Press

Lina Song is the Founder & CEO of Doogooda, building auditable decision systems that turn operational data into defensible actions for hospitals and public institutions. Her company operates Entity Flow (US hospital operations) and Entity Value (Korean institutional clients). She holds a Harvard PhD in Decision Science and Health Policy and previously held a faculty appointment in operations and technology.

Lina Song is the Founder & CEO of Doogooda, where she builds auditable decision systems that translate data into defensible actions under real constraints—capacity, staffing, budgets, and policy. Doogooda operates Entity Flow for US hospital operations and Entity Value for Korean government and institutional clients. Her approach combines causal reasoning, scenario simulation, and optimization as decision infrastructure that can be explained, stress-tested, and audited. A Harvard PhD in Decision Science and Health Policy, she previously held an academic appointment in operations and technology. Lina speaks and writes about decision-making under uncertainty, accountable AI in deployment, and why operations is fundamentally a governance problem.

Lina Song is the Founder & CEO of Doogooda, a decision-intelligence company that builds auditable systems helping institutions make defensible choices under uncertainty. Rather than treating AI as prediction, she builds decision infrastructure: explicit assumptions, scenario simulation, constraint-aware optimization, and governance-ready outputs that teams can justify, execute, and audit. Doogooda operates two products—Entity Flow for US hospital operations and Entity Value for Korean government and institutional clients—both grounded in real operational constraints: capacity, staffing, budgets, and policy trade-offs. Currently based in Madison, WI through gener8tor's gBETA Healthcare accelerator, she is working directly with American hospitals on operational decision-making through Entity Flow, bridging what she built in Korea's universal healthcare system with the US market. A Harvard PhD in Decision Science and Health Policy with previous faculty experience in operations and technology, Lina speaks and writes about the practical meaning of "accountable AI," the politics of AI infrastructure, and why many operational problems are ultimately governance problems. Her core themes include trade-offs and incentives, decision quality under uncertainty, and designing systems that remain credible when stakes are high and accountability is non-negotiable.

Decision Architecture for Uncertain Times

A framework for structuring organizational decisions when predictions fail. Covers trade-off mapping, assumption documentation, and governance design.

Auditable AI: Beyond Explainability

What institutions actually need from AI systems, and why current approaches fall short. A practical framework for accountability.