Not a dashboard. A decision engine.
Your analysts have the data. Your models have predictions. You still can't defend the decision.
Decisions are hard because of uncertainty and trade-offs. Entity Value helps leaders make choices that are explainable, auditable, and updateable — by externalizing objectives, constraints, assumptions, and what would change the conclusion.
Outputs: Decision Brief (10p) + Methods & Assumptions (2–8p) + What-Changes-My-Mind (1p)
Auditable Decision Packet
All results delivered as verifiable Decision Packets
Complete decision packages with CEA, budget impact, uncertainty analysis, and audit logs
권고: 옵션 B - ICER ₩5천만/QALY, 85% 확률로 ₩6천만 기준 비용효과적
비용효과분석
예산영향분석
불확실성분석 (PSA)
민감도 범위: ₩4천만 - ₩5천2백만/QALY (95% 신뢰구간)
What Entity Value Does
One decision engine. Multiple policy domains.
Who needs a Decision Engine?
Organizations where decisions involve trade-offs, uncertainty, and accountability.
Policy Decision-Makers
국회/지자체/정부기관 의사결정자
“Which policy option should we fund — and how do we defend that choice under audit?”
You need:
scenario comparison, cost-effectiveness, budget impact, and explicit documentation of what would change the recommendation.
Healthcare Executives
병원 경영진 / 보험자 / 제약·의료기기 기업
“Is this intervention cost-effective — and how sensitive is that conclusion to our assumptions?”
You need:
CEA with ICER, probabilistic sensitivity analysis, budget impact over time, and audit-ready methodology documentation.
Analysts & Reviewers
정책분석가 / 감사·자문위원 / 연구자
“Can I reproduce this result? Can I trace the recommendation back to specific data and assumptions?”
You need:
version-tracked data, transparent model structure, assumption registry, and run-level auditability.
Why Doogooda
Differentiated decision support capabilities
Slow integration? No problem.
We create your first Decision Packet with external demand + domain ontology + minimal operational input.
Only then do we expand internal data integration to the necessary scope.
External-first Demand Engine
We connect public health, population, and regional indicators first, generating baselines and scenarios even before internal integration.
Ontology-as-Compression
Domain ontology maps data/policy/resources to a unified framework, reducing IT burden on hospitals.
Decision Unit Packaging
We deploy 'one decision' in 6–8 weeks, not a platform.
Traceable Assumptions
All underlying assumptions are explicit and traceable. Results can be verified for reliability.
Counterfactual Evaluation
Analyze what made the difference. Evaluate the causal effects of decisions.
Constraint-aware Optimization
Find optimal solutions without violating real-world constraints.
Leadership & Credentials
Harvard PhD · Former UCL faculty — decision science applied to healthcare & public policy.
Engagement Options
Three engagement models optimized for decision-making processes
View sample Decision Packet (1 min)💡 Start without EHR integration: We create your first Decision Packet with external demand indicators + domain ontology + minimal operational input.
Decision Ops Diagnostic
Define one decision as a "payable unit" and create a baseline with minimal data.
Duration
2 weeks
Data Required
Aggregate KPI (weekly/monthly), resource status (staff·slots·rooms·hours), operational constraints (max 5 rules)
Deliverables
Decision Brief (1p) + KPI Baseline + Data/Constraint Spec (1p) + Next Sprint Plan
Evidence-to-Action Sprint
Compare 3-5 scenarios and deliver recommendations with constraints, evidence, and trade-offs.
Duration
6–8 weeks
Data Required
External demand (population/epidemiology/regional indicators) + Internal minimal operational data (volume/resources/constraints)
Deliverables
Scenario Pack (3–5) + Action Recommendations + Audit Pack (evidence·assumptions·constraints·alternatives·logs) + Next Experiment
Ops Retainer
Update decision rules in response to policy/rule/environment changes and deliver monthly Decision Packets.
Duration
Monthly (minimum 3 months recommended)
Data Required
Monthly operational KPI + change events (policy/staffing/promotions/seasonality, etc.)
Deliverables
Monthly Decision Packet (summary+scenarios+recommendations+audit logs) + drift/rule violation alerts + rule updates
* Specific deliverables may be adjusted based on scope and data access level. (Finalized after NDA if required)
FAQ
Frequently Asked Questions
Trust Signals
Official selections, institutional invitations, and project track record
Sample Decision Packet
Real project deliverable structure - CEA·BIA·PSA·Audit
3-page PDF · Synthetic Data
Case Studies
Real project outcomes across industries
National Assembly — Healthcare Workforce Policy
ER Operations Optimization
Provincial Assembly Policy Impact Analysis
Defense Decision Support System
National Assembly (Anonymized)
Location: Seoul, Korea
Simulated 4 policy scenarios for medical workforce distribution and regional access. Delivered audit-ready Decision Packet for parliamentary committee.
Problem
When comparing policy alternatives, cost-effectiveness analysis, budget impact, and uncertainty analysis were fragmented, making it difficult for decision-makers to judge from an integrated perspective. Evidence documentation for internal persuasion and audit response was not systematically organized.
Result
Provided integrated document enabling clear comparison of decision options. Used as evidence for internal persuasion, shortening policy consensus process. Available as transparent decision rationale for audit response.
How we turn data into decisions
How the decision engine works
A 5-step process for making measurable decisions under uncertainty
Decision Framing
What needs to be decided? Who are the stakeholders? What are the constraints? — We clearly define the decision problem.
Output
Decision Frame Document — objectives, stakeholders, constraints
Minimal Data Start
Start with minimal internal data. Fill gaps with public data, literature, expert opinions.
Output
Data Source Map — internal data + external supplement specs
Causal & Forecast Layer
Model causal relationships and forecast the future. Simulate outcomes by scenario.
Output
Forecast Model + scenario outcomes (cost, effectiveness, risk)
Optimization & Scenarios
Find optimal scenarios under constraints. Perform CEA, BIA, and PSA analyses.
Output
Optimal scenario recommendations + CEA/BIA/PSA results
Live Monitoring & Updates
Update models with real-time data. When conditions change, recommendations update too.
Output
Live dashboard + update logs + condition change alerts
Architecture
A layered system: Source → Ontology → Insight → Product, designed for auditability and controlled deployment.
Shown as a conceptual diagram; implementation varies by client constraints.
Ontology
Domain-specific knowledge graph enables consistent decision logic.
Ontology-backed model layer (example)
Credibility comes from method, constraints, and decision trails—not badges.
Doogooda doesn't build "AI recommendations." We design decision workflows where constraints, assumptions, rationale, and trade-offs are documented. Research doesn't end as a paper—it ships as reproducible deliverables (Decision Packets) in operational environments.
What this changes
- Decision rationale is documented in logs and records.
- Constraints (budget, staffing, regulations) are verified for compliance.
- Deliverables include operational loops for the next iteration.
Collaboration
If you fit one of these types, you'll connect fastest.
Hospital/clinic operations decision-maker
Buyer / Operator Path
What to send
- One-line decision problem
- Available data level (none is okay)
- Decision timeline (2–10 weeks)
Data/operations system partner
Implementation Ally
What to send
- Target integration systems
- Security/access constraints
- Project scope
Partner to co-create auditable methodologies and validation frameworks
Research / Methods Collaboration
What to send
- Research question
- Deliverable format (brief/whitepaper/pilot)
- Data accessibility
Contact Us
Request a pilot project or proposal