A health system, anywhere
Tuesday, 6:42 AM
A children's hospital. A community system. An academic medical center. Pick yours — the morning looks the same.
The people who run it are about to spend hours on work their AI tools could already help with. Most of them have never been shown how.
This is one Tuesday, for four of them.
The people in this story are illustrative composites. The work — and the friction — is real in every health system we've trained.
Today
wks
Six weeks later
Each Tuesday below is told twice: as it is today, and as it runs six weeks after each person built a capstone — a working AI workflow of their own design, presented at the end of their cohort.
“You don't leave with a certificate of attendance. You leave with a real AI workflow for your role.”
Renee · Clinical Nurse
Her Tuesday runs 7:00 AM to — officially — 5:30 PM.
Fifteen tabs deep
Renee's first patient is complex, and the family traveled hours to make the 8:00 appointment. So she's in the chart at 7:10 — flowsheets, consults, med lists, two sets of labs — building the picture one click at a time.
Open in the chart
She'll do this five more times before lunch.
The picture, assembled
Her capstone ran overnight: the draft summary is waiting, flags on top. Renee verifies it against the chart — that's the job now, verification, not excavation — and is in the hallway early when the family arrives.
The chart — 15 tabs
Encounter notes (47)
Specialist consults — pulm, cardio, neuro
Med admin record · 3 active, 2 PRN
Labs 06/09 · Labs 05/28
Prior admission summary (11 pp.)
Pre-visit summary — drafted for Renee's review
Why they're here: 6-mo follow-up, post-op course stable
What changed since last visit: new PRN added 5/28
⚑ Flag: weight percentile drop — confirm with family
⚑ Flag: pending lab from outside clinic — chase result
Meds reconciled ✓ · Allergies confirmed ✓
Same chart. Same kid. Different morning.
weeks
The shift that ends at 5:30 ends at 7
Renee's shift ended at 5:30. She's still here, typing handoff summaries from memory and a day's worth of notes — the most important writing she does, done entirely after her job is over.
Handoff summaries · shift ended 5:30
Dinner waits again.
She goes home at 5:30
Her capstone's second act: handoff drafts assembled from the day's notes, morning flags carried forward automatically. She reviews, fixes the nuance only she knows, signs — and is out the door at 5:31.
A day of notes — 6 patients
Vitals q4h × 6 patients
Med admin events (31)
Family conversations — 3 significant
New orders after 3 PM (4)
Pending: outside lab, imaging read
Handoff drafts — for Renee's review & signature
Rm 4012: post-op day 2, pain managed, mom staying overnight
⚑ Rm 4015: new PRN after 3 PM — watch response
Rm 4018: discharge likely AM, education started
⚑ Pending lab from outside clinic — night shift to chase
One workflow, both ends of the shift. Capstones compound.
weeks
Renee's ledger — her Tuesday's documentation load
The capstone — a workflow Renee designed, week 6
The Pre-Visit & Handoff Workflow
Modern EHRs increasingly ship with AI that can draft chart summaries and end-of-shift notes — that part is product, not magic. Renee's capstone is the workflow she designed around it: when drafts run, what gets verified against the chart before anything touches care, and how morning flags carry automatically into the evening handoff.
Built on your EHR's own AI drafting features, deployed through clinical informatics — the governed path your clinical tools already follow.
Marcus · Patient Access
His queue was longer when he arrived than when he left last night. It always is.
The queue grew overnight
Each prior auth is the same ritual: pull the order, find the payer's requirements, hunt the documentation, assemble, submit, log. Twenty minutes when it's clean. Most aren't — and the on-hold queue of families blinks on his other monitor.
authorizations waiting
+6 overnight
+ 18 more below the fold
Every packet he's assembling is a family waiting to schedule care.
The packet, pre-assembled
His capstone drafts the ritual's first half — requirements matched to documentation, gaps in red. Marcus reviews, fixes, submits. Seven minutes when it's clean, and the recovered time goes where it should have been all along: on the phone with a family, walking through what their coverage means.
Prior-authorization packet — drafted for review
The queue still grows. He's faster than it now.
weeks
Marcus's ledger — his morning auth block
The capstone — a workflow Marcus designed, week 6
The Prior-Auth Packet Assembler
A workflow Marcus designed in the enterprise AI assistant his team already licenses: it drafts each authorization packet from the payer's published requirements — documents matched, gaps flagged red, nothing submitted without his review.
Runs in the enterprise AI assistant most health systems already deploy (e.g., a BAA-covered Microsoft 365 Copilot tenant).
Priya · HR Generalist
Three open reqs, and every clinical posting competes with systems three states away.
Three open reqs and a buried answer
The posting drafts are stale copies of old postings. Before she can touch them, a manager calls with a float-pool policy question. She knows the answer is in the handbook — somewhere. The reqs age another day.
Open requisitions
The candidates she's losing won't wait for the handbook search.
Drafts that start at version three
Her capstone drafts the postings from approved templates — three reqs out before lunch. The float-pool question takes four minutes: her handbook workflow answers in plain language with the policy citation, she reads the cited section, confirms, calls back.
“Can a part-time RN in the float pool pick up weekend ICU shifts?”
Yes, if they hold current ICU competency validation and the shift keeps them under the part-time hours cap for the pay period.
