| Duration: 1h 32m
Make defensible AI decisions without becoming technical. Frameworks for healthcare operations and transformation leaders
What you'll learn
Use the AI vs. Non-AI Decision Filter to evaluate any AI proposal in under 15 minutes using a repeatable, defensible framework
Ask seven specific, evidence-based questions in any vendor meeting that surface performance gaps, population mismatches, and governance blind spots
Design a pre-pilot success definition — including primary metrics, equity metrics, failure criteria, and named decision authority — before any contract is signe
Apply the five AI implementation failure mode framework to identify and mitigate risks before an initiative reaches go-live
Complete a governance RACI for any AI initiative that assigns a single accountable owner to every governance activity
Apply the retrain, replace, or decommission decision framework to make evidence-based recommendations about underperforming AI systems
Produce three professional artifacts — a Personal AI Decision Checklist, an AI Initiative Evaluation Worksheet, and a 90-Day AI Leadership Action Outline
Requirements
No technical background required — no coding, data science, or IT experience needed
Experience working in or alongside a healthcare organization — hospital, health system, public health, or digital health — is assumed but not formally required
Helpful but not required: current or recent exposure to an AI initiative at work. You will learn more if you can apply the frameworks to a real scenario.
No prior AI knowledge assumed — the course builds from first principles using plain language throughout
Description
You are already in rooms where AI decisions are being made. The question is whether you are leading those decisions — or quietly nodding along hoping nobody asks a question you cannot answer.Healthcare leaders at every level are being asked to sponsor, approve, and govern AI initiatives without a structured way to evaluate them. They sit through vendor demos without knowing what to challenge. They approve pilots that fail in ways they did not see coming. And when something goes wrong — a model underperforms, an equity gap surfaces, a staff adoption failure embarrasses the organization — the accountability flows upward to the leader who approved it.This course was built to close that gap. Not by making you technical. By giving you the judgment, the frameworks, and the language that let you lead AI decisions the same way you lead every other high-stakes organizational decision: with structure, with evidence, and with accountability.WHAT THIS COURSE IS — AND WHAT IT IS NOTThis is not a general AI literacy course. It is not a survey of AI tools. It is not a coding course, a machine learning course, or a healthcare technology overview.This is a decision-making course for mid-career healthcare leaders who need to engage with AI initiatives at the level their organizations — and their career trajectories — now require.Specifically, it is structured around four questions that every AI initiative in healthcare needs a leader to answer:1. Is this the right problem for AI — or is something else being disguised as an AI opportunity?2. Is this the right vendor and solution — and are the performance claims actually defensible?3. Is the organization ready to implement this well — or will it fail in the ways that AI initiatives typically fail?4. Is this being governed responsibly over time — or will it quietly degrade until something goes wrong?WHAT MAKES THIS COURSE DIFFERENT1. Built around judgment, not knowledge. Most AI courses teach you what AI is. This course teaches you what to do about it — in specific organizational situations, under real pressure, with real stakes.2. Frameworks, not theory. Every module introduces a practical, named framework you can apply immediately. The Problem Diagnostic. The Three-Gate Readiness Filter. The 7 Defensible Questions. The Post-Deployment Governance Calendar. Each one is designed to be used in a meeting, not just remembered for an exam.3. Two running case tracks across all six modules. A scheduling AI initiative in a regional hospital (Track A) and a colorectal cancer screening prioritization initiative in a public health unit (Track B) run as live examples throughout the course — showing exactly how the same framework produces different outputs depending on clinical stakes and organizational context.4. Three capstone artifacts you leave with. Not just a certificate. Actual documents: a Personal AI Decision Checklist, an AI Initiative Evaluation Worksheet, and a 90-Day AI Leadership Action Outline. Each is designed to be brought into a real organizational conversation.5. Taught by a practitioner, not a theorist. The instructor has 15-plus years of North American digital health, analytics and health systems transformation experience, holds academic faculty appointments in health informatics (at University of Toronto), and advises health-tech startups. The frameworks come from watching AI initiatives succeed and fail in real health systems — not from literature reviews.WHAT YOU WILL BUILD — THE THREE CAPSTONE ARTIFACTS1. Personal AI Decision Checklist (built in Modules 2 and 4) — A two-section decision tool covering use case validation and implementation readiness. Use this any time an AI initiative lands on your desk, before resources are committed.2. AI Initiative Evaluation Worksheet (built in Modules 3 and 5) — A three-section document covering use case validation, vendor evaluation, and governance design. This is the professional decision record you bring to an executive sponsor or governance committee.3. 