13. AI for Doctors and Health Professionals

Course Positioning

This course teaches safe AI use in healthcare administration, patient communication, clinical documentation support, literature review, medical education, triage workflow design, and quality improvement. It does not train clinicians to delegate diagnosis to AI. It emphasizes patient safety, privacy, bias, clinical governance, and human accountability.

Learning outcomes

Identify healthcare tasks where AI can assist safely: documentation, education, summarization, patient instructions, research, operations.
Use AI to prepare patient-friendly explanations, discharge instructions, FAQs, and clinician education materials with review.
Understand risks of hallucination, bias, privacy breach, automation bias, and unsafe clinical recommendations.
Design human-in-the-loop workflows for AI-assisted screening, triage, or administrative support.
Build a healthcare AI implementation checklist for one low-risk use case.

Expanded Topic-by-Topic Coverage

Module 1. AI in healthcare: promise and boundaries

Module focus: Clinical admin, patient education, literature support, imaging/diagnostics overview, triage, operations, safety. Primary live activity or lab: Classify use cases by clinical risk. Expected take-home output: Healthcare AI risk map.

Topics and coverage

Clinical admin

What it means: show where Clinical admin appears in the learner's real workflow and which parts are judgment-heavy versus draftable.
What to cover: current workflow, pain points, AI-assisted steps, human review checkpoints, quality standard, and ownership of the final decision.
Demonstration: convert one messy real-world input into a structured brief, draft, analysis, checklist, or next action.
Evidence of learning: learners produce a reusable template or playbook entry that can be used after the course.

patient education

What it means: define patient education clearly and connect it to the module focus: Clinical admin, patient education, literature support, imaging/diagnostics overview, triage, operations, safety.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

literature support

What it means: show where literature support appears in the learner's real workflow and which parts are judgment-heavy versus draftable.
What to cover: current workflow, pain points, AI-assisted steps, human review checkpoints, quality standard, and ownership of the final decision.
Demonstration: convert one messy real-world input into a structured brief, draft, analysis, checklist, or next action.
Evidence of learning: learners produce a reusable template or playbook entry that can be used after the course.

imaging/diagnostics overview

What it means: define imaging/diagnostics overview clearly and connect it to the module focus: Clinical admin, patient education, literature support, imaging/diagnostics overview, triage, operations, safety.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

triage

What it means: define triage clearly and connect it to the module focus: Clinical admin, patient education, literature support, imaging/diagnostics overview, triage, operations, safety.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

operations

What it means: show where operations appears in the learner's real workflow and which parts are judgment-heavy versus draftable.
What to cover: current workflow, pain points, AI-assisted steps, human review checkpoints, quality standard, and ownership of the final decision.
Demonstration: convert one messy real-world input into a structured brief, draft, analysis, checklist, or next action.
Evidence of learning: learners produce a reusable template or playbook entry that can be used after the course.

safety

What it means in this course: define safety in operational terms, not as an abstract principle.
What to cover: sensitive data boundaries, affected stakeholders, approval paths, documentation, and what doctors, nurses, allied health professionals, hospital administrators, healthcare founders must never delegate blindly to AI.
Use case: present one acceptable use, one borderline use, and one prohibited use, then ask learners to justify the classification.
Evidence of learning: learners add a risk control, review step, or escalation rule to their course project.

Practice and evidence of learning

Learners complete or discuss: Classify use cases by clinical risk.
Learners produce: Healthcare AI risk map.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 2. Privacy and data governance

Module focus: Patient identifiers, consent, medical records, secure tools, de-identification, access controls. Primary live activity or lab: Rewrite prompts to remove unnecessary patient identifiers. Expected take-home output: Safe prompt checklist.

Topics and coverage

Patient identifiers

What it means: define Patient identifiers clearly and connect it to the module focus: Patient identifiers, consent, medical records, secure tools, de-identification, access controls.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

What it means: define consent clearly and connect it to the module focus: Patient identifiers, consent, medical records, secure tools, de-identification, access controls.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

medical records

What it means: define medical records clearly and connect it to the module focus: Patient identifiers, consent, medical records, secure tools, de-identification, access controls.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

secure tools

What it means: define secure tools clearly and connect it to the module focus: Patient identifiers, consent, medical records, secure tools, de-identification, access controls.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

de-identification

What it means: define de-identification clearly and connect it to the module focus: Patient identifiers, consent, medical records, secure tools, de-identification, access controls.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

access controls

What it means: define access controls clearly and connect it to the module focus: Patient identifiers, consent, medical records, secure tools, de-identification, access controls.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Rewrite prompts to remove unnecessary patient identifiers.
Learners produce: Safe prompt checklist.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 3. Clinical documentation support

Module focus: SOAP notes, discharge summaries, referral letters, visit summaries, medical language simplification. Primary live activity or lab: Turn fictional consultation notes into a structured summary. Expected take-home output: Documentation workflow.

