The Power of Optimization and AI in Healthcare
This article was adapted from The Analytics Edge In Healthcare written by Dimitris Bertsimas, Ph.D., Agni Orfanoudaki, Ph.D., Holly Wiberg, Ph.D.
Healthcare is one of the most complex and high-stakes systems in the world. Every day, leaders must decide how to allocate limited resources, schedule specialized staff, and deliver life-saving care – all while ensuring fairness and equity. Traditionally, these decisions have relied on experience, intuition, and spreadsheets. But as healthcare grows in complexity, so does the need for data-driven, transparent, and fair decision-making.
That’s where optimization and artificial intelligence (AI) come in.
What Optimization Really Means in Healthcare
An optimization problem is about finding the best possible solution when there are competing goals and real-world constraints. In healthcare, this could mean:
Maximizing the use of expensive resources like operating rooms.
Balancing physician schedules to avoid burnout while maintaining revenue.
Distributing limited vaccines or treatments to reduce population-level risk.
Selecting a drug regimen that’s effective across multiple conditions while minimizing side effects.
Optimization provides a structured, mathematical way to make complex decisions. It defines:
Decision Variables: the choices you can make (e.g., how many nurses to assign per shift).
Objectives: what to maximize or minimize (e.g., patient access, cost, fairness).
Constraints: the rules you must respect (e.g., staffing limits, safety regulations).
Modern optimization algorithms, once limited to military logistics, now solve massive, real-world healthcare problems in minutes. And when paired with AI, the possibilities expand even further.
AI + Optimization: Smarter and Fairer Decision Making
AI and optimization work hand-in-hand:
AI predicts - such as forecasting patient demand or surgery length.
Optimization prescribes - using predictions to plan resources and schedules.
Together, they create a closed-loop system that not only anticipates the future but also makes the best possible plan. This is especially critical when fairness matters. AI can unintentionally replicate historical bias if unchecked, but optimization can embed fairness directly. For example, balancing workloads, protecting vulnerable groups, and making decisions transparent.
Case Study: Making Physician Scheduling Fair and Efficient
This approach isn’t just theoretical. Dynamic Ideas, the parent company of H2O, helped Brigham and Women’s Hospital (BWH) in Boston redesign how it schedules physicians in its Division of Angiography and Interventional Radiology.
As the hospital expanded to multiple campuses, scheduling became far more complicated. Leaders needed to:
Balance high-and low-revenue assignments fairly among doctors.
Distribute tough shifts like nights and weekends without overburdening anyone.
Respect personal preferences and time-off requests.
Avoid burnout while keeping the department financially healthy.
Historically, this process was done by hand which is time-consuming and subjective.
By applying a mixed integer optimization model with fairness objectives, Dynamic Ideas created an automated scheduling system that:
Increased departmental revenue by over 8%.
Reduced pay inequities and distributed undesirable shifts more fairly.
Incorporated physician preferences transparently.
Produced schedules in minutes instead of days.
The result: a fairer, more efficient schedule that staff trusted and embraced.
Why Fairness in AI and Optimization Matters
In healthcare, unfair or opaque decision-making isn’t just a technical issue, it’s an ethical one. Algorithms can unintentionally reinforce inequities if not designed with fairness in mind. By explicitly including fairness constraints, organizations can:
Protect vulnerable groups or individuals.
Distribute workload more equitably.
Build transparency and trust into decision-making.
Fair optimization turns AI from a “black box” into a clear, accountable tool that leaders and staff can understand and trust.
Building Smarter, Fairer Healthcare Systems
Healthcare leaders face mounting pressure to deliver high-quality, equitable care with limited resources. Optimization and fair AI offer a disciplined, future-ready way forward. They help organizations:
Reveal trade-offs clearly so stakeholders can make informed, ethical choices.
Move debates from gut feeling to measurable outcomes.
Accelerate decision-making and process redesign.
Build trust by making fairness explicit.
The result is decision-making that’s smarter, fairer, and better aligned with both patient needs and operational realities. From operating room schedules to public health planning and beyond.
Final Thoughts
The tools to achieve this are here today. With modern optimization solvers and AI models, even the most complex healthcare systems can turn data into fair, transparent, and impactful decisions.
Healthcare will always involve tough choices, but with optimization and AI, those choices can be made faster, fairer, and with greater confidence, improving care for patients and working conditions for the people who serve them.