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// Path 02 — 5-phase curriculum

Healthcare
+ AI.

Clinicians, nurses, researchers, and administrators. AI will not replace clinical judgment — but professionals who use AI effectively will outperform those who do not. Privacy and safety come first.

// Core principle

"High-risk outputs must be reviewed by appropriate professionals. AI augments — it does not replace clinical judgment."

— virat-lab / healthcare-ai


EDUCATIONAL ONLY This content is for learning purposes — not medical advice. Never use real patient data (PHI) in AI tools. Always follow your organization's HIPAA policies and get compliance approval before implementing any AI workflow in clinical settings.
// The curriculum

Five phases.
Privacy first. Always.

Each phase has specific topics and a concrete artifact to build. Phase 5 is your portfolio — real work you can show.

Phase 1

AI Basics for Healthcare

Build an accurate mental model before applying AI to any clinical or administrative task.

↳ What to learn
  • What LLMs are — and are not (not a database, not deterministic)
  • AI capabilities: what it genuinely does well in healthcare contexts
  • AI limitations: hallucination, recency gaps, missing clinical context
  • Recognizing hallucinated outputs: what to check, what to verify
  • Implementing human review as a workflow step, not an afterthought
  • Safe prompting: how to get useful outputs without sharing PHI
↳ This week: practice exercise
  • Hallucination log — Use AI to answer 10 healthcare questions. Verify each answer. Document every error you find and how you caught it.
  • Safe prompting examples — Rewrite 5 prompts that originally included PHI to achieve the same goal without it.
Phase 2

Healthcare Data Awareness

Before you use AI at work, you need a clear understanding of what data you cannot share — and what happens if you do.

↳ What to learn
  • PHI and PII: the full list of identifiers that must be protected
  • Data minimization: share only what the task requires
  • Consent considerations when using AI with patient-related content
  • De-identification approaches: removing identifiers safely
  • How to evaluate whether an AI vendor is HIPAA-compliant
  • Secure handling: what to do when AI output contains sensitive info
↳ What to build
  • PHI identification exercise — Take 5 sample clinical notes (de-identified or fictional). Identify every PHI element and classify it by type.
  • AI vendor evaluation checklist — A one-page checklist for evaluating whether an AI tool is appropriate for healthcare use.
Phase 3

Practical Workflows

Apply AI to real, lower-stakes healthcare tasks. Build reliable, repeatable workflows you actually use.

↳ What to learn
  • Research summarization: how to use AI on published literature
  • Comparing clinical guidelines: structured prompts for differential review
  • Drafting patient education material at appropriate reading levels
  • Administrative automation: scheduling support, documentation
  • Quality improvement: using AI to surface patterns in process data
↳ What to build
  • Research workflow — A documented, repeatable process for summarizing 3-5 papers from your specialty using AI
  • Patient education draft — AI-assisted patient education content on a topic from your work, verified for accuracy
  • Admin automation map — Map one administrative process and document which steps can be AI-assisted
Phase 4

Safety & Compliance

Advanced practitioners design AI workflows that can be audited, justified, and safely stopped.

↳ What to learn
  • Designing human review protocols as formal workflow steps
  • Bias and fairness evaluation in healthcare AI outputs
  • Audit trail design: how to log AI use for accountability
  • Vendor assessment: what to ask before adopting any AI tool
  • Risk categorization: low-stakes vs. high-stakes AI applications
↳ What to build
  • AI workflow risk checklist — A framework for assessing any new AI use case in your organization
  • Audit log template — A structured format for documenting every AI-assisted decision in a clinical workflow
Phase 5

Portfolio Projects

Build four real artifacts. These are your portfolio — work you can share with employers, teams, or continuing education reviewers.

↳ Portfolio project 1
Healthcare research digest — A recurring workflow that uses AI to synthesize recent literature in your specialty. Include: the prompt library, the verification checklist, and 3 sample digests.
↳ Portfolio project 2
Patient education prompt library — 10+ tested prompts for generating accurate, appropriate patient education materials at different reading levels and for different conditions.
↳ Portfolio project 3
AI workflow risk assessment — A complete risk assessment of one proposed AI use case in your organization, including: risk level, controls, approval process, and monitoring plan.
↳ Portfolio project 4
Compliance-aware automation — One documented AI automation workflow that explicitly identifies PHI touchpoints, de-identification steps, human review gates, and an audit log structure.

// Prompts to use right now

Three healthcare prompts.
Privacy-safe by design.

None of these prompts require you to share PHI. Replace [brackets] with your specifics.

// Research paper summary
Role: You are a clinical research assistant helping a healthcare professional review literature.
Goal: Summarize this paper for a [specialist type, e.g. nurse educator] who will use it for continuing education.
Context: The reader needs to understand the clinical implications, not the methodology details.
Format: (1) 3-sentence overview, (2) 5 key findings as bullets, (3) one sentence on clinical implications, (4) one limitation worth knowing. Do not speculate beyond what the paper states.

[Paste the abstract or key sections of the paper here]
// Patient education material
Role: You are a healthcare communicator who writes plain-language patient materials.
Goal: Draft patient education content about [condition or procedure — use generic terms, no patient names or PHI].
Context: Patient audience: [general description, e.g. adults aged 50-70 with newly diagnosed Type 2 diabetes].
Format: Short paragraphs. 8th grade reading level. Include: (1) what it is, (2) what to expect, (3) what to watch for, (4) when to call your doctor. End with one empowering sentence.
Important: This is a draft for professional review. Do not include specific dosage or treatment recommendations.
// PHI audit before sharing
Role: You are a healthcare data privacy analyst.
Goal: Review this text and flag any PHI (Protected Health Information) that should be removed before sharing with an external AI tool.
Context: The text comes from [describe source, e.g. internal clinical notes, meeting minutes].
Format: List each PHI element with: (1) the text found, (2) PHI category (name / DOB / MRN / address / etc.), (3) recommended action (remove / replace with [X] / anonymize how).

[Paste the text you want to audit here]

// Career track
Healthcare AI Specialist

Designs and governs AI use in clinical and administrative contexts. Builds compliance-aware workflows, evaluates tools against PHI constraints, leads responsible AI adoption for clinical teams.

Target roles

Healthcare AI Coordinator · Clinical Informatics Specialist · Health AI Implementation Lead · AI-Enabled Research Analyst

All 8 career tracks →
// Interview questions
  • How do you identify what data is PHI before sending it to an AI tool?
  • How would you design a workflow that ensures AI outputs are reviewed before acting on them?
  • What questions would you ask when evaluating an AI vendor for healthcare use?
  • How would you explain AI limitations to a clinician who wants to rely on AI outputs?
// Non-negotiables
  • Never send PHI to an AI tool without a BAA in place
  • Every high-stakes AI output requires a human review step
  • Document what AI was used, when, and for what purpose