Topics and coverage

SOAP notes

What it means: define SOAP notes clearly and connect it to the module focus: SOAP notes, discharge summaries, referral letters, visit summaries, medical language simplification.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

discharge summaries

What it means: define discharge summaries clearly and connect it to the module focus: SOAP notes, discharge summaries, referral letters, visit summaries, medical language simplification.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

referral letters

What it means: define referral letters clearly and connect it to the module focus: SOAP notes, discharge summaries, referral letters, visit summaries, medical language simplification.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

visit summaries

What it means: define visit summaries clearly and connect it to the module focus: SOAP notes, discharge summaries, referral letters, visit summaries, medical language simplification.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

medical language simplification

What it means: define medical language simplification clearly and connect it to the module focus: SOAP notes, discharge summaries, referral letters, visit summaries, medical language simplification.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Turn fictional consultation notes into a structured summary.
Learners produce: Documentation workflow.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 4. Patient communication

Module focus: Plain-language explanations, multilingual instructions, adherence support, appointment prep. Primary live activity or lab: Create patient instructions for a fictional condition and review for safety. Expected take-home output: Patient education sheet.

Topics and coverage

Plain-language explanations

What it means: define Plain-language explanations clearly and connect it to the module focus: Plain-language explanations, multilingual instructions, adherence support, appointment prep.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

multilingual instructions

What it means: define multilingual instructions clearly and connect it to the module focus: Plain-language explanations, multilingual instructions, adherence support, appointment prep.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

adherence support

What it means: show where adherence support appears in the learner's real workflow and which parts are judgment-heavy versus draftable.
What to cover: current workflow, pain points, AI-assisted steps, human review checkpoints, quality standard, and ownership of the final decision.
Demonstration: convert one messy real-world input into a structured brief, draft, analysis, checklist, or next action.
Evidence of learning: learners produce a reusable template or playbook entry that can be used after the course.

appointment prep

What it means: define appointment prep clearly and connect it to the module focus: Plain-language explanations, multilingual instructions, adherence support, appointment prep.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Create patient instructions for a fictional condition and review for safety.
Learners produce: Patient education sheet.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 5. Medical literature and evidence support

Module focus: PICO questions, guideline search, summarizing evidence, citation verification, evidence hierarchy. Primary live activity or lab: Build a literature summary template. Expected take-home output: Evidence memo.

Topics and coverage

PICO questions

What it means: define PICO questions clearly and connect it to the module focus: PICO questions, guideline search, summarizing evidence, citation verification, evidence hierarchy.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

guideline search

What it means: define guideline search clearly and connect it to the module focus: PICO questions, guideline search, summarizing evidence, citation verification, evidence hierarchy.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

summarizing evidence

What it means: define summarizing evidence clearly and connect it to the module focus: PICO questions, guideline search, summarizing evidence, citation verification, evidence hierarchy.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

citation verification

What it means: define citation verification clearly and connect it to the module focus: PICO questions, guideline search, summarizing evidence, citation verification, evidence hierarchy.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

evidence hierarchy

What it means: define evidence hierarchy clearly and connect it to the module focus: PICO questions, guideline search, summarizing evidence, citation verification, evidence hierarchy.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Build a literature summary template.
Learners produce: Evidence memo.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 6. AI-assisted triage and screening workflows

Module focus: Intake forms, red flags, escalation, human review, false positives/negatives, audit. Primary live activity or lab: Design a triage workflow with mandatory clinician oversight. Expected take-home output: Workflow diagram.

Topics and coverage

Intake forms

What it means: define Intake forms clearly and connect it to the module focus: Intake forms, red flags, escalation, human review, false positives/negatives, audit.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

red flags

What it means: define red flags clearly and connect it to the module focus: Intake forms, red flags, escalation, human review, false positives/negatives, audit.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

escalation

What it means: define escalation clearly and connect it to the module focus: Intake forms, red flags, escalation, human review, false positives/negatives, audit.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

human review

What it means: define human review clearly and connect it to the module focus: Intake forms, red flags, escalation, human review, false positives/negatives, audit.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

false positives/negatives

What it means: define false positives/negatives clearly and connect it to the module focus: Intake forms, red flags, escalation, human review, false positives/negatives, audit.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

audit

What it means: define audit clearly and connect it to the module focus: Intake forms, red flags, escalation, human review, false positives/negatives, audit.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Design a triage workflow with mandatory clinician oversight.
Learners produce: Workflow diagram.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 7. Bias, safety, and clinical governance

Module focus: Automation bias, demographic bias, explainability, monitoring, incident reporting, procurement. Primary live activity or lab: Analyze a healthcare AI failure scenario. Expected take-home output: Governance checklist.

