Chief Health AI

Programs

Evidence-Based Health AI Training

8 weeks

Best for: Technical and clinical professionals entering evidence-based health AI — software engineers, data scientists, health IT professionals, physicians, nurse practitioners, PAs, public health professionals, consultants, and product managers

✓Built on curriculum that began development through the Stanford Medicine Health Futurist program and taught at Morehouse College
✓Learn to evaluate health AI claims against source literature, design deployment thresholds, and build monitoring plans
✓Cohorts forming now

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EvidenceCycle Sprint

Decide

4 weeks

Best for: One urgent decision requiring evidence discipline fast

Outcomes

✓A decision-ready recommendation grounded in traceable evidence
✓Stakeholders aligned on risks, unknowns, and what would change the decision
✓A reusable EvidenceCycle workspace your team can keep using

Deliverables

—EvidenceVault (curated and traceable)
—EvidenceCards (focused set covering decision-critical claims/risks; recommended)
—EvidenceAtlas (final) with embedded EvidenceCycle Brief(s)
—One-page executive summary with risk/unknowns register and 'what would change our mind?'

Requirements

●Named decision owner + 2-6 stakeholders
●Access to relevant context (current workflows, constraints, vendor documentation)
●60-90 minute weekly working session + async feedback cycles

Request scoping call for proposal

EvidenceCycle Implementation

Deploy Safely

8-12 weeks

Best for: Organizations preparing to deploy or scale AI where success depends on evaluation, monitoring, governance, and workflow fit—not just a decision memo

Outcomes

✓Decision readiness and deployment readiness
✓A practical evaluation + monitoring plan aligned to real workflows
✓EvidenceCycle OS embedded into your team's way of working (repeatable cadence)

Deliverables

—EvidenceAtlas (final) with embedded EvidenceCycle Brief(s)
—EvidenceBench (final) with embedded EvidenceCycle Brief(s)
—Pre-deployment tests, metrics, thresholds, governance/escalation protocols
—EvidenceCycle OS workspace (templates, roles, weekly cadence for your team)
—Handoff pack for leadership and implementers

Requirements

●Decision owner + implementation lead + security/compliance/workflow stakeholders
●Ability to run lightweight pilots/tests (or define how you would)
●60-90 minute weekly working session + periodic leadership check-ins

Request scoping call for proposal

EvidenceCycle OS (Ongoing)

Stay Current

Monthly or Quarterly

Best for: Keeping decisions defensible as evidence, vendors, and regulations change

Entry point: After completing Program A (Sprint) or Program B (Implementation)

Radar Lite

Monthly or Quarterly

▶Ingest new evidence and product changes
▶'What changed?' deltas against your current position
▶Refresh Vault/Cards/Briefs + update open questions
▶Quarterly decision check-in

Embedded Support

Fractional Team

▶Ongoing EvidenceCycle operator support inside your workflow
▶Continuous updates to Atlas/Bench artifacts
▶Training + governance cadence support

Deliverables

—Updated Vault/Cards and short delta brief ('what changed + what it means')
—Updated monitoring plan signals/thresholds if relevant
—Decision-ready update memos for leadership

Request scoping call for proposal

EvidenceCycleAI (Pro)

Coming Soon

Best for: Teams with enough evidence volume that manual upkeep becomes a bottleneck

•Faster evidence ingestion and deduplication
•Change detection + alerts ('new evidence that impacts our decision')
•Auto-drafted deltas/briefs for human review
•Evaluation harness scaffolds to accelerate Bench work

EvidenceCycleAI accelerates the operating system—it does not replace human judgment.

Optional Add-Ons

+Guest expert session (clinical operations, safety, policy, evaluation methods)
+Vendor comparison pack (structured EvidenceCards + procurement appendix)
+Private academic-industry roundtable tied to your focus area