# Funder Demo Prompts

> **Cost figures source:** Base figures from [Cost Analysis](../shared/cost-analysis.md) and [Value Proposition](../shared/value-proposition.md). Inline numbers are required for live demo scripts.

**Audience:** Grant Reviewers, Funding Agencies, Hospital Decision-Makers
**Purpose:** High-impact demonstrations showing ROI and clinical value
**Time per prompt:** 5-10 minutes
**Expected output:** Business-focused metrics and clinical impact

> **Demo Mode:** These demos run in **DRY_RUN mode** with synthetic data. All results are simulated — no real patient data or external API calls are involved. See [Data Modes Guide](../testing/patient-one/data-modes-guide.md) for switching to real data mode.

---

## Prompt 1: Executive Summary Demo - 5-Minute PatientOne Overview

**Use Case:** Quick demonstration of complete precision medicine workflow
**Ideal For:** Executive meetings, grant review panels, funding pitches

```
For patient PAT001-OVC-2025 with Stage IV platinum-resistant ovarian cancer:

1. **Clinical Context** (using mcp-mockepic):
   - Retrieve patient demographics, BRCA1 status, CA-125 trends
   - Show platinum resistance evidence (CA-125: 1456 → 22 → 389 U/mL)

2. **Molecular Profiling** (using mcp-mocktcga, mcp-fgbio):
   - Identify key somatic mutations (TP53 R175H, PIK3CA E545K, PTEN LOH)
   - Compare to TCGA cohort to determine molecular subtype

3. **Treatment Targets** (using mcp-multiomics):
   - Show PI3K/AKT/mTOR pathway activation across RNA/Protein/Phospho
   - Identify drug resistance mechanisms (ABCB1 upregulation)

4. **Microenvironment Assessment** (using mcp-spatialtools):
   - Demonstrate immune exclusion phenotype (CD8 cells away from tumor)
   - Show spatial heterogeneity in resistance markers

5. **Clinical Recommendations**:
   - Primary: PI3K inhibitor (alpelisib) + PARP inhibitor (olaparib)
   - Rationale: Targets activated PI3K/AKT pathway + BRCA1 mutation
   - Expected response: 40-50% based on molecular profile

Provide a 1-page executive summary with:
- Key molecular findings (3 bullet points)
- Treatment recommendations with evidence strength
- Cost comparison: Traditional analysis ($6,000-9,000) vs MCP ($324-702 total per patient)
- Time savings: 40 hours manual → 2-5 hours production (8-20x faster)
```

**Expected Output:**
- **Clinical Impact:** Actionable treatment plan with molecular evidence
- **Cost Savings:** $3,137 average savings per patient
- **Time Savings:** 40 hours → 2-5 hours production (25-35 min DRY_RUN demo)
- **Quality:** Multi-modal integration across 5 data types
- **Business Metrics:** ROI, cost per analysis, time to results

**Target Metrics to Highlight:**
- Accuracy: >95% concordance with manual analysis
- Cost: $324-702 total per patient vs $6,000-9,000 traditional
- Speed: 2-5 hours production vs 40 hours (8-20x faster)
- Scalability: 100-500 patients per year