The handbook didn't get shorter. The path through it did.
weeks
Priya's ledger — postings + policy lookups
The capstone — a workflow Priya designed, week 6
The Posting & Policy Desk
Two workflows Priya designed and presented as one capstone: postings drafted from the approved template library so her first edit is judgment, not transcription — and a handbook answerer she built to refuse any answer without a section citation.
Runs in the enterprise AI assistant your teams already license, against your own approved templates and handbook.
Dana · Clinic Manager
Two call-outs by 1:00. The afternoon clinic is a puzzle with a deadline.
Schedule Tetris
She works the options in her head, on a sticky note, in the scheduling screen: who's cross-trained, who's at their cap, whether the 2:15 block survives. It takes most of an hour. The option she picks is fine — she'll never know if it was the best one.
This afternoon · 2 uncovered
2:15 block — family rescheduled twice already
Every unfilled slot this afternoon is a family rescheduling a trip.
Three options, eyes open
Her capstone lays out three coverage scenarios side by side — overtime risk, throughput, who gets stretched. Dana still decides; she's the one who knows Kim is burned out and the 2:15 family has been rescheduled twice. Twelve minutes, door to door, and the afternoon clinic runs full.
1:15 PM — two call-outs
Afternoon clinic
2 gaps · 2:15 block at risk
Option analysis
in Dana's head + a sticky note
Time to decision
~50 minutes
1:27 PM — decision made
Afternoon clinic
fully covered · 2:15 block runs
Option analysis
3 scenarios, costs shown side-by-side
Time to decision
12 minutes — decision Dana's
The judgment was always hers. Now it has options to judge.
weeks
Dana's ledger — the coverage decision
The capstone — a workflow Dana designed, week 6
The Coverage Scenario Builder
A workflow Dana designed in her enterprise AI assistant: she gives it the constraints — who's out, who's cross-trained, hours caps, the afternoon template — and it lays out three coverage scenarios with what each costs. She decides, and enters the schedule in the system of record herself.
Runs in the enterprise AI assistant your teams license. The schedule of record stays in your scheduling system, entered by Dana.
Now multiply one Tuesday.
Across four people, 245 minutes of Tuesday friction became 55.
If proficiency returns even 20 minutes a week to each team member, a 3,000-person system recovers roughly 48,000 hours a year.
Those hours don't disappear — they go back into care, into families, into the work that mattered before the friction.
Four people. Four capstones. One Tuesday transformed.
Renee · Clinical Nurse
The Pre-Visit & Handoff Workflow
verified against the chart, deployed with informatics
Marcus · Patient Access
The Prior-Auth Packet Assembler
human-reviewed before every submission
Priya · HR Generalist
The Posting & Policy Desk
refuses to answer without a citation
Dana · Clinic Manager
The Coverage Scenario Builder
the decision stays human
A cohort graduates up to 40 capstones.
Run the program across your workforce and the library compounds.
That's a growing library of working AI workflows your system would own — built by your people, for your work, reviewed by your standards.
Most health systems already have the ideas: an innovation team, a pipeline of staff suggestions, pockets of brilliant local fixes. What's missing is the last mile — the skills to let every team build its own answer.
Innovation centers concentrate capability in one room. Proficiency training distributes it to every department.
The certificate hangs on a wall. The capstone shows up for work on Tuesday.
Every one of those capstones started in the same place: the certification.
This is what the certification produces.
Every workflow in this story is the kind of thing a graduate builds — not because they learned to code, but because they learned when to trust AI, when to question it, and how to wrap human judgment around it.
The curriculum is live and public. Each framework below opens onto the actual lesson your teams would use.
Every role gets its own workshop.
The four role-based workshops below are tailored to real work — built with your teams, for your scenarios. Tell us which roles to start with.
The plan, in one scroll
Three layers: baseline proficiency for everyone, role-based application per team, and executive training for leaders. The curriculum below is live — every module links to the actual lesson your teams would use.
All team members
Baseline AI Proficiency
6 live, 1-hour sessions in role-based cohorts — or internal trainers, or interactive online modules
All team members, tailored by role
Persona-Based Application
1 live, 2-hour workshop per team — the four role workshops in this story are working examples
Senior leadership
Executive Training
Tailored session & coaching for your leaders
The six modules — live, not described
Every framework below is a door. They open onto the actual public lessons your cohorts would use.
A representative rollout — tailored to your calendar
Scheduling adapts to team availability and business priorities while preserving the cadence. Most systems run baseline cohorts first, then advanced role workshops in waves.
What's at stake in each domain
Companions to the live curriculum, not replacements for it. Each interactive shows what goes wrong when one of the six domains doesn't take hold — try one and you'll feel the gap the lessons close.
The Deference Reflex
What missing Judgment looks like: trusting the AI answer over your own expertise.
~90 secondsThe Fluency Trap
What missing Evaluation looks like: confident, polished, and wrong.
~90 secondsBrain Fry
What missing Orchestration looks like: AI everywhere, workflow nowhere.
~90 secondsThe Mirror Trap
What missing critical Interaction looks like: an AI that agrees with everything you say.
~90 secondsStart the conversation
Bring this to your teams.
Tell us about your system and the roles you'd start with. We respond within two business days to schedule a free discovery call — no obligation, no slides you've seen before.
Your mission is to care for patients and families.
Ours is to give the people who do it their Tuesdays back.
Request a consultation