Fictional training simulation. D2V Mock is an invented training organization. All people, data, incidents, and documents are fictional. Any resemblance to a real entity is coincidental.
MISSION 07 · CAPACITY COORDINATION
COMPLETE GUIDED LAB
D2V Mock
Health
Design a privacy-aware clinic-capacity coordination workflow that improves access without overriding clinical restrictions, local control, or equitable allocation.
Guided lab available: review the public mission specification here, then enter the paid workspace for staged evidence, decisions, artifacts, progress tracking, and portfolio export.
01 / CORE QUESTION
Where should the patient be offered care—and who has authority?
The simulated care network operates multiple outpatient clinics with different specialties, staffing, equipment, hours, referral rules, and local scheduling practices. Central operations wants to reduce wait times by recommending alternate sites and appointment windows. Clinic managers worry that central recommendations will ignore clinical appropriateness, continuity, travel burden, local capacity, and privacy restrictions.
The mission is not solved by producing a fluent recommendation. The learner must define the decision boundary, reconstruct the evidence, build an inspectable workflow, and establish what would justify deployment.
DECISION TO IMPROVEWhich clinically eligible appointment options should be presented, in what order, and what information and approvals are required before a referral or appointment moves?
CORE METHOD → MISSION
One operating decision. Multiple deployment boundaries.
The Core Labs isolate individual failure modes. This mission forces several of them to coexist under one customer timeline, one evidence room, and one final deployment recommendation.
$79STANDARD INDIVIDUAL PRICE · REGULATED MISSION · LAUNCH ACCESS IS CURRENTLY FREE
Can a multi-site referral coordination system present and reserve feasible options without taking over clinical eligibility, patient choice, clinic acceptance, or privacy authority?
Clinical rules, clinic capacity, patient preferences, accessibility, travel burden, privacy limits, and local restrictions change on different timelines and belong to different owners.
Recommend a bounded rollout, narrow deployment, redesign, or stop with clinical ownership, privacy boundaries, capacity provenance, patient choice, and allocation feedback controls.
CORE METHOD MAP
Review all 19 Core Labs →7 Core Labs are exercised here
02 / STAKEHOLDERS
Success has competing definitions.
The design must represent authority, incentives, operational constraints, and unacceptable outcomes rather than collapsing stakeholder needs into a generic requirements list.
Patient access director
Wants shorter waits, fewer abandoned referrals, and a consistent cross-clinic coordination process.
Clinic operations manager
Owns local templates, staffing, rooms, equipment, exceptions, and the practical meaning of capacity.
Clinical service lead
Defines appropriateness, urgency, continuity, preparation, and conditions that require clinician review.
Privacy and security officer
Requires minimum-necessary access, purpose limitation, auditability, consent handling, and role boundaries.
03 / EVIDENCE PLAN
The operating truth must be reconstructed.
The guided evidence room combines structured data, policies, interviews, and operational records. Every material conclusion must remain traceable to source evidence and freshness.
Requested service, clinical priority, reason, ordering location, restrictions, and expiration.
Templates, holds, cancellations, overbooking, waitlists, and release rules.
Clinicians, skills, rooms, devices, support staff, and downtime.
Age, diagnosis, equipment, language, sedation, labs, imaging, and pre-visit requirements.
Travel distance, availability, accessibility, language, continuity preference, and communication channel.
Local approvals, referral acceptance, escalation, scheduling authority, and exception handling.
Wait time, no-shows, cancellations, rescheduling, completion, transfer, and abandonment.
Permitted fields, role access, consent, disclosure, retention, and access history.
EXPECTED DATA DEFECTSTemplate capacity mismatchLate cancellationsStale restrictionsDuplicate referralsHidden local holdsIncomplete patient preferences
04 / REQUIRED BUILD
A thin slice that can change a real decision.
The build must connect evidence, logic, human authority, failure handling, and measurement. A model or dashboard alone is not a complete intervention.
Canonical capacity model
Represent appointment inventory, resources, eligibility, referral status, and local restrictions at a shared decision grain.
Clinical eligibility filter
Apply deterministic and clinician-owned rules before ranking any option.
Patient-aware recommendation
Rank feasible choices using urgency, wait, travel, accessibility, continuity, and stated preferences.
Local review and reservation
Preserve clinic authority, prevent double booking, time-limit offers, and explain rejections.