Topics and coverage

Automation bias

What it means in this course: define Automation bias in operational terms, not as an abstract principle.
What to cover: sensitive data boundaries, affected stakeholders, approval paths, documentation, and what doctors, nurses, allied health professionals, hospital administrators, healthcare founders must never delegate blindly to AI.
Use case: present one acceptable use, one borderline use, and one prohibited use, then ask learners to justify the classification.
Evidence of learning: learners add a risk control, review step, or escalation rule to their course project.

demographic bias

What it means in this course: define demographic bias in operational terms, not as an abstract principle.
What to cover: sensitive data boundaries, affected stakeholders, approval paths, documentation, and what doctors, nurses, allied health professionals, hospital administrators, healthcare founders must never delegate blindly to AI.
Use case: present one acceptable use, one borderline use, and one prohibited use, then ask learners to justify the classification.
Evidence of learning: learners add a risk control, review step, or escalation rule to their course project.

explainability

What it means: define explainability clearly and connect it to the module focus: Automation bias, demographic bias, explainability, monitoring, incident reporting, procurement.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

monitoring

What it means: define monitoring clearly and connect it to the module focus: Automation bias, demographic bias, explainability, monitoring, incident reporting, procurement.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

incident reporting

What it means: define incident reporting clearly and connect it to the module focus: Automation bias, demographic bias, explainability, monitoring, incident reporting, procurement.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

procurement

What it means: define procurement clearly and connect it to the module focus: Automation bias, demographic bias, explainability, monitoring, incident reporting, procurement.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Analyze a healthcare AI failure scenario.
Learners produce: Governance checklist.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 8. Implementation studio

Module focus: Use-case selection, pilot metrics, SOPs, patient consent language, training, quality review. Primary live activity or lab: Build a pilot plan for one low-risk workflow. Expected take-home output: Healthcare AI pilot plan.

Topics and coverage

Use-case selection

What it means: define Use-case selection clearly and connect it to the module focus: Use-case selection, pilot metrics, SOPs, patient consent language, training, quality review.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

pilot metrics

What it means: define pilot metrics clearly and connect it to the module focus: Use-case selection, pilot metrics, SOPs, patient consent language, training, quality review.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

SOPs

What it means: define SOPs clearly and connect it to the module focus: Use-case selection, pilot metrics, SOPs, patient consent language, training, quality review.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

What it means: define patient consent language clearly and connect it to the module focus: Use-case selection, pilot metrics, SOPs, patient consent language, training, quality review.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

training

What it means: place training inside the AI system stack so learners know what problem it solves and what tradeoffs it introduces.
What to cover: inputs, outputs, system boundaries, evaluation criteria, cost or latency implications, and common failure cases.
Demonstration: use a diagram, small code sample, worksheet, or tool trace to make the mechanism visible.
Evidence of learning: learners compare two approaches and explain which one they would choose for a realistic constraint.

quality review

What it means: define quality review clearly and connect it to the module focus: Use-case selection, pilot metrics, SOPs, patient consent language, training, quality review.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Build a pilot plan for one low-risk workflow.
Learners produce: Healthcare AI pilot plan.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Labs, projects, and assessments

Lab 1: Convert a fictional medical note into patient-friendly instructions and clinician summary.
Lab 2: Build a PICO-based literature search and evidence summary workflow.
Lab 3: Design a human-reviewed intake or screening workflow.
Capstone: AI implementation checklist and pilot plan for a low-risk healthcare workflow.

Evaluation approach

20% use-case risk classification.
25% documentation and patient communication exercise.
20% evidence summary workflow.
35% implementation/pilot plan.

Recommended tools and materials

Approved AI assistant, medical literature databases, hospital policy templates, secure note/document tools.
Use only fictional, synthetic, or properly de-identified patient scenarios in training.

Safety, ethics, and governance emphasis

AI should not be positioned as an autonomous diagnostic or treatment decision-maker.
Clinician review is mandatory for patient-facing or clinical content.
Patient data must be protected according to applicable law, institutional policy, and professional ethics.

Delivery notes

Separate clinician, administrator, and health-tech founder tracks where possible.
Emphasize low-risk high-value workflows first: documentation, education, operations, and research support.

Instructor Build Checklist

Prepare one short demo for each module and one learner activity that creates a saved artifact.
Prepare examples that match the audience, local context, and likely tools learners can access.
Add a verification step to every AI-generated output: factual check, source check, data sensitivity check, and quality review.
Keep a running portfolio folder so each module contributes to the final project or learner playbook.
Reserve time for reflection on what the learner did, what AI did, what was checked, and what remains uncertain.