90-Day AI Leadership Action Outline (built in Module 6) — A structured personal commitment to three specific leadership actions grounded in your own organization: one initiative you will evaluate, one stakeholder conversation you will reframe, and one governance gap you will name and propose to address.COURSE STRUCTURE — SIX MODULESModule 1 — AI in Healthcare: What Leaders Actually Need to Know (~18 min)The grounded baseline every healthcare leader needs. What AI is in plain language. The practical spectrum from rule-based logic to machine learning to generative AI. Where AI currently operates in healthcare operations, clinical care, and population health. Four misconceptions that lead to bad sponsorship decisions — and the reframes that replace them. Introduction of the two running case tracks.Module 2 — Identifying the Right AI Use Cases (~20 min)The most valuable module for immediate application. The Problem Diagnostic (five problem types — and why AI is only right for one of them). The Three-Gate AI Readiness Filter (data, outcome, workflow). The AI vs. Non-AI Decision Filter flowchart. A Red Flag Pre-Approval Checklist. Evidence-strength assessment for common healthcare AI use cases. First section of Capstone Artifact 1.Module 3 — Evaluating AI Vendors and Solutions (~22 min)The highest technical depth module — and the one most leaders wish they had before a vendor meeting. The Demo Skepticism Checklist. Just enough statistics to be dangerous: sensitivity, specificity, the base rate problem, and the four-level validation hierarchy. Training data bias and how to detect it. The 7 Defensible Questions Framework. The Reference Check Protocol. The Procurement Red Flags Checklist. The Vendor Evaluation Scorecard.Module 4 — Implementation Reality and Change Management (~18 min)Where most AI initiatives fail — and where your leadership judgment has the most leverage. The Five AI Implementation Failure Modes and how to prevent each. The Pilot-to-Production Reality Model. The Pre-Pilot Success Definition Checklist. The Stakeholder Engagement Architecture. The alert fatigue problem. Five conditions for frontline AI trust. The Implementation Readiness Scorecard. Capstone Artifact 1 completed.Module 5 — AI Governance, Monitoring, and Sustainability (~18 min)What responsible AI governance looks like after go-live — the phase most organizations treat as optional. Model drift: what it is, why it is inevitable, and how to govern it before it causes harm. The Post-Deployment Governance Calendar. The AI Governance RACI. The Four Performance Dimensions. Override rate as a governance signal. The Retrain, Replace, or Decommission Decision Framework. Regulatory orientation. Capstone Artifact 2 completed.Module 6 — AI Leadership and Career Positioning (~12 min)Synthesizing the frameworks into a coherent leadership identity. The four AI leadership traps to avoid. What Director-level AI engagement looks like versus operational execution. Developing a personal AI leadership stance. Speaking about AI at the leadership table: three contexts and three language patterns. The full AI Leadership Decision Architecture. Capstone Artifact 3 introduced and completed.HONEST TIME COMMITMENTVideo content: approximately 100+ minutes (under 2 hours) of lecture across six modules.Capstone workbook and artifacts: 3 to 5 hours, depending on how deeply you engage with your chosen organizational initiative.Total realistic time investment: 5 to 7 hours for the complete experience, including application.This is a course designed to be completed in a focused weekend or spread across a two-week period. The video content is intentionally concise. The work is in the application.WHY NOT JUST USE FREE CONTENT?There is no shortage of articles, webinars, and YouTube videos about AI in healthcare. What is missing is a coherent, end-to-end framework designed specifically for the governance and evaluation decisions that mid-career healthcare leaders actually face.This course is not a content curation. The frameworks were designed from the ground up for this specific leadership context. The case tracks run end-to-end across all six modules so you see how the same framework produces different outputs in different clinical and organizational contexts. The capstone artifacts are designed to be professionally usable — not course appendices.DOWNLOADABLE RESOURCES INCLUDED• Capstone Project Workbook — the fillable end-to-end evaluation document applied to a real initiative you choose• Filled capstone example — a worked example using an AI medical scribe implementation in a pediatric hospital• Personal AI Decision Checklist (blank template)• AI Initiative Evaluation Worksheet (blank template)• 90-Day AI Leadership Action Outline (blank template)• AI vs. Non-AI Decision Filter — one-page flowchart for immediate use• Red Flag Pre-Approval Checklist — one-page quick reference• The 7 Defensible Questions — formatted reference card• Vendor Evaluation Scorecard — weighted scoring tool• Post-Deployment Governance Calendar — template• Six-term AI Vocabulary Card (Module 1)• Full AI Leadership Decision Architecture — reference posterTHE BOTTOM LINEHealthcare AI is not a technical problem with a technical solution. It is a governance problem with a leadership solution. The organizations that deploy AI well — and avoid the failures that are now well documented in the literature — do so because they have leaders who bring judgment to the decision, not just enthusiasm.This course gives you that judgment. In six focused modules, three usable artifacts, and one end-to-end capstone workbook, you will build a decision architecture for AI that is grounded in evidence, informed by real implementation failures, and immediately applicable to your organization.Enroll if you are ready to lead AI decisions — not just attend the meetings where they happen.
Operations Managers and Senior Operations Managers in hospitals, health systems, or public health organizations,Mid-career healthcare leaders (8–15 years experience) preparing for Director-level roles,Digital Health Product Managers working inside health systems or alongside clinical operations,Healthcare Executives (VP, Director, CMO) who approved AI initiatives without a structured framework,Program Managers and Transformation Leads responsible for digital health or AI initiatives.,Clinical Informatics Professionals who understand the clinical context of AI but want a structured operational and governance framework,Not Right For Software developers, data scientists, or AI engineers. Also not ideal for Beginners with no healthcare context or Students looking for a quick overview or general AI literacy course