From Medicine to Precision Medicine, Prevention and Precision Prevention

The Role of Biomedical Informatics in Precision Medicine

The history of medicine has been shaped by groundbreaking innovations. Among them, precision medicine represents one of its most transformative shifts. By offering personalized care based on genetic, environmental, and lifestyle factors, precision medicine has ushered in a new era of healthcare. This paradigm shift has been made possible by technological advancements, such as the sequencing of the human genome, which identifies disease-associated mutations, and the integration of omics data (e.g., transcriptomics, proteomics, and metabolomics), enabling a holistic understanding of disease mechanisms. Additionally, Electronic Health Records (EHRs) provide longitudinal patient data, helping to uncover correlations between genetic predispositions and clinical outcomes. Advanced informatics tools, powered by Artificial Intelligence (AI) and Machine Learning (ML), further bolster precision medicine by analyzing large datasets, identifying biomarkers, predicting disease progression, and optimizing treatment pathways.

Yet, medicine has been historically rooted in a curative framework. While transformative, precision medicine has remained focused on the management of diseases after symptoms had manifested. Recently, however, the medical community has pivoted toward prevention—not merely as a means to cure diseases more effectively but to delay or even eliminate their occurrence. Vaccination programs, public health campaigns, and lifestyle interventions are testaments to the power of preventive strategies. However, traditional prevention strategies have often relied on broad, population-level approaches, lacking the granularity that precision medicine offers. To achieve truly impactful prevention, we must now harness the tools of biomedical informatics to develop precision prevention—a paradigm that applies personalized strategies to mitigate risks before disease onset.

The Role of Biomedical Informatics in Precision Prevention

Biomedical informatics is poised to play a central role in the building of precision prevention. By leveraging big data, AI, and predictive algorithms, healthcare providers can stratify individuals into risk groups for conditions like cardiovascular disease or diabetes. Tools like polygenic risk scores (PRS), which aggregate multiple genetic variants, enable a detailed estimation of an individual's susceptibility to specific conditions. AI-driven models trained on diverse datasets can recommend tailored preventive measures, from specific dietary changes to precise dosing of prophylactic medications. Advancements in wearable technologies also contribute to this shift, enabling continuous monitoring of health indicators such as heart rate, glucose levels, and physical activity. Biomedical informatics systems can process this data in real-time to detect deviations from baseline, signaling early signs of disease. Moreover, by incorporating psychographic and behavioral data, informatics tools can predict how individuals are likely to respond to preventive strategies.

Challenges on the Path to Precision Prevention

Despite its promise, the journey toward precision prevention is not without challenges. Integrating diverse datasets from EHRs, wearables, and omics requires overcoming issues of interoperability, data privacy, and potential biases in AI algorithms. Yet, the opportunities far outweigh the obstacles. Precision prevention has the potential to revolutionize public health by enabling more efficient resource allocation and reducing the overall burden of disease. Beyond individual benefits, it could address systemic inequities by identifying underserved populations at higher risk and tailoring interventions accordingly.

Thus, we could consider that the necessary technologies to advance prevention and precision prevention are in place, and if not, this is on the go. We could also consider that healthcare systems are ready to prioritize prevention over treatment and they would realign financial incentives to reward prevention and promote the active engagement of clinicians in this new paradigm. However, providing an attractive environment, if necessary, is not sufficient to generate overall commitment. The ultimate success of precision prevention hinges on the active participation of individuals who are not yet patients. Prevention is not only a medical or technological challenge—it is also a sociological one. While advances in healthcare technology, genomics, and medical informatics have opened unprecedented opportunities for precision prevention, the ultimate success of such initiatives hinges on individual and collective behavior. Encouraging people to adopt preventive measures requires more than access to tools or medical recommendations; it demands engagement strategies rooted in health education, social influence (harnessing the power of peer influence, community leaders, and role models to encourage healthy behaviors), and incentives (using gamification, and subtle environmental shifts to make healthier choices more appealing and accessible).

As François Grémy (1929-2014), a French physician often referred to as one of the “fathers of medical informatics”, used to say, to fully leverage the potential of biomedical advancements, we need more than just hardware (technological tools) and software (algorithms and programs), we also need “peopleware”—the human willingness and engagement essential to driving this transformation.

Conclusion: Precision Prevention, charting the Course to a Healthier Future

Medicine has historically been reactive, but with the tools of modern informatics, it can become proactive. Precision prevention is not merely an extension of precision medicine—it represents the next great leap forward. By empowering individuals and fostering systemic changes, we can transition from a healthcare model focused on curing illness to one centered on sustaining health. This transformation holds the promise of a healthier and more equitable future for all.

No conflict of interest has been declared by the author(s).

Publication History

Article published online:
08 April 2025