. Confirm the legal basis. Is the operator a covered entity or business associate? Is a BAA in place with every downstream vendor that touches PHI? OpenMed running on-device means no third-party processor for the NLP step — note that as a control in your favor.
  3. Run the three safeguard groups from references/hipaa-checklist.md: administrative (risk analysis, workforce training, sanctions), physical (facility/device controls), and technical (access control, audit controls, integrity, transmission security).
  4. Enforce minimum necessary. Pull only the fields the task needs; mask the rest. De-identify as early in the flow as the use case allows.
  5. Record the de-identification method (Safe Harbor vs Expert Determination) and attach the signed AuditReport as evidence.
  6. Emit the gap report — met / not-met / N/A per control, with the 45 CFR citation and the remediation owner. Hand it to the Privacy Officer.

Hand-off to / from OpenMed

  • De-identification: deidentifying-clinical-text (openmed.deidentify, policy="hipaa_safe_harbor") is the technical control that converts PHI to non-PHI on-device — the heart of a HIPAA pipeline.
  • Identifier coverage: auditing-safe-harbor-checklist maps detected spans to the 18 Safe Harbor categories so you can prove each is handled.
  • Audit control: auditing-deidentification-runs (audit=TrueAuditReport.sign()/.verify()) gives the tamper-evident, PHI-free record the Security Rule audit-controls standard (164.312(b)) expects.
  • No-PHI logging: enforcing-nophi-logging keeps identifiers out of logs and traces (a recurring audit finding).
  • OpenMed is local-first — the NLP step adds no new business associate.

Edge cases & gotchas

  • De-identified data is out of scope — but only if done right. Safe Harbor requires all 18 categories removed and no actual knowledge of re-identifiability. A residual rare ZIP3 or a free-text name the model missed re-introduces PHI. Verify coverage; don't assume.
  • Limited Data Sets are still PHI. Dates and ZIPs retained under a Data Use Agreement (164.514(e)) are not de-identified — different rules apply.
  • Logs and caches are PHI too. Model caches, exception messages, and temp files holding raw notes are in scope. This is the most common gap.
  • A BAA is required for every vendor that creates/receives/maintains/ transmits PHI on your behalf — including cloud storage and any LLM API. Running OpenMed on-device avoids adding one for the NLP step.
  • Minimum necessary is a duty, not a nicety (164.502(b)). Don't pull full charts when a problem list suffices.
  • Breach notification clock. Unsecured PHI exposure triggers 164.400-414 duties; encryption to NIST standards renders data "secured" and can avoid the notification trigger.
  • Not legal advice. This checklist supports, but does not replace, a Privacy Officer's determination and (for Expert Determination) a qualified statistician.

Standards & references

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Checking Hipaa Compliance AI skill do?

Runs a HIPAA Privacy and Security Rule checklist over a data pipeline and produces a gap report before deploying OpenMed on PHI. Use when the user is about to process protected health information, needs a pre-deployment compliance review, wants to know which administrative, physical, and technical safeguards apply, is scoping a Business Associate Agreement, or must document minimum-necessary and de-identification controls. Trigger keywords: HIPAA, Privacy Rule, Security Rule, 45 CFR 164, PHI, BAA, business associate, minimum necessary, safeguards, Safe Harbor, Expert Determination, gap anal...

Why use Checking Hipaa Compliance on TypingMind?

Because you install it once and use it with any model. Checking Hipaa Compliance is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Checking Hipaa Compliance in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/maziyarpanahi/openmed/tree/master/skills/checking-hipaa-compliance. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Checking Hipaa Compliance?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Checking Hipaa Compliance?

As many as you like. As long as a model supports skills, you can use Checking Hipaa Compliance with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Checking Hipaa Compliance AI skill free?

Yes. It is published on GitHub by maziyarpanahi under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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