How AI and Optimization Shaped a Landmark U.S. Transplant Policy
This article was adapted from The Analytics Edge In Healthcare written by Dimitris Bertsimas, Ph.D., Agni Orfanoudaki, Ph.D., Holly Wiberg, Ph.D.
For decades, U.S. organ allocation policy relied on slow, simulation-heavy debate. Policymakers guessed how much weight to give competing priorities like saving the sickest patients versus minimizing organ transport. Then they ran expensive simulations, reviewed results, and repeated for years.
Dynamic Ideas, the parent company of H2O, helped break that cycle. Using machine learning and mathematical optimization, the company partnered with the Organ Procurement & Transplantation Network (OPTN) Lung Transplantation Committee to design the nation’s first Continuous Distribution policy for lung allocation – approved in December 2021 and set to govern how lungs are allocated across the U.S. going forward.
From Trial-and-Error to Outcome-Driven Design
This framework flips the traditional approach:
Start with the outcomes you want – fewer waitlist deaths, equitable access, manageable logistics.
Use AI to predict outcomes for any proposed scoring rule in real time.
Apply optimization to find the exact policy weights that achieve those outcomes.
This shift means leaders debate values, not formulas, putting the focus on saving lives and promoting fairness rather than technical minutiae.
Impact at National Scale
The analysis identified two key inflection points:
Placement Efficiency:
A 10% weight minimized transport burden without sacrificing survival. This was validated across multiple transport metrics and by independent Scientific Registry of Transplant Recipients (SRTR) simulation on updated 2018–2019 data.Pediatric Access:
A 15–20% weight was necessary to preserve access for children under the new system.
These findings were adopted into OPTN’s official policy, which is projected to:
Reduce lung waitlist deaths by 21% or ≈118 lives saved over two years with SRTR modeling suggesting up to ≈ 40% reduction or ≈175 lives saved.
Make access more equitable by eliminating rigid geographic boundaries.
Why This Matters for Healthcare Leaders
This isn’t just about organ transplants. It’s about a new model for making high-stakes, ethically complex healthcare decisions faster and with greater clarity.
Speed: Policy cycles that once took a decade can be cut dramatically.
Transparency: Stakeholders see tradeoffs clearly and can align on shared values.
Impact: Data-driven, ethically informed decisions save lives and resources.
From resource allocation to value-based care reimbursement, AI-powered optimization gives health systems the tools to act decisively – without sacrificing fairness or rigor.
The Takeaway
Healthcare can no longer afford slow, opaque policy development.
This work reveals how AI and optimization can help create a healthcare system that is better for patients, fairer, and faster.
If you would like to read the full case study, click here (link to extended article below)