SAMPLE REPORT — Illustrative company and figures. No live research was performed, actual agent executions: 0, MAX Not run, Guardians not run (built · integration pending). Human decisions remain required.
Sample Report 7 · Healthcare Operations
Medical Practice — Reduce Patient Wait Times
Maple Grove Family Practice, 4 providers (sample) · Sample results only
Client assignment
Patients wait too long. The practice manager wants an operational flow analysis, bottlenecks and a no-show plan. Operational analysis only; no clinical advice.
How the client assigns it to TED: The practice manager gives TED de-identified sample timestamps and scheduling counts and asks where the delays are. TED confirms no patient-identifying data is used and clinical decisions stay with clinicians.
Executive summary
Arrival-to-room averages 33 minutes, of which 22 is lobby waiting. Arrivals cluster between 8 and 9 a.m. (18 patients) against front-desk capacity of 20 per hour, leaving no slack. Staggered scheduling and pre-visit check-in could cut lobby wait substantially; reminders could recover about 20 visits a week.
33 min
Arrival to room
6 check-in + 22 wait + 5 rooming
62 min
Total visit cycle
Illustrative average
12%
No-show rate
58 of 480 weekly appointments
Key findings
1. Lobby wait is the main delay
22 of 33 minutes before rooming is waiting, not service.
2. Morning arrival peak
18 arrivals in the first hour versus 20/hour desk capacity runs near 90% utilization, so queues grow fast.
3. No-shows waste capacity
58 empty slots a week; cutting the rate to 8% recovers about 20 visits.
Patient flow (illustrative averages)
Operational timestamps only.
StepCheck-in
- Minutes
- 6
StepLobby wait
- Minutes
- 22
StepRooming
- Minutes
- 5
StepWait for provider
- Minutes
- 14
StepProvider visit
- Minutes
- 15
StepTotal
- Minutes
- 62
No-show reduction
Weekly appointments 480.
RateCurrent 12%
- No-shows
- 58
- Change
- —
RateTarget 8%
- No-shows
- 38
- Change
- 20 visits recovered
Options & decision table
Flow options
Illustrative.
OptionStagger first-hour appointments
- Expected effect
- Smooths peak
- Consideration
- Template change
OptionOnline pre-visit check-in
- Expected effect
- Cuts check-in time
- Consideration
- Privacy-compliant tool required
OptionTwo-step reminders
- Expected effect
- Fewer no-shows
- Consideration
- Consent for messages
Prioritized action plan
- 1Re-template schedules to spread 8–9 a.m. arrivalsPractice manager · 30 days
- 2Start reminder calls/texts 3 days and 1 day aheadFront desk lead · 30 days
- 3Evaluate a privacy-compliant pre-visit check-in toolPractice manager + compliance · 60 days
Success metrics
- Arrival-to-room under 20 minutes
- No-show rate at or below 8%
- Patient experience survey wait score improves
SOCRATEE's Perspective
Sample text, not generated
Assumptions to test
- Timestamps are recorded consistently
- Morning peak is scheduling-driven
What deserves questioning
- Are provider delays upstream of the lobby wait?
- Will staggering reduce access for working patients?
Sources & evidence
Illustrative assumptions (not evidence)
- All times and counts are sample data
Verified data needed before deciding
- De-identified scheduling system timestamps
- Staffing rosters
- Patient survey results
Verified evidence attached: none. No citations are provided because no research was performed.
Safeguards
- Operational analysis only; not clinical advice
- De-identified data; privacy rules apply
- Clinical decisions remain with licensed clinicians
Report status · Research & Review Record
- Actual agent executions: 0
- MAX independent grade: Not run
- 18 Guardians: Built · Integration Pending; no review occurred
- Live research: None performed
- Final decision: Always a human decision
Specialist roles (role definitions · not executed)
- TED — Coordinator: would scope the assignment, route work and assemble the report.
- RES — Would gather operational best-practice references, cited.
- KNOLLY — Would reconcile timestamps and schedules.
- Operations Analyst (role definition) — Would map flow and queue capacity.
- SOCRATEE — Would challenge the bottleneck diagnosis.
- LIBBY — Would check privacy requirements for any data or messaging.
- MAX — Potential independent grader. Not run; no grade exists.
- 18 Guardians — Potential oversight layer. Built · Integration Pending; no review occurred.
How AI Team One Works — Step by Step (prewritten)
Stage 1
The assignment
The practice manager asks how to reduce patient waits, operationally.
Stage 2
TED coordinates
TED routes flow mapping, queue capacity and no-show analysis with privacy limits.
Stage 3
Specialist responsibilities
An Operations Analyst role maps flow, KNOLLY reconciles timestamps, LIBBY checks privacy.
Stage 4
Oversight design
Oversight here would center on staying operational: SOCRATEE would test whether provider delays sit upstream of the lobby wait, so the fix is not aimed at the wrong queue, LIBBY would keep the analysis free of clinical recommendations and the timestamps de-identified, MAX could grade the queue math, and the Guardians could review the privacy handling. In this sample none of that ran: MAX Not run, Guardians not run (built · integration pending).
Stage 5
The executive report
The result would look like "Medical Practice — Reduce Patient Wait Times": findings, options, a prioritized action plan and the data still needed. Prewritten sample content; actual agent executions: 0.