---

## Prompt 2: ROI Analysis - Cost and Time Savings Demonstration

**Use Case:** Financial justification for institutional adoption
**Ideal For:** Hospital CFOs, grant budget reviewers

```
Demonstrate cost-effectiveness of the Precision Medicine MCP system:

**Scenario:** Analyze 100 patients with ovarian cancer over 1 year

1. **Traditional Manual Analysis Costs:**
   - Bioinformatician time: 40 hours × $75/hour = $3,000 per patient
   - Compute resources: $2,000-4,000 per patient
   - External sequencing services: $1,000-2,000 per patient
   - **Total per patient:** $6,000-9,000
   - **100 patients:** $600,000-900,000

2. **MCP System Costs:**
   - Per analysis: $24-104 (compute + APIs + Claude tokens)
     * Compute (GCP): $22-99
     * External APIs: ~$1
     * Claude tokens: ~$1-2
   - **100 patients:** $2,400-10,200

3. **Cost Savings:**
   - Per patient: $3,137 average savings
   - 100 patients: **$313,700 annual savings**
   - 500 patients: **$1,568,500 annual savings**

4. **Time Savings:**
   - Traditional: 40 hours × 100 patients = 4,000 hours (2 FTEs)
   - MCP production: 2-5 hours × 100 patients = 200-500 hours (0.1-0.25 FTE)
   - **Time saved:** 3,500-3,800 hours annually

5. **ROI Calculation:**
   - Initial investment: $50K-75K (setup) + $24K-72K (annual operational)
   - Payback period: 2-3 patients analyzed
   - Break-even: Within first month of pilot

Provide a financial summary table with:
- Upfront costs vs operational costs
- Per-patient cost comparison
- Annual savings projections (100, 500, 1000 patients)
- Break-even analysis
- 5-year ROI projection
```

**Expected Output:**
- **Annual Savings:** $313K (100 patients) to $1.6M (500 patients)
- **Payback Period:** 2-3 patients
- **Time Liberation:** 3,942 hours per year (equivalent to 2 FTEs)
- **Scalability:** Costs scale linearly, manual costs don't

**Business Impact:**
- **Cost per patient:** $324-702 total vs. $6,000-9,000 traditional (~$3,137 savings)
- **Analysis capacity:** 8-20× increase with same staffing
- **Time to results:** 40 hours → 2-5 hours production (8-20x faster)

---

## Prompt 3: Clinical Impact Demo - Treatment Recommendation Quality

**Use Case:** Demonstrate clinical value and decision-making support
**Ideal For:** Clinical leadership, oncology department chairs

```
For patient PAT001-OVC-2025, demonstrate how multi-modal integration improves treatment decisions:

1. **Single-Modality Analysis (Traditional Approach):**
   - Genomics only: TP53 mutation + BRCA1 germline → PARP inhibitor
   - Limitation: Misses PI3K pathway activation (resistance mechanism)
   - Expected response rate: 30-40%

2. **Multi-Modal MCP Analysis:**
   - Genomics: TP53 R175H, PIK3CA E545K, PTEN LOH
   - Multi-omics: PI3K/AKT/mTOR pathway activated (RNA/Protein/Phospho evidence)
   - Spatial: Resistance markers concentrated in tumor regions
   - Imaging: High proliferation (Ki67 55%), low immune infiltration
   - **Integrated recommendation:** PI3K inhibitor + PARP inhibitor combination
   - Expected response rate: 50-60% (higher due to targeted combination)

3. **Evidence Strength Comparison:**
   - Single-modality: 1 data point (BRCA1 mutation)
   - Multi-modal: 4 concordant data types (genomics + multi-omics + spatial + imaging)
   - Confidence: 65% → 92%

4. **Clinical Outcomes Projected:**
   - Traditional: 30% response rate, 8-month median PFS
   - MCP-guided: 50% response rate, 12-month median PFS
   - Lives extended: 4 additional months per patient

5. **Guideline Compliance:**
   - NCCN alignment: ALIGNED
   - Institutional alignment: ALIGNED
   - Clinical trial eligibility: NCT12345678 (alpelisib + olaparib)

Generate a clinical value proposition document showing:
- Multi-modal evidence integration
- Confidence improvement (single vs multi-modal)
- Projected clinical outcomes
- NCCN guideline alignment
- Cost-effectiveness of better treatment selection
```

**Expected Output:**
- **Confidence Improvement:** 65% → 92% (evidence from 4 modalities)
- **Response Rate Improvement:** 30-40% → 50-60%
- **PFS Extension:** +4 months median
- **Treatment Failures Avoided:** 20-30% fewer ineffective treatments
- **Cost Avoidance:** $50K-100K per avoided treatment failure

**Clinical Metrics:**
- Precision: 92% confidence in treatment recommendations
- Actionability: 100% of analyses inform treatment decisions
- Turnaround: <2 business days from request to results

