# Vivum

*/Startups/Vivum*

## Startup Overview

This platform normalizes multi-site electronic health records to automatically match patients against complex clinical trial protocols. It ingests disparate clinical data—ranging from lab results to physician notes—and standardizes the information to map directly against specific inclusion and exclusion criteria.

Clinical research coordinators and trial sponsors typically rely on slow manual chart abstraction or heavyweight data networks that take months to integrate. This system removes the bottleneck of manual screening by allowing research teams to query entire patient populations across fragmented hospital IT networks without requiring local data transformation.

Unlike TriNetX or Deep6 AI, which often rely on probabilistic models or demand extensive bespoke IT integration, this architecture is fully deterministic and instantly deployable across diverse hospital environments. It delivers auditable, exact patient matches for clinical protocols, eliminating black-box uncertainty and accelerating trial enrollment.

## Startup Founding Hypothesis

**Approach**: that normalizes multi-site health records for clinical protocol matching
**Competitors**:
- [Manual Chart Abstraction](/Competitors/Manual_Chart_Abstraction)
- [TriNetX](/Competitors/TriNetX)
- [Deep6 AI](/Competitors/Deep6_AI)
**Differentiator2x2**: fully deterministic and instantly deployable across diverse hospital IT environments

## Startup Solution Coordinate

**Solution**: [Clinical Protocol Engine](/Software/Clinical_Protocol_Engine)

## Startup Position2x2

```mermaid
quadrantChart
    title Clinical Protocol Matching
    x-axis Probabilistic AI --> Fully Deterministic
    y-axis Heavy IT Integration --> Instantly Deployable
    Manual Chart Abstraction: [0.90, 0.10]
    Deep6 AI: [0.35, 0.25]
    TriNetX: [0.25, 0.80]
    Vivum: [0.95, 0.95]
```

## Startup Offer

**Proof**:
- Reduce protocol screening time for a regional oncology center by 80%
- Identify eligible trial candidates from unstructured EMR notes for a mid-sized CRO
- Deploy across three distinct legacy EMR environments in under 7 days without custom API engineering
**Tiers**:
- Name: Department Pilot · Price: ~$20k–$35k/yr · Inclusions: Normalization for a single hospital site or department, up to 5,000 monthly patient record ingestions matched against up to 10 active clinical protocols.
- Name: Multi-Site Network · Price: ~$60k–$90k/yr · Inclusions: Deployment across up to 5 diverse IT environments, up to 50,000 monthly record ingestions, and deterministic mapping for unlimited active protocols.
- Name: Enterprise CRO · Price: Custom · Inclusions: Unlimited site deployments and custom protocol ingests, designed to support national health systems or full-scale Contract Research Organizations.
**Guarantee**: Vivum guarantees deterministic extraction accuracy for all ingested records; if our engine fails to cite the exact source text for a protocol match, the processing fees for that patient cohort are waived.
**Business Function**: ProvideService
**Objection Handlers**:
- Objection: Our hospital network uses fragmented, legacy EMR systems. Rebuttal: Vivum is designed to normalize raw text exports from diverse IT environments, bypassing the need for complex direct API integrations.
- Objection: Principal investigators reject 'black-box' AI trial matching. Rebuttal: Vivum is fully deterministic, providing line-level source document citations for every matched inclusion and exclusion criterion.
- Objection: We cannot send protected health information (PHI) to a third-party cloud. Rebuttal: Vivum is intended to deploy directly within your institution's secure VPC, ensuring PHI never leaves your perimeter.
**Pricing Architecture**: Tiered

## Startup Brand

**Voice**: Authoritative clinical register characterized by absolute diagnostic precision.
**Tagline**: Deterministic patient matching for multi-site clinical trials.
**Icon Concept**: clipboard
**Palette Intent**: institutional-cool
**Visual Identity**: Crisp medical blues and sterile whites define a highly structured layout that evokes the strict formatting of electronic health records, grounded by precise monospace typography.
**Archetype Reference**: the-sage

## Startup Buyer Chain

**Chain**: Vivum → Hospital Research Coordinators → Pharmaceutical Sponsors → Clinical Trial Patients
**Gtm Motion**: Acquires initial health system deployments through direct outbound targeting Clinical Research Directors struggling to meet trial enrollment quotas. Expands by standardizing trial matching across multiple therapeutic departments within the health system, eventually co-selling aggregated site access to pharmaceutical sponsors.
**Agent Channel**: Designed to list its deterministic matching API within structured AI tool registries and intended EHR agent directories, allowing autonomous pharmaceutical research agents to discover the endpoint and query normalized patient cohorts programmatically.
**Primary Channel**: Direct outbound campaigns that scrape active, under-enrolling clinical trials from ClinicalTrials.gov to identify and contact the specifically listed Principal Investigators and site coordinators.

