Module 6 Handout — Implementation, Sustained Practice & Peer Mentorship

How this module works. Take the short Preview Quiz first — five minutes, ungraded. Then work through this handout (or the companion slide deck) at your own pace. Sections B and E aren't just reading — they're worksheets you fill in on paper, and you'll bring both, completed, to the live workshops and the closing session. Finish with the Post-Test.

By the end of this module you will be able to:

Draft a personal 90-day plan to integrate one or two AI tools into your own practice, with a measurable outcome — time saved, or patient-facing time restored.
Participate in or lead a peer case-audit comparing AI-assisted and unassisted outcomes.
Identify or help build a local clinical-AI-champion or community-of-practice structure to sustain use after formal training ends.

Section A — Why Most AI Adoption Efforts Quietly Fail (KNOW)

Five modules have built real skills — a documentation habit, a way of reading AI-assisted differentials, a routine for interrogating outputs, a checklist for adopting new tools. None of that guarantees anything three months from now. The literature this course draws from is consistent on exactly one point: one-off training does not stick on its own, no matter how good it is.

The failure isn't usually dramatic. It's quiet. Nobody decides to stop using what they learned — the old habits just quietly reassert themselves the first busy week, and nothing was in place to notice or resist that.

Common failure pattern	Why it happens
No follow-up after training ends	Skills fade back toward old habits within weeks without something that keeps the new behavior visible and expected.
No way to measure whether anything changed	Without a number to check, "did it work" becomes a feeling rather than a fact — and feelings drift toward whatever's easiest to believe.
Trying to adopt everything at once	Attention spread across five new tools sustains none of them. One habit, genuinely embedded, beats five habits abandoned within a month.
Relying on individual willpower alone	Without peers or institutional support, the first genuinely difficult week is often enough to quietly end the change.

The pattern this module exists to break Training ends, enthusiasm is real, and three months later nothing has actually changed in daily practice — not because the training failed, but because nothing was built to make the change durable. Sections B through E are the mechanisms this module hands you to prevent exactly that.

Section B — Building Your 90-Day Implementation Plan (THINK)

This is arguably the single most important exercise in the entire course — worth doing on paper, not just in your head. Be specific throughout; a vague plan fails the same quiet way Section A just described.

1. Which one or two tools from this course will you actually use in the next 90 days? (Naming more than two is usually a sign of trying to do too much at once — a documentation tool from Module 2, a diagnostic-support habit from Module 3, or the adoption checklist from Module 5 are all reasonable, specific choices.)

2. What's the measurable outcome? Something you can actually track — minutes saved per note, number of patients seen restored per session, or another real number — not "I'll use it more."

3. Days 0–30: what does "started" look like? What's the smallest concrete first step, and when exactly does it happen?

4. Days 31–60: what does "becoming routine" look like? What would tell you it's slipping, and how will you notice if that's happening?

5. Days 61–90: how will you actually check whether the measurable outcome from Question 2 moved — and who will you tell, either way?

6. What's most likely to derail this, realistically — and what's your specific plan for that specific obstacle?

Bring this plan, filled in, to the live workshop — you'll refine it with a colleague's outside perspective, which routinely catches gaps a plan made alone tends to miss.

Section C — Setting Up a Peer Case-Audit Process (KNOW)

A peer case-audit is a small, recurring, non-punitive practice: a few colleagues periodically compare a case handled with AI assistance against one handled without, looking at process and outcome — not at grading each other.

Audit element	What it looks like
Group size	Three to six colleagues — small enough to stay honest, large enough to actually see patterns across cases.
Frequency	Monthly is usually sustainable long-term; weekly commitments tend to quietly lapse within a few months.
Case selection	A mix of routine and difficult cases — reviewing only the interesting ones misses where AI assistance actually earns or loses its keep day to day.
What's compared	Not "was the AI right," but whether the process of using it changed what actually happened for the patient.
Tone	Process-focused, not evaluative — the same spirit as any other clinical case discussion, applied to a new category of tool.