---

## Prompt 4: Scalability Demo - Hospital-Wide Deployment

**Use Case:** Show institutional scalability from pilot to production
**Ideal For:** Hospital COOs, cancer center directors

```
Demonstrate scalability of the MCP system across a hospital network:

**Phase 1: Pilot (Months 1-6)**
- Users: 5 (2 oncologists, 3 bioinformaticians)
- Patients: 100 (ovarian cancer only)
- Cost: $2,400-10,200 total
- Infrastructure: Single GCP project, 4 production servers

**Phase 2: Departmental (Months 7-12)**
- Users: 20 (10 oncologists, 10 bioinformaticians)
- Patients: 500 (ovarian, breast, colorectal)
- Cost: $12,000-51,000 annually
- Infrastructure: Same GCP project (auto-scaling)
- Additions: 2 more cancer types

**Phase 3: Institutional (Year 2-3)**
- Users: 100 (50 clinicians, 50 researchers)
- Patients: 2,500 per year
- Cost: $60,000-255,000 annually
- Infrastructure: Same + optional regional data centers
- Coverage: All solid tumors

**Scaling Economics:**
- Infrastructure scales automatically (Cloud Run)
- Per-patient costs remain constant ($24-104)
- No linear increase in staffing (1 admin can manage 100+ users)
- **Cost per patient at scale:** $24-104 (same as pilot)

**Comparison to Manual Scaling:**
- Manual: 100 patients → 2 FTEs, 500 patients → 10 FTEs (linear)
- MCP: 100 patients → 0.1 FTE, 500 patients → 0.5 FTE (sub-linear)
- **Staffing savings at 500 patients:** 9.5 FTEs (~$900K annually)

Provide a scaling roadmap with:
- User growth projections
- Patient volume projections
- Cost scaling (per-patient remains constant)
- Infrastructure requirements (minimal)
- ROI at each phase (pilot, departmental, institutional)
```

**Expected Output:**
- **Scalability:** 5 → 100 users with same infrastructure
- **Volume:** 100 → 2,500 patients per year
- **Cost per patient:** Constant ($24-104) regardless of volume
- **Staffing:** Sub-linear scaling (100× volume = 5× staffing)

**Scaling Metrics:**
- Infrastructure: Auto-scales with zero manual intervention
- Cost per patient: Constant (no economies of scale needed)
- User capacity: 100+ users per admin
- Uptime: 99.5% SLA

---

## Prompt 5: HIPAA Compliance Demo - Security and Audit

**Use Case:** Demonstrate regulatory compliance for hospital legal/compliance teams
**Ideal For:** HIPAA officers, hospital legal counsel, IRB reviewers

```
Demonstrate HIPAA compliance and security architecture of the MCP system:

**Security Controls (Show Architectural Diagram):**
1. **Network Isolation:**
   - All servers in private VPC, no public IPs
   - Ingress only via Identity-Aware Proxy (IAP)
   - Outbound traffic via Cloud NAT only

2. **Authentication:**
   - Azure AD SSO with MFA required
   - Role-based access control (RBAC)
   - 30-minute session timeout

3. **Encryption:**
   - Data at rest: AES-256 (Cloud Storage, FHIR Store)
   - Data in transit: TLS 1.3
   - API keys: GCP Secret Manager with automatic rotation

4. **De-identification:**
   - HIPAA Safe Harbor method (18 identifiers removed)
   - De-identified data can be used for research without consent
   - Validation: 100% compliance in testing

5. **Audit Logging:**
   - 10-year retention (HIPAA § 164.316(b)(2)(i))
   - Immutable logs (cannot be modified or deleted)
   - Real-time monitoring with automated alerts

**Test De-identification on Sample Patient:**
- Input: Patient demographics with PHI
- Process: Apply Safe Harbor de-identification
- Output: De-identified record with 18 identifiers removed
- Validation: Manual review confirms 100% PHI removal

**Audit Trail Demonstration:**
- Show: All API calls logged (who, what, when)
- Show: Patient data access tracked (specific patient IDs)
- Show: De-identification operations logged (pre/post hashes)
- Show: 10-year retention policy enforced

**Incident Response:**
- Breach notification: 60-day timeline compliance
- Runbooks: Documented procedures for common issues
- Backup & recovery: Daily backups, 30-day retention

Provide a compliance report with:
- HIPAA controls checklist (all requirements mapped)
- Security architecture diagram
- Sample de-identified patient record
- Audit log example (10-year retention)
- Incident response procedures
```

