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Clinical Practice + Research/Academic Medicine + Healthcare Leadership | 16 Hours
Master AI Prompting Systems That Are Ethical, Evidence-Based, and Patient-Safe
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About the Course
In an era where AI is transforming clinical decision support, medical research, documentation, and health system operations, physicians must evolve from AI observers to AI-literate clinical leaders. This 16-hour intensive masterclass equips Medical Doctors (MDs/DOs) with advanced prompt engineering frameworks and AI governance competencies needed to harness Large Language Models (LLMs) responsibly, efficiently, and defensibly across all domains of medical practice.
Every session combines universal prompt engineering principles with three parallel practice tracks:
The Transformation
What You Will Achieve
Use the MED-RTF+ Framework to generate evidence-based, patient-safe outputs
Chain-of-Verification and Evidence Triangulation to mitigate error and bias
Chain prompts for clinical reasoning, literature synthesis, and quality improvement
Comply with HIPAA, AMA ethics, IRB requirements, and institutional AI policies
Assess model reliability, bias, explainability, and integration safety
Meta-prompting and version control strategies to scale AI adoption
Disclosure templates, prompt logs, and clinical documentation protocols
Articulate AI strategies to colleagues, patients, administrators with authority
4-Week Curriculum
4 hours per week. Click each week to expand and see the detailed hourly breakdown with clinical, research, and leadership tracks.
AI Fundamentals, Prompt Architecture, Literature Synthesis, Validation
Diagnose which medical tasks are suitable for AI augmentation. Understand LLM capabilities and limitations in clinical, research, and leadership contexts.
| Clinical | Research | Leadership |
|---|---|---|
| Map AI use cases to patient care: differential diagnosis, patient education, care transitions | Map AI use cases to scholarly workflow: literature synthesis, grant aims, manuscripts | Map AI use cases to system workflow: quality metrics, policy drafting, operational bottlenecks |
Structure any medical request using the MED-RTF+ Framework (Medical Role, Task, Format + Constraints, Citations, Tone, Patient-Safety Guardrails).
| Clinical | Research | Leadership |
|---|---|---|
| Draft a differential diagnosis prompt for chest pain with ACC/AHA citations and patient-friendly explanation | Draft a PICO-formatted research question with systematic review methodology and IRB compliance | Draft a quality improvement proposal with CMS metrics, stakeholder analysis, and timeline |
Retrieve and synthesize authoritative medical evidence using RAG patterns while preventing citation hallucination.
| Clinical | Research | Leadership |
|---|---|---|
| Synthesize current hypertension guidelines for elderly patients with comorbidities | Synthesize meta-analyses on immunotherapy outcomes; flag methodological limitations | Synthesize QI literature on reducing hospital readmissions; map to operational levers |
4-step validation workflow: Self-Consistency → Evidence Cross-Check → Expert Corroboration → Documentation for Defensibility.
| Clinical | Research | Leadership |
|---|---|---|
| Validate AI-generated differential against guidelines and patient factors; document reasoning | Validate manuscript methods against CONSORT/STROBE guidelines; flag areas needing work | Validate quality metric dashboard against CMS specs and organizational priorities |
Prompt Chaining, Pattern Recognition, Communication, Compliance
Assemble complex medical documents by chaining modular prompts with version control and review trails.
| Clinical | Research | Leadership |
|---|---|---|
| Chain: Assessment → Differential → Plan → Patient Instructions → Follow-up | Chain: Research Question → Methods → Results → Discussion → Limitations | Chain: Problem Statement → Analysis → Intervention → Implementation → Evaluation |
Auto-flag unusual clinical patterns, safety concerns, or quality gaps using constraint-based prompting.
| Clinical | Research | Leadership |
|---|---|---|
| Analyze diabetes data; flag unusual lab trends, medication adherence; output risk recommendations | Analyze trial enrollment; flag recruitment bottlenecks, demographic imbalances | Analyze hospital quality metrics; flag units with outlier infection rates or readmissions |
Translate complex medical information into compelling, audience-appropriate communications.
| Clinical | Research | Leadership |
|---|---|---|
| Translate cardiac diagnosis into patient-friendly explanation with shared decision-making prompts | Translate research findings into lay summary for grant public impact section | Translate QI data into physician leadership briefing with change management strategy |
Validate decisions against multi-framework requirements using constraint-based prompting.
| Clinical | Research | Leadership |
|---|---|---|
| Validate heart failure treatment against ACC/AHA + CMS + formulary constraints | Validate trial protocol against FDA + ICH-GCP + IRB requirements | Validate telehealth policy against state licensure + CMS + HIPAA security |
Narrative Design, Automation, Ethics, AI Tool Evaluation
Synthesize complex evidence into compelling, risk-focused narratives for diverse audiences.