Continuity from Module 4 This is the self-audit exercise from Module 4 Section B, run as a peer process instead of alone. The individual version taught you to catch hallucination and overconfidence in your own outputs; the group version catches the blind spots that an individual working solo, by definition, tends to miss.

Section D — Finding — or Becoming — a Clinical AI Champion (KNOW)

A clinical AI champion is the informal role of someone who keeps momentum going after formal training ends — not a grand title, usually just whoever is already doing this informally, made explicit and given a little more support.

What the role actually involves

Being the first call when a colleague's tool isn't working as expected. A quick, low-friction answer keeps a small problem from becoming a reason to quietly stop using the tool.
Keeping a running, informal sense of what's working and what isn't across the group. Not a formal survey — just noticing patterns across casual conversations.
Bringing that front-line experience to the people making adoption and procurement decisions. This is Module 5 Section D's advocacy work, turned into an ongoing role rather than a one-time act.
Not being the only technically capable person. Actively bringing others along rather than gatekeeping keeps the practice from depending on one person who might leave.

In the live seminar, you'll discuss who already plays something like this role where you work — formally or not — and what it would take to either support that person better or become that person yourself.

Section E — Closing Session: Your Commitment and Follow-Up Schedule (THINK)

Public, specific, scheduled commitments are followed through on more reliably than private, vague intentions — a general behavior-change principle, not something unique to this course. This section turns your Section B plan into exactly that kind of commitment.

Your commitment

I will — a specific action from your 90-day plan, not a general intention.

I will know it worked when — the measurable outcome from Section B, Question 2.

I will check in with — a named colleague or mentor, not "myself."

on — an actual date, not "sometime soon."

If it's not working by then, I will — a specific fallback, decided now rather than improvised later.

Why each part matters

Specificity beats vagueness. "I'll use the scribe more" quietly fails; "I'll use the scribe for every follow-up visit" is checkable.
A named person beats "myself." A commitment only you know about is the easiest one to quietly let slide.
A set date beats "eventually." "Eventually" never arrives; a calendar date does.
A fallback prevents quiet abandonment. Deciding what happens if it's not working, in advance, turns a potential silent failure into a planned course correction.

Bring the completed commitment to the closing session — you'll say it out loud to the group, and set the actual follow-up date on the spot.

Section F — Course Wrap-Up: What Carries Forward (KNOW)

Six modules, one throughline: not a verdict on whether AI is good or bad for medicine, but a working habit of scrutiny, applied consistently, to tools that are already part of daily practice whether or not they're formally taught.

None of this required believing AI is either a solution or a threat. It required a working habit of scrutiny, applied consistently — the same habit, in different clothes, in every module of this course. That habit is now yours to keep running, on your own schedule, long after this course ends.

Module	What it left you with
1 — Foundations	A working vocabulary and realistic expectations before any tool touched a patient.
2 — Documentation	A first tool in safe, competent daily use, and the habit of catching what it gets wrong.
3 — Decision Support	AI as a second opinion, never a verdict — and a calibrated sense of when a tool's training doesn't match your patients.
4 — Critical Appraisal	The Black Box Routine — one habit underneath everything else, and where accountability actually sits.
5 — Governance	A practical adoption checklist, and a clear-eyed view of the local regulatory gap and your role in it.
6 — Implementation	A plan, an audit process, and a champion role — mechanisms built to outlast the course itself.

Layer	What this module put there
KNOW	Why one-off training doesn't stick and what breaks that pattern; how to structure a sustainable peer case-audit process; what a clinical AI champion role actually involves; the throughline connecting all six modules.
THINK	A concrete personal 90-day implementation plan with a measurable outcome, and a specific, scheduled commitment made publicly rather than left as a private intention.

Implementation, Sustained Practice & Peer Mentorship