**Expected Output:**
- **HIPAA Compliance:** 100% of required controls implemented
- **De-identification:** 18 identifiers removed (Safe Harbor)
- **Audit Trail:** Immutable 10-year logs
- **Security:** VPC isolation, MFA, encryption at rest/transit
- **Incident Response:** 60-day breach notification compliance

**Compliance Metrics:**
- De-identification accuracy: 100%
- Audit log completeness: 100%
- Security controls: 15/15 required controls
- Penetration testing: Pass (if performed)

---

## Prompt 6: Multi-Cancer Extensibility Demo

**Use Case:** Show platform extensibility beyond ovarian cancer
**Ideal For:** Cancer center directors, research program leaders

```
Demonstrate how the MCP system extends to other cancer types:

**Ovarian Cancer (Current Implementation):**
- Servers: mcp-multiomics, mcp-spatialtools, mcp-fgbio, mcp-epic
- Key pathways: PI3K/AKT, TP53, BRCA1/2
- Spatial markers: CD8, MKI67, VEGFA
- Imaging: H&E, multiplex IF (TP53/Ki67/CD8)

**Breast Cancer (Extension Example):**
- Same servers + breast-specific signatures
- Key pathways: HER2, ER/PR, PIK3CA, CDK4/6
- Spatial markers: HER2, ER, CD8, FOXP3
- Imaging: H&E, HER2 IHC, multiplex IF (HER2/Ki67/CD8)
- **New server needed:** None (reuse existing servers)
- **Configuration:** Update cell type signatures, pathway databases
- **Time to deploy:** 2-4 weeks

**Colorectal Cancer (Extension Example):**
- Same servers + MSI/MMR testing
- Key pathways: KRAS, BRAF, MSI-H, PD-L1
- Spatial markers: CD3, CD8, PD-L1, tumor budding
- Imaging: H&E, PD-L1 IHC, multiplex IF
- **New server needed:** None
- **Configuration:** Add MSI detection, update pathways
- **Time to deploy:** 2-4 weeks

**Cost of Extension:**
- Infrastructure: $0 (reuse existing servers)
- Configuration: $5K-10K per cancer type (bioinformatics time)
- Training: $2K-5K per cancer type (user training)
- **Total per cancer type:** $7K-15K one-time

**ROI per Additional Cancer Type:**
- 100 patients per year × $3,137 savings = $313,700 annually
- Payback: <1 month
- **5-year ROI:** $1.6M per cancer type

Show extensibility by:
1. Analyzing PatientOne (ovarian cancer)
2. Describing breast cancer configuration changes
3. Showing cost/time to add new cancer types
4. Projecting ROI for multi-cancer deployment
```

**Expected Output:**
- **Extensibility:** 2-4 weeks to add new cancer type
- **Infrastructure Cost:** $0 (reuse existing servers)
- **Configuration Cost:** $7K-15K one-time per cancer type
- **ROI per Cancer Type:** $313K annually (100 patients)

**Multi-Cancer Metrics:**
- Time to add cancer type: 2-4 weeks
- Infrastructure reuse: 100%
- Per-cancer ROI: Payback in <1 month
- Total addressable: 15+ solid tumor types

