Medical education has a blind spot.
We spend years teaching students to identify diseases. We drill them on symptoms, pathophysiology, and treatment algorithms. By the time they reach clinical rotations, most can rattle off the differential diagnosis for chest pain or abdominal discomfort without hesitation.
But ask a third-year student how much a CT scan costs, or whether an MRI is worth the added expense over an ultrasound, and you’ll often get a blank stare.
This gap matters more than ever. Health care spending in the United States exceeds $4 trillion annually, and a significant portion of that comes from diagnostic testing. Studies suggest that up to 30 percent of medical tests ordered may be unnecessary, tests that add cost, delay care, and sometimes lead patients down rabbit holes of false positives and follow-up procedures.
The problem isn’t that students don’t care about cost. It’s that they’re never forced to think about it.
The friction-free path to diagnosis
In a traditional medical education, the path to diagnosis is frictionless. Need a test? Order it. The simulated patient doesn’t complain about their insurance. The attending doesn’t ask you to justify the expense. The feedback focuses on whether you got the right answer, not whether you got there efficiently.
This creates a dangerous habit. Students learn to order defensively: More tests mean fewer missed diagnoses, and fewer missed diagnoses mean better grades. The instinct to “rule everything out” becomes muscle memory before they ever see a real bill.
Then residency hits, and suddenly cost matters. Attendings push back on unnecessary imaging. Hospital administrators track resource utilization. Patients ask why they’re being charged thousands for tests that didn’t change their treatment. The habits formed in medical school become liabilities.
USMLE Step 3 has started to address this, with questions increasingly focused on “next best step” management rather than pure diagnosis. But by the time students reach Step 3, they’ve already spent years learning the wrong instincts.
Introducing constraints
The fix isn’t complicated: We need to introduce resource constraints earlier in medical education.
Simulation tools can help. When students work through clinical cases with a limited budget for testing (forcing them to prioritize which investigations actually matter) they develop different instincts. They learn to think in probabilities. They start asking “will this test change my management?” before ordering it. They experience the frustration of running out of resources and having to make decisions with incomplete information, just like they will in real practice.
This isn’t about teaching students to ration care. It’s about teaching them to think critically about the value of each diagnostic step. The best clinicians don’t order every test available; they order the right tests, in the right sequence, for the right patients.
Medical schools are beginning to incorporate high-value care curricula, but progress is slow. Until cost-conscious reasoning becomes as fundamental as anatomy or pharmacology, we’ll continue producing graduates who are excellent diagnosticians but poor stewards of health care resources.
The students who practice with constraints will be better prepared than those who don’t. The question is whether we’ll build those constraints into medical education, or leave students to learn the hard way, one unnecessary CT scan at a time.
Helena Kaso is a software developer and independent creator focused on improving clinical reasoning education through technology. She is the founder of Diagnostic Studios and the creator of MedDiagnosis, a clinical reasoning simulator designed to teach diagnostic decision-making under real-world constraints such as limited time and limited budget. The app is available on both iOS and Android.
Based in Tirana, Albania, Kaso built MedDiagnosis to address a persistent gap in medical education. While trainees are taught what diagnoses to reach, they rarely practice how to choose efficiently among competing tests and pathways. Her work emphasizes cost-aware, constraint-based clinical reasoning that reflects real clinical environments.
