These failures are not random. They reflect structural weaknesses across the prevention pipeline. Life-course exposure is often captured inconsistently at intake and relegated to unstructured clinical history that cannot be used systematically. Prevention guidelines rely heavily on age and current residence, offering little guidance on when early-life exposure or prior health system context should modify action. And because migration-informed risk remains implicit, health systems lack clear metrics to assess whether prevention strategies align with cumulative exposure rather than administrative convenience. While downstream treatment decisions grow increasingly individualized, upstream prevention continues to operate on coarse population assumptions, undermining the internal logic of precision medicine itself.

Toward Explicit, Migration-Informed Prevention

A migration-informed approach offers a pragmatic extension of precision medicine by shifting attention upstream from treatment optimization to prevention targeting. Rather than competing with genetic or molecular tools, it complements them by incorporating a dimension of risk that is often decisive and comparatively inexpensive to assess. What this approach requires is not disease-specific reinvention or rigid thresholds, but a clearer prevention logic. First, prevention frameworks must explicitly recognize early-life geography and prior health system context as legitimate, actionable components of risk assessment, rather than treating them as informal background information. Disease-specific guidelines can then operationalize this principle in ways that reflect differences in latency, exposure mechanisms, and preventive effectiveness.

Second, making migration-informed risk explicit improves accountability and evaluability of prevention strategies. When exposure histories remain implicit, prevention performance cannot be meaningfully assessed. Explicit incorporation allows health systems to evaluate whether prevention strategies align with epidemiologic risk shaped by life-course exposure, enabling systematic learning and refinement over time. Finally, for health systems, this approach offers a practical opportunity to improve targeting without expanding cost or complexity. Much of the relevant information already exists within clinical and administrative infrastructure. What is missing is intentional integration into prevention logic and policy—treating exposure history not as background context, but as prevention-relevant data.

Emerging analytic tools, including machine-learning–based risk stratification, could facilitate the operationalization of migration-informed prevention without introducing new clinical burdens. For example, routinely collected data elements—such as country of birth, age at migration, language preference, and prior health system contact—could be integrated into automated risk indices to flag individuals whose cumulative exposure history suggests elevated risk despite meeting conventional “average-risk” criteria. In this context, artificial intelligence does not redefine risk; it helps health systems consistently identify it at scale, transforming already documented but rarely actionable exposure histories into prevention-relevant signals.

Conclusion

Precision medicine has transformed diagnosis and treatment, yet prevention often remains anchored in assumptions that no longer reflect how risk is acquired across the life-course. Population mobility and early-life exposure shape disease susceptibility in durable ways, yet these realities remain largely implicit in prevention policy—with tangible consequences in conditions such as gastric cancer and chronic hepatitis B, in which failure to operationalize migration-informed prevention has contributed to delayed diagnosis and preventable morbidity despite decades of epidemiologic evidence. A migration- informed approach does not introduce new technologies or argue for narrow exceptions; rather, it makes explicit a dimension of risk that many clinicians already recognize but that health systems rarely operationalize. As medicine continues to invest in increasingly sophisticated downstream precision, comparable intentionality is needed upstream. Making life-course exposure visible within prevention policy is a necessary step toward ensuring that precision medicine fulfills its promise—not only in treatment, but also in prevention.