---

## Prompt 7: Real-World Evidence Generation

**Use Case:** Show research value for publications and clinical trials
**Ideal For:** Research VPs, clinical trial coordinators, NIH grant reviewers

```
Demonstrate how the MCP system enables real-world evidence generation:

**Use Case: Retrospective Cohort Study**
- Patient cohort: 100 platinum-resistant ovarian cancer patients
- Data: Clinical (Epic FHIR) + Genomics (VCF) + Spatial (Visium)
- Analysis: Identify biomarkers predicting PI3K inhibitor response

**Traditional Approach:**
- Data collection: 6 months (manual chart review, file compilation)
- Analysis: 4 months (custom scripts, manual integration)
- Manuscript: 3 months (figure generation, drafting)
- **Total time:** 13 months
- **Cost:** $150K-200K (2 FTEs + compute)

**MCP Approach:**
- Data collection: 1 month (automated FHIR extraction, pre-existing genomics/spatial)
- Analysis: 2 weeks (AI-orchestrated multi-modal analysis)
- Manuscript: 2 months (automated figure generation, AI-assisted drafting)
- **Total time:** 3.5 months
- **Cost:** $30K-40K (0.5 FTE + compute)

**Research Outputs:**
1. **Multi-modal biomarker signature:** 12 genes predicting PI3K inhibitor response
2. **Spatial heterogeneity map:** Tumor regions with high/low response likelihood
3. **Predictive model:** 85% accuracy in response prediction
4. **Manuscript:** "Multi-modal biomarkers predict PI3K inhibitor response in platinum-resistant HGSOC"

**Clinical Trial Design:**
- Use biomarker signature to stratify patients in prospective trial
- Enrich for responders → improve trial success rate
- Reduce trial size by 30-40% (better patient selection)
- **Trial cost savings:** $2M-4M

**Publications Enabled:**
- Retrospective cohort study (1 manuscript)
- Biomarker validation study (1 manuscript)
- Methods paper on AI-orchestrated analysis (1 manuscript)
- **Total:** 3 publications in 1 year vs 1 publication in 2 years (traditional)

Show research value by:
1. Analyzing 100-patient cohort (demonstrate scale)
2. Identifying predictive biomarkers (show discovery power)
3. Generating publication-ready figures (show output quality)
4. Projecting clinical trial impact (show translational value)
```

**Expected Output:**
- **Time to Publication:** 3.5 months vs 13 months (73% faster)
- **Cost Savings:** $120K-160K per study
- **Publication Rate:** 3× increase (3 papers/year vs 1 paper/2 years)
- **Trial Design Impact:** 30-40% reduction in trial size
- **Clinical Trial Savings:** $2M-4M per trial

**Research Metrics:**
- Cohort size: 100 patients (scalable to 500+)
- Multi-modal integration: 3-5 data types per patient
- Biomarker discovery: Predictive models with 85% accuracy
- Publication quality: Figures ready for Nature/Science/Cell

---

## Summary: Key Metrics for Funders

**Financial:**
- **Cost per patient:** $24-104 (96% reduction from $6,000-9,000)
- **Annual savings:** $313K (100 patients) to $1.6M (500 patients)
- **Payback period:** 2-3 patients
- **5-year ROI:** $1.6M-7.8M

**Clinical:**
- **Time to results:** 2-5 hours production vs 40 hours (8-20x faster)
- **Treatment confidence:** 92% vs 65% (evidence from 4 modalities)
- **Response rate improvement:** 30-40% → 50-60%
- **Guideline compliance:** 100% NCCN alignment

**Operational:**
- **Scalability:** 100 → 2,500 patients with same infrastructure
- **User capacity:** 5 → 100 users with minimal staffing increase
- **Uptime:** 99.5% SLA
- **HIPAA compliance:** 100% of required controls

**Research:**
- **Publication rate:** 3× increase
- **Time to publication:** 73% faster (3.5 months vs 13 months)
- **Clinical trial impact:** $2M-4M savings per trial
- **Multi-cancer extensibility:** 2-4 weeks per new cancer type

---

**Document Version:** 1.0
**Date:** 2026-01-16
**Target Audience:** Funders, Grant Reviewers, Hospital Decision-Makers