Minimum-necessary interface
Expose only the data needed for the current coordination task with role-based access and audit.
Pilot and allocation monitoring
Track access, completion, burden, clinic distribution, overrides, and feedback loops.
05 / STAGED COMPLICATION
The fastest clinic becomes the default for everyone.
The complication is released only after the learner commits the first problem frame, architecture, and evaluation plan.
REVEAL CASE COMPLICATION+
The first pilot ranks clinics partly by prior acceptance speed and near-term availability. The largest clinic responds quickly, receives more referrals, gains more operational data, and appears even more reliable. Smaller clinics receive fewer referrals even when they are closer or clinically appropriate, while the large clinic begins protecting slots with additional local holds.
Required response: Revise ranking, capacity truth, local incentives, feedback features, and monitoring. Decide how to prevent response speed and historical volume from creating a self-reinforcing allocation loop.
Remove acceptance speed
Reduces feedback-loop risk but may ignore an operationally meaningful signal.
Capacity normalization
Compares clinics relative to resources but depends on accurate local capacity reporting.
Exploration allocation
Tests underused clinics deliberately but can increase uncertainty and coordination workload.
Patient-first choice set
Presents several eligible options rather than one ranking, shifting more decision burden to the patient.
06 / EVALUATION
Value and harm must be measured together.
The final deployment decision must use predeclared technical, operational, adoption, financial, and risk measures. The strongest metric cannot erase a critical failure.
| Measure | What it tests | Target behavior |
|---|---|---|
| Time to appropriate care | Referral-to-completed-visit time for clinically eligible services. | Improve by urgency and service type. |
| Clinical mismatch rate | Recommendations rejected for specialty, preparation, equipment, or continuity reasons. | Near zero for deterministic restrictions. |
| Patient burden | Travel, rescheduling, communication attempts, and preference mismatch. | Do not optimize wait time alone. |
| Allocation concentration | Referral share, slot use, and burden across clinics relative to capability. | Detect self-reinforcing routing. |
| Completion and no-show | Whether offered alternatives lead to completed care. | Measure outcomes beyond acceptance. |
| Privacy and access events | Minimum-necessary access, inappropriate viewing, export, and audit exceptions. | Critical controls must pass. |
Clinical eligibility is resolved before operational ranking.
The system recommends options and preserves patient, clinic, and clinician authority.
Capacity includes real resource and policy constraints rather than open calendar slots alone.
Ranking avoids using sensitive information beyond the permitted coordination purpose.
The evaluation examines travel, accessibility, continuity, and clinic concentration alongside wait time.
The rollout includes local ownership, correction workflows, audit, incident response, and rollback.
HOW TO WORK
Bring your own agent. Keep the evidence chain visible.
The lab permits agent-assisted investigation and implementation. Strong work records material agent recommendations, checks them against authorized evidence, and documents what the learner accepted, changed, rejected, or left unresolved.
Give bounded context.
Provide the mission objective, approved evidence, constraints, and required output rather than asking for a generic solution.
Ask for alternatives.
Require multiple hypotheses, failure modes, and disconfirming evidence before choosing an intervention.
Trace every material claim.
Check source IDs, calculations, code behavior, policy constraints, and unsupported causal language.
Own the recommendation.
Record why the final decision follows from the evidence and what would cause it to change.
07 / REQUIRED DELIVERABLES
The complete record of the deployment decision.
The reviewed lab will score evidence traceability, technical judgment, implementation quality, risk handling, operating readiness, and the consistency of the final recommendation.
Coordination decision frame
Decision boundaries, clinical authority, patient choice, baseline, and value hypothesis.
Capacity and eligibility model
Referral, service, resources, restrictions, preferences, lineage, and freshness.
Recommendation workflow
Filtering, ranking, choice presentation, local review, reservation, and escalation.
Privacy and access design
Roles, permitted fields, purpose, audit, consent, retention, and incident handling.
Pilot and allocation evaluation
Access, appropriateness, burden, completion, concentration, and adoption measures.
Executive implementation recommendation
Conditions, unresolved risks, operating ownership, rollout, and scale/revise/stop decision.
GUIDED LAB
Enter the D2V Mock Health guided lab.
The guided lab is now available with synthetic evidence, a role-based workspace, staged complication, evaluation record, and implementation artifacts.