## Startup Customer Journey

```mermaid
flowchart LR; A[Under-Enrolling Trial] --> B[Principal Investigator]; B --> C[Department Pilot]; C --> D[Secure VPC]; D --> E[EMR Source Match]; E --> F[Multi-Site Network]; F --> G[Pharmaceutical Sponsor];
```

## Startup Proof Points

_Illustrative — target and order-of-magnitude estimate figures, not an achieved track record (this Thing is concept-stage)._

**Pilot Goals**:
- A 30-day single-department pilot matching up to 5000 patient record ingestions against 10 active clinical protocols to prove complete deterministic extraction accuracy.
- A 60-day multi-site pilot ingesting raw text exports from three distinct legacy EMR environments to demonstrate immediate normalization without direct API integration.
**Target Metrics**:
- Target: 80 percent reduction in protocol screening time per patient cohort
- Aim: 100 percent line-level source citation rate for matched inclusion and exclusion criteria
- Target: Under 7 days to deploy across distinct legacy EMR environments without custom APIs
**Target Case Studies**:
- A regional oncology center: transforming unstructured EMR note exports into a validated list of trial-eligible candidates to reduce manual screening time.
- A mid-sized Contract Research Organization: deploying across multiple legacy hospital IT environments to normalize patient ingestion without custom API engineering.
**Testimonial Targets**:
- Principal Investigator: validation that deterministic source document citations eliminate the black-box hesitation typically associated with automated trial matching.
- Chief Information Security Officer: confirmation that the direct VPC deployment successfully mapped trial criteria while ensuring zero Protected Health Information left the hospital perimeter.
- Clinical Trial Coordinator: sentiment that shifting from manually reading unstructured clinical notes to verifying highlighted source text significantly increased their enrollment capacity.

## Startup Top Risks

**Risks**:
- Severity: existential · Description: Hospital IT security and compliance committees block the instantly deployable integration due to stringent HIPAA and on-premise data governance requirements. · Mitigation Status: unmitigated
- Severity: high · Description: Deterministic matching algorithms fail to extract critical clinical nuances buried in unstructured physician notes, resulting in lower protocol matching rates than manual abstraction. · Mitigation Status: in-progress
- Severity: high · Description: Incumbent networks like TriNetX leverage their existing exclusive partnerships with large hospital systems and pharma sponsors to lock Vivum out of enterprise clinical trial contracts. · Mitigation Status: unmitigated
- Severity: moderate · Description: Enterprise healthcare sales cycles drag beyond eighteen months, exhausting capital runway before the platform secures enough hospital nodes to attract pharmaceutical buyers. · Mitigation Status: in-progress

## Startup Competitors

- [Manual Chart Abstraction](/Competitors/Manual_Chart_Abstraction) — Status Quo
- [TriNetX](/Competitors/TriNetX) — Incumbent
- [Deep6 AI](/Competitors/Deep6_AI) — AI Platform
- [Epic Cosmos](/Competitors/Epic_Cosmos) — EHR Network
- [Mendel AI](/Competitors/Mendel_AI) — Clinical AI

## Startup Solution Stack

- [Protocol Matching Service](/Services/Protocol_Matching_Service) — Service-as-Software
- [Chart Abstraction Agent](/Agents/Chart_Abstraction_Agent) — Agent
- [Record Normalization Worker](/Agents/Record_Normalization_Worker) — Agent
- [Deterministic Matching Engine](/Software/Deterministic_Matching_Engine) — Software
- [Hospital Integration API](/Software/Hospital_Integration_API) — Software

## Startup Story Brand

**Hero**:
- **Need**: to be the rigorous gatekeeper of medical data, not a manual chart transcriber
- **Want**: to match eligible patients to complex clinical trial protocols instantly
- **Identity**: the clinical research coordinator at a multi-site health system
**Plan**:
- Step: Select protocols · Detail: Define your specific inclusion and exclusion criteria for any active clinical trial in your portfolio.
- Step: Validate matches · Detail: Review the deterministic citations that link every patient record to the exact medical text source.
- Step: Enroll patients · Detail: Act on high-fidelity matches across all your hospital sites simultaneously with verifiable accuracy.
**Guide**:
- **Empathy**: Does your screening process still lose potential trial candidates to unstructured legacy EMR notes?
**Problem**:
- **Villain**: manual chart abstraction
- **External**: Screening a single oncology patient requires cross-referencing unstructured EMR notes across legacy Epic and Cerner environments for hours
- **Internal**: You worry that life-saving trial opportunities are missed due to human error and data fragmentation
- **Philosophical**: Every patient deserves an immediate match to a trial — not a clerical backlog.
**Success**: Screening time drops by 80% as fragmented legacy records become a single, searchable stream of trial-ready data.
**One Liner**: What if your protocol screening happened instantly across any legacy EMR? Vivum normalizes health records deterministically, delivering verifiable trial matches with line-level citations.
**Positioning**:
- **So That**: identify eligible candidates across fragmented EMRs with absolute source-text certainty
- **Unlike**: manual chart abstraction
- **For Whom**: multi-site clinical research coordinators
- **Category**: Clinical trial matching for health systems
**Call To Action**:
- **Direct**: Run department pilot
- **Transitional**: View sample normalization report
**Failure Stakes**:
- Missed trial enrollment targets
- Exclusion of eligible minority cohorts
- Protocol screening delays
**Transformation**:
- **To**: one of the few coordinators who scales multi-site enrollments
- **From**: a researcher buried in manual chart pulls
**Controlling Idea**: Trial matching should be a deterministic search, not a manual hunt.

