Can a Digital Health Twin Simplify Oncology Guidelines?

Clinical guidelines in oncology are essential tools—but implementing them in real-world settings often proves challenging, especially in resource-limited environments. Physicians must navigate complex patient profiles, institutional protocols, and limited access to diagnostics or medications. The concept of a Digital Health Twin offers a powerful solution: a real-time, data-driven model of an individual patient that integrates their conditions, treatments, and risk factors into one dynamic system. By simulating how a patient’s unique profile responds to specific interventions, a Digital Health Twin can filter and personalize guideline recommendations, making them more actionable. This not only improves alignment with evidence-based care but also reduces reliance on memory or one-size-fits-all approaches. As oncology grows more complex—with multimorbidity, genetic markers, and targeted therapies—guidelines must evolve to be both adaptive and patient-specific. A Digital Health Twin does just that—bridging the gap between clinical standards and the nuanced reality of individual patients. In real life settings especially, this could be the key to transforming good guidelines into practical, personalized care.