Your instinct is right. We make it defensible.
We help hospital leaders make data-driven decisions and prove they were right — without needing a data team, new tools, or a PhD.
Already deployed in hospital systems managing 500+ beds. Currently in WEDC-backed gBETA Madison.
As Featured In
Selected through WEDC's Accelerate Wisconsin initiative as one of five startups for gBETA Madison.
The most expensive mistakes aren't bad decisions. They're decisions that were never revisited.
Your analysts have the data. Your models make predictions.
But when it's time to decide — how many nurses next quarter, which service line to expand, whether to add weekend OR capacity — the decision still happens in a conference room with slides, gut feel, and no audit trail.
The gap isn't data. It's the last mile: turning analysis into a defensible decision that survives the CFO, the board, and the audit.
Entity Flow closes that gap.
Not a dashboard. A decision engine.
Entity Flow generates scenario-ranked, constraint-aware, auditable decision packages for hospital operations.
Scenario Engine
Model 3–5 staffing or capacity scenarios with real constraints — budget, regulations, union rules, seasonal demand. See trade-offs side by side.
Decision Brief
A 10-page decision document your COO can defend to the board. Recommended action + reasoning + what changes the answer.
What-Changes-My-Mind
The 1-page document that tells you when to revisit this decision. Top 5 conditions that would change the recommendation. No other vendor provides this.
What you get: Decision Brief (10p) + Methods & Assumptions (2–8p) + What-Changes-My-Mind (1p)
Download a sample →First decision in 6 weeks.
No EHR integration required.
Frame
Define the decision. What's the question? What are the constraints? We start with public data + minimal operational inputs.
Model
Build scenarios. Run cost-effectiveness, budget impact, and sensitivity analysis. Rank options by your constraints.
Deliver
Deliver Decision Brief + What-Changes-My-Mind. Your COO has a defensible recommendation. Your board has an audit trail.
Frame
Define the decision. What's the question? What are the constraints? We start with public data + minimal operational inputs.
Model
Build scenarios. Run cost-effectiveness, budget impact, and sensitivity analysis. Rank options by your constraints.
Deliver
Deliver Decision Brief + What-Changes-My-Mind. Your COO has a defensible recommendation. Your board has an audit trail.
No EHR integration needed to start. We work with public data + your existing reports.
Expand data connections only when (and if) it adds decision value.
But when you connect your EHR, this is what happens.
After the first Decision Packet: monthly retainer.
We update the model as conditions change — policy shifts, seasonal demand, staffing changes. Your decision brief stays current.
What we see before the first meeting.
Built from public data alone — no internal files, no EHR access, no NDA. Hover nodes to explore relationships. Yellow edges reveal decision leverage points.
WHERE DECISIONS HIDE
Each node is a public data point. Most hospitals already track them — separately, in different departments, on different dashboards. The yellow edges are what matters: 670 open positions don't just mean “we're hiring” — they predict ED wait time pressure. A 3.6% margin doesn't just mean “tight” — it constrains every staffing decision for the next two years. Entity Flow connects these into auditable decisions, not more dashboards.
All data from publicly available sources. No internal hospital data used. · Entity Flow by Doogooda
A methodology, not a black box.
Entity Flow is built on Decision Science — a structured approach to making defensible choices under uncertainty.
This is what a Decision Brief looks like.
Already deployed. Already working.
4 years of hospital and government deployment. Not a prototype.
Built by people who've done this before.
Lina Song
CEO & Founder
- •Harvard PhD (Health Policy & Decision Science)
- •Trained in the program founded by Milton Weinstein (founder of cost-effectiveness analysis)
- •PhD committee: Richard Zeckhauser (decision theory), Joseph Newhouse (health economics)
- •NIH/AHRQ-funded Principal Investigator — US Medicare claims data
- •Taught machine learning at Harvard | MS Statistics (Yale) | BS Applied Math (Caltech)
- •Former faculty at UCL and Cornell
- •Clinical experience: Moorfields Eye Hospital NHS, MGH
- •AcademyHealth Best Paper Award · Published in Management Science
CTO
- •Full-stack CTO · 10+ years shipping production systems at 50M+ user scale
- •Led platform reliability and infrastructure for one of Asia's largest consumer tech companies
- •DevOps/SRE · Cloud architecture · Security & compliance · AI/ML
- •At Entity: translates complex healthcare operational data into secure, auditable, enterprise-grade decision systems
One built the decision science. The other builds the systems that run it.
Research-grade methodology. Production-grade engineering.
See Entity Flow in action.
30-minute demo. Bring your hardest operational question.
We'll show you what a decision engine can do with it.