| Clinical | Research | Leadership |
|---|---|---|
| Synthesize complex case into handoff communication: problems + active issues + contingency plans | Synthesize research status into grant progress report with preliminary data and challenges | Synthesize performance data into board briefing with quality metrics and resource requests |
Automate multi-phase medical workflows with state management, quality gates, and review protocols.
| Clinical | Research | Leadership |
|---|---|---|
| Automate chronic disease workflow: intake → plan → education → follow-up with safety checks | Automate manuscript workflow: outline → methods → results with peer review triggers | Automate QI workflow: metrics → root cause → intervention with stakeholder checkpoints |
Embed ethical guardrails, privacy protections, and professional standards into AI workflows.
| Clinical | Research | Leadership |
|---|---|---|
| Auto-redact PHI + generate patient-friendly AI disclosure + document clinical reasoning defensibly | Enforce IRB compliance + generate research integrity docs + maintain version-controlled trails | Auto-detect sensitive data + enforce AI governance + generate leadership communication templates |
Apply specialized frameworks to evaluate reliability, bias, explainability, and integration safety.
| Clinical | Research | Leadership |
|---|---|---|
| Evaluate AI sepsis detection tool: sensitivity/specificity evidence, bias across populations | Evaluate AI literature screening: accuracy vs. human reviewers, reproducibility, time savings | Evaluate AI risk stratification: predictive validity, equity considerations, resource implications |
Prompt Libraries, Quality Assurance, Capstone I & II
Build a self-improving, reusable medical prompt library with version control and performance tracking.
| Clinical | Research | Leadership |
|---|---|---|
| Build prompt library for common clinical scenarios with guideline alignment tags and version history | Build prompt library for scholarly tasks with journal guideline tags and collaboration protocols | Build prompt library for system initiatives with regulatory alignment tags and templates |
Implement structured evaluation protocols to continuously improve AI-assisted medical procedures.
| Clinical | Research | Leadership |
|---|---|---|
| A/B test prompt versions for clinical notes; measure completeness, guideline alignment, time savings | A/B test AI-assisted vs. traditional literature review; measure comprehensiveness and efficiency | A/B test prompt versions for quality metric analysis; measure actionable insights and clarity |
Apply all frameworks to design an end-to-end AI-augmented workflow for a complex medical scenario.
| Clinical | Research | Leadership |
|---|---|---|
| Complex patient case with multiple comorbidities: assessment → differential → plan → follow-up | Research project from concept to dissemination: protocol → analysis → manuscript → strategy | System QI initiative: problem → analysis → intervention → plan → evaluation |
Mock peer review of AI-augmented work, identify improvements, and document optimization decisions.
| Clinical | Research | Leadership |
|---|---|---|
| Mock peer chart review of AI-augmented clinical documentation; output improvement plan | Mock journal peer review of AI-assisted manuscript; output revision plan | Mock executive committee review of AI-augmented QI initiative; output adoption roadmap |
Your Toolkit
Practice-specific exercises, reflection prompts, framework cheat sheets, and medical standards cross-references
80+ vetted templates tagged by specialty, task, evidence level, and patient population
Clinical skepticism checklist, evidence triangulation protocol, peer review rubric, A/B testing framework
AI usage policy draft, prompt logging template, PHI safeguarding checklist, disclosure examples
Protocols for assessing clinical validity, bias, explainability, and integration safety
Eligible for 16 hours AMA PRA Category 1 Credit (ACGME-compliant documentation provided)
Got Questions?
Your Instructor
Doctor of Engineering, Machine Learning/AI (GWU)
Dr. Beza holds a Doctor of Engineering in Cybersecurity Analytics and Machine Learning/AI from The George Washington University. He developed novel machine learning algorithms to detect DDoS attacks on the U.S. smart grid and has built enterprise-grade analytics solutions for leading organizations. With deep expertise in AI systems, cybersecurity, and data analytics, Dr. Beza brings a unique technical perspective to the intersection of artificial intelligence and healthcare.
His work spans from securing critical infrastructure to designing AI governance frameworks that ensure ethical, evidence-based, and defensible use of emerging technologies in regulated industries. This course distills years of hands-on experience into practical, physician-ready frameworks.
20+ Years in Tech, CISSP, CISM, CDPSE, MEng Cybersecurity (GWU). Worked across multiple continents, consulted Big Tech and Federal Government.
Limited Time Offer
Join physicians who are leading the AI transformation in medicine. Early bird pricing ends April 30.