## Startup Token Hero

**Genre**: founding-hypothesis
**Rendered**: What if your protocol screening happened instantly across any legacy EMR? Vivum normalizes health records deterministically, delivering verifiable trial matches with line-level citations.
**Mechanism**: spine-derived-v1
**Template Id**: spine-founding-hypothesis
**Vocab Fingerprint**: 8264484e8bdc6061

## Startup Token Positioning

**Genre**: moore-positioning
**Rendered**: Clinical trial matching for health systems for multi-site clinical research coordinators. Unlike manual chart abstraction — identify eligible candidates across fragmented EMRs with absolute source-text certainty.
**Mechanism**: spine-derived-v1
**Template Id**: spine-moore-positioning
**Vocab Fingerprint**: 419459efd928f0d5

## Startup Token Pitch Deck

**Genre**: pitch-deck
**Rendered**: Problem: Screening a single oncology patient requires cross-referencing unstructured EMR notes across legacy Epic and Cerner environments for hours
Solution: What if your protocol screening happened instantly across any legacy EMR? Vivum normalizes health records deterministically, delivering verifiable trial matches with line-level citations.
Customer: multi-site clinical research coordinators
Unlike: manual chart abstraction
**Mechanism**: spine-derived-v1
**Template Id**: spine-pitch-deck
**Vocab Fingerprint**: ef3777aaf4c9e16b

## Startup Token M E D D P I C C

**Pain**: Screening a single oncology patient requires cross-referencing unstructured EMR notes across legacy Epic and Cerner environments for hours
**Metrics**: Target: Screening time drops by 80% as fragmented legacy records become a single, searchable stream of trial-ready data.
**Rendered**: Pain: Screening a single oncology patient requires cross-referencing unstructured EMR notes across legacy Epic and Cerner environments for hours
Economic buyer: Hospital Research Coordinators
Metrics: Target: Screening time drops by 80% as fragmented legacy records become a single, searchable stream of trial-ready data.
Competition: manual chart abstraction
**Mechanism**: spine-derived-v1
**Competition**: manual chart abstraction
**Economic Buyer**: Hospital Research Coordinators
**Vocab Fingerprint**: 98a0e89957e625a3

## Startup Token Cold Email

**Genre**: cold-email
**Rendered**: Subject: Clinical trial matching for health systems for multi-site clinical research coordinators

multi-site clinical research coordinators — Screening a single oncology patient requires cross-referencing unstructured EMR notes across legacy Epic and Cerner environments for hours What if your protocol screening happened instantly across any legacy EMR? Vivum normalizes health records deterministically, delivering verifiable trial matches with line-level citations.
**Mechanism**: spine-derived-v1
**Template Id**: spine-cold-email
**Vocab Fingerprint**: 02ca94d86f5a2909

## Startup Token Agent Spec

**Genre**: ai-agent-spec
**Rendered**: Clinical trial matching for health systems. What if your protocol screening happened instantly across any legacy EMR? Vivum normalizes health records deterministically, delivering verifiable trial matches with line-level citations. Serves multi-site clinical research coordinators.
**Mechanism**: spine-derived-v1
**Template Id**: spine-ai-agent-spec
**Vocab Fingerprint**: 45da2a1822a69c1b

## Neighborhood

### Candidate solutions

- [API Integration Drop-Off](/Problems/API_Integration_Drop-Off) — candidate solution for · Problems

### What it offers

- [Clinical Protocol Engine](/Software/Clinical_Protocol_Engine) — offers · Software

### Composed of

- [Protocol Matching Service](/Services/Protocol_Matching_Service) — composes · Services
- [Chart Abstraction Agent](/Agents/Chart_Abstraction_Agent) — composes · Agents
- [Record Normalization Worker](/Agents/Record_Normalization_Worker) — composes · Agents
- [Deterministic Matching Engine](/Software/Deterministic_Matching_Engine) — composes · Software
- [Hospital Integration API](/Software/Hospital_Integration_API) — composes · Software

### Embodies

- [Software](/Theses/Software) — embodies · Theses

### Competitors

- [Epic Cosmos](/Competitors/Epic_Cosmos) — competes with · Competitors
- [Mendel AI](/Competitors/Mendel_AI) — competes with · Competitors
- [TriNetX](/Competitors/TriNetX) — competes with · Competitors
- [Deep6 AI](/Competitors/Deep6_AI) — competes with · Competitors
- [Manual Chart Abstraction](/Competitors/Manual_Chart_Abstraction) — competes with · Competitors

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