What Does Individualized Healthcare Mean?

First generalization, then customization.

Scientific research begins with generalization—comparing two or more groups that differ in a specific variable such as a treatment, risk factor, or test result. These groups are made as similar as possible so that any outcome can be confidently attributed to the variable under study. Individuals with other characteristics that might affect the results are typically excluded to maintain statistical clarity and homogeneity. As a result, findings reflect the average effect observed in controlled populations.

However, real-world patients rarely match the narrow profiles of clinical trial participants. They often have coexisting conditions, different lifestyles, or concurrent medications. When applying these general findings to actual patients, clinicians and guideline committees must interpret how an effective intervention in a homogeneous group can be safely and meaningfully applied to a complex individual. This process—essentially a form of clinical reverse engineering—bridges the gap between standardized science and personalized care.

Here is where the Digital Health Twin becomes crucial. By modeling a patient’s complete health profile digitally, it enables the customization of clinical knowledge to the individual. It brings together average-based scientific evidence and patient-specific data to support decisions that are both evidence-based and person-centered.

In short, science offers the averages. Individualized care—especially through a digital health twin—applies them intelligently to the person in front of us.