At the height of the COVID-19 surge, many hospital leaders faced moments they will never forget. Beds were nearly full. Staff were exhausted. Supplies that once felt routine suddenly became scarce. In those moments, decisions had to be made quickly, often based on experience, instinct, and whatever information was available at the time.
We like to believe that good leadership means trusting our gut. In clinical medicine, intuition matters. Years of training and pattern recognition save lives every day. But when decisions shift from individual patients to entire systems (staffing, logistics, safety, resource allocation) intuition begins to break down. The pandemic exposed an uncomfortable truth: Modern health care systems are too complex to be managed by instinct alone. Health care leaders were not unprepared or incompetent. They were overwhelmed by complexity. Too many variables. Too many moving parts. Too many ripple effects no single human mind can track in real time.
This is where many well-intentioned decisions failed, not because they were wrong in isolation, but because they triggered unintended consequences elsewhere in the system. We see this every day in operations. Cutting costs in one area can quietly increase risk in another. Staffing just “lean enough” may work under normal conditions, but during a surge it can rapidly turn into burnout, errors, and system collapse. Stockpiling fewer supplies may look efficient on paper, until a disruption exposes how fragile the system really is. This is not a moral failure. It is a cognitive one.
Human intuition evolved for direct, linear problems, not for managing networks of interdependent decisions with delayed effects. When systems grow complex, intuition becomes unreliable, no matter how experienced the leader. This is where operations research becomes relevant, not as a technical discipline, but as a decision-support mindset. At its core, operations research asks different questions than intuition does. The first question is not, “What is the best option?” but rather, “What is even possible?” If a plan cannot be executed under real-world constraints, debates about optimization are meaningless.
Only after feasibility is established does the harder question emerge: What trade-offs are we willing to accept? Health care decisions almost always involve competing goals: Lower cost versus higher safety. Efficiency versus resilience. Speed versus redundancy. There is rarely a single “right” answer. Decision models do not remove these trade-offs. They make them visible.
For example, a staffing model might reveal that saving a small percentage in labor costs significantly increases the risk of adverse events during demand surges. That risk was always there, but invisible. The model does not dictate what to choose. It forces leaders to confront the true price of their decisions. This transparency changes the nature of leadership conversations. Instead of arguing based on anecdotes or hierarchy, teams can discuss trade-offs using shared evidence. Politics do not disappear, but they become informed.
Perhaps the most important lesson from the pandemic is that systems optimized for normal conditions often fail under stress. What looks efficient in calm times can become a bottleneck in crisis. When volume spikes or resources disappear, systems without slack collapse first. Resilience is often mislabeled as inefficiency. In reality, redundancy, flexibility, and surge capacity are forms of insurance. They protect both patients and clinicians when reality deviates from the plan, as it inevitably does.
Operations research does not replace human judgment. It supports it. It allows clinicians and leaders to focus on care and ethics, rather than guessing how fragile their system really is. The real danger is not complexity itself. The danger is pretending complexity does not exist. Health care systems are already complex. Ignoring that fact does not make decisions simpler; it makes failures more likely. In medicine, clarity is not a luxury. It is a responsibility.
Gerald Kuo, a doctoral student in the Graduate Institute of Business Administration at Fu Jen Catholic University in Taiwan, specializes in health care management, long-term care systems, AI governance in clinical and social care settings, and elder care policy. He is affiliated with the Home Health Care Charity Association and maintains a professional presence on Facebook, where he shares updates on research and community work. Kuo helps operate a day-care center for older adults, working closely with families, nurses, and community physicians. His research and practical efforts focus on reducing administrative strain on clinicians, strengthening continuity and quality of elder care, and developing sustainable service models through data, technology, and cross-disciplinary collaboration. He is particularly interested in how emerging AI tools can support aging clinical workforces, enhance care delivery, and build greater trust between health systems and the public.
