Boris Berat is an operator, investor, and advisor. Co-founder & CTPO of Carna Health.getty​Cardio-kidney-metabolic disease is one of the largest clinical burdens that modern healthcare faces today.​Globally, hundreds of millions of people live with one or more metabolic conditions. Diabetes affects more than 800 million individuals worldwide. Chronic kidney disease alone impacts 850 million people, many of whom remain undiagnosed until late stages. Cardiovascular disease remains the leading cause of death globally, responsible for over 20 million deaths each year, with metabolic risk factors at its core. In 2021, cardio-kidney-metabolic risk factors contributed to an estimated 13.6 million cardiovascular deaths, accounting for the majority of all cardiovascular mortality. Elevated blood pressure alone is responsible for about one-quarter of global noncommunicable disease deaths, making metabolic risk factors a major contributor to preventable illness and mortality worldwide​This burden is not confined to a single region or healthcare system. It spans high-income and low-income countries alike, urban centers, rural communities, advanced hospital networks and resource-constrained primary care settings. Patients are distributed across continents, languages, cultures and care models, often moving between systems that were never designed to work together. Clinical software in this space does not operate in a stable environment. It operates in a world that changes constantly.Guidelines change. Data quality varies. Infrastructure differs by country, by hospital and sometimes by department. The same clinical rule may be executed in a modern hospital system in Europe and in a primary care setting with limited digital support elsewhere.When we started building Carna, this reality shaped our engineering decisions early. The core challenge was not user scale. It was behavioral consistency under change.As a CTO, the central question was simple: How do we make sure the system keeps doing the right thing as it grows more complex?In healthcare, small ambiguities do not stay small. A slightly unclear rule. A threshold change without full context. An update that behaves differently across environments. These issues do not always cause visible failures. They surface slowly, through thousands of individual clinical decisions.This is where our engineering approach became very deliberate.We do not treat tests as a safety net added at the end. We treat them as the place where behavior is defined. Acceptance test-driven development, in this context, is not about speed or style. It is about forcing clarity before code exists.Before implementation begins, each rule must be explicitly defined through clear, verifiable examples. All inputs and outputs should be specified using concrete scenarios understood by both technical and nontechnical stakeholders. Edge cases need to be identified and named up front. Every example must include an expected outcome written in a form the system can automatically verify, typically as an acceptance test. Only after these examples are agreed upon, documented and automated should development proceed with writing the code.This process is often uncomfortable. It exposes uncertainty early, when it is still cheap to fix. That is precisely why it works.In many software systems, behavior is discovered after deployment. In clinical systems, that approach is not acceptable. Discovery must happen before scale.​In healthcare software, failures may not appear as a single event. They would likely appear as many small, silent errors, such as a rule applied inconsistently, a regression that shifts risk scores slightly or a deployment that behaves differently across sites.These failures are harder to detect and harder to undo. There is no clean rollback for a clinical decision that already influenced care.This is why scalability in healthcare is not just about performance. It is about preserving correctness as complexity grows.Acceptance tests play a central role in this approach, not as a procedural checkbox and not as something added after the fact. They serve as living specifications that define the intended behavior of the system. By expressing behavior through concrete, executable examples, acceptance tests allow the system to evolve while keeping its meaning and intent stable.As clinical-grade platforms expands across regions, infrastructures and workflows, this discipline becomes more important, not less. Without it, speed creates risk. With it, change remains controlled.In environments where software influences real clinical decisions, rigor is not optional. It is the condition that makes progress safe.Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?