10 Years of Personalizing Medicine: How the Incorporation of Genomic Information Is Changing Practice and Policy

Amalia M Issa


Personalized Medicine. 2015;12(1):1-3. 

In This Article

The Policy Landscape of Personalized Medicine

Economic & Regulatory Considerations

The value of various personalized genomic technologies has been investigated using health economic methods. The cost–effectiveness of different genomic diagnostics and targeted therapeutics has been studied, and in some cases found to be cost effective[7] and less so in others.[8] This area of research is still relatively young and economic analyses of the use of personalized medicine interventions in clinical settings are at this time not entirely conclusive.[8] Nevertheless, this pharmacoeconomic research is valuable for payers and other stakeholders for policy decisions.

Internationally, regulatory agencies including the US FDA, EMA and Health Canada have made progress in regulatory statements and in some cases legislative actions, that have favored personalized medicine research and clinical developments. However, regulatory activity is still in a formative state throughout the world, even in countries where a lot of personalized medicine research and clinical developments have been made. Indeed, the need for appropriate regulatory guidance and international harmonization is an important emerging policy challenge that is necessary to foster innovation and the adoption of personalized medicine technologies into health systems.

Big Data, Information Technology & Personalized Medicine Policy

Continuing computational advances and information technology (IT) capabilities have spawned the new era of 'big data'. Although there are varying definitions of the term 'big data', it is generally understood as voluminous amount of data that exceeds the capacity of readily available bioinformatic and computational tools. Although computational technologies, and particularly large databases and electronic medical records (EMR), have supported and underpinned many of the developments in personalized genomic medicine research, ongoing advances will depend on developing and implementing a stable and reliable infrastructure.

Going forward, what is needed for personalized medicine to become more fully incorporated into clinical settings and health systems is an infrastructure that allows for large multimodal data sets to accurately model biological complexity and integrate it with key clinical and patient-centered components such as phenotypic features, patient's history and health status, as well as decision-support capacity, in order to derive effective decision algorithms that are necessary to allow more precise personalized and predictive outcomes for patients. This requires serious investment at national and international levels in IT infrastructure across health systems for reliable and accessible (to healthcare providers and patients) inpatient and ambulatory care that goes well beyond the mixed bag of EMR systems currently in place today which, for the most part, do not allow for the decision support capabilities required to support and facilitate personalized medicine.

We[9,10] and others[11] have been investigating patient and provider decision-making related to personalized medicine over the past decade. However, this area of research is still in its infancy and clearly more work needs to be done to better understand patient and provider-decision-making and develop effective clinical decision support tools and strategies.

More recently, renewed interest in `patient-centered care'[12] underscores the importance of understanding decisions surrounding personalized genomic medicine and personalized medicine applications. We need to continue to conduct research to better understand the role of patient and provider decisions surrounding personalized genomic medicine, probabilistic risk understanding and knowledge and the uncertainty that often underlies such decisions in order to better develop appropriate and effective decision aids and support tools that enable the delivery of information to facilitate good decision-making about personalized genomic medicine and to allow for positive health outcomes.

In addition to the issues discussed above, the policy landscape for personalized medicine has been inundated with ethical, legal and social issues (ELSI), and progress has been made to recognize ELSI considerations from the earliest studies[13,14] to an increasingly broader array of research.[15] Some policy-relevant progress has been made such as the passage of the Genetic Information Non-Discrimination Act (GINA) in the USA in 2008, legislation designed to protect privacy and mitigate discrimination based on use of genetic information.[16] More recently, a global initiative focused on facilitating ELSI research that recognizes the need for harmonization has been launched.[17] Issues such as the return of research results to human subjects, and newer ELSI concerns arising from WGS and future advances in personalized genomic medicine will continue to present with global policy challenges.

In order to fully realize the adoption and implementation of clinical personalized genomic medicine in health systems globally, it is important to achieve an integration of basic molecular science, technological advances (including in bioinformatics and clinical decision support infrastructure), and clinical science and to develop appropriate and effective policies. In other words, to effectively translate the advances that are now beginning to lead to the practice of personalized medicine will require systems-level innovations in order for personalized medicine to contribute substantially to patient care and health outcomes. It is time to make a collaborative and global concerted effort to accelerate the adoption and effective implementation of personalized medicine within health systems.