Personalized Medicine Faces 4 Key Challenges in the US Marketplace


While personalized medicine has the potential to detect the onset of disease early and preempt disease progression, this promising field of medicine faces unique challenges in the US healthcare marketplace.

While personalized medicine has the potential to detect the onset of disease early and preempt disease progression, this promising field of medicine faces unique challenges in the US healthcare marketplace. A newly released report from the Personalized Medicine Coalition highlights 4 key challenges to the widespread adoption of a personalized approach to patient care.

Regulatory Policy

The landscape for the regulation of personalized medicine is still in its infancy. Despite the FDA’s strides in producing draft guidance and other documents that concern a regulatory pathway for personalized medicine diagnostics (such as the 2017 “Discussion Paper on Laboratory Developed Tests” and the 2016 draft guidance “Use of Public Human Genetic Variant Databases to Support Clinical Validity for Next Generation Sequencing-Based In Vitro Diagnostics”), uncertainty remains about the regulatory landscape for laboratory-developed tests (LDTs). According to the report, “Although FDA has historically claimed jurisdiction to regulate LDTs, the agency has also historically refrained from actively regulating these tests, under a policy it describes as ‘enforcement discretion,’” and the regulatory uncertainty surrounding these tests has discouraged industry investment in new diagnostics.

Regulatory oversight for next-generation sequencing technology is also still developing; in 2016, the FDA issued 2 draft guidance documents describing potential processes for analytic standards development and FDA-recognized public genome database development, but the agency has yet to finalize these guidance documents. Similarly, companion diagnostics (in vitro diagnostics or imaging tools to ensure safe use of a therapeutic product) still lack a clear regulatory pathway.

Coverage and Payment Policy

Reimbursement for personalized medicine products is just as key, say the report’s authors, as clarity in regulation. In the current milieu, payers seek evidence of therapeutics’ clinical and economic impacts, though there is not yet a set approach for obtaining and disseminating these data.

Furthermore, value assessment frameworks used by many payers may not fully account for the heterogeneity of patient response to personalized medicine, CMS’ reimbursement policies for diagnostics have a downward pressure on the use of these tests, and alternative payment models could limit physician flexibility in tailoring the best care to an individual patient’s genetics and other factors.

Clinical Adoption

According to the report, health systems have been slow to adopt personalized medicine in clinical practice; most healthcare organizations do not yet have formal plans to use genomics or advanced data for personalized patient care.

Education may be the greatest hurdle to adoption, and the report points to groups such as the Genomic Medicine Institute at Cleveland Clinic and the Mayo Clinic’s Center for Individualized Medicine as paving the way for greater clinician education, though programs offered by these institutions do not as yet reach a large proportion of providers.

Patients, too, need education on personalized therapies, and must be assured that their genetic data will not be used in ways that would harm them (ie, discrimination, job loss, or loss of health insurance coverage). Best practices must be established for the collection and distribution of these data that will ensure that they are only used for effective clinical care.

Payers will require education to facilitate a better understanding of the evidence needed for positive coverage determinations, providers will need effective structures for managing data, and the traditional fee-for-service model may need to be adapted to incorporate new technologies and ensure patient access to care.

Health Information Technology

Health systems must develop and implement data management platforms that can capture, interpret, and share complex and accurate data. Integration of these platforms at the point of care is a challenge, though the report’s authors point to the widespread use of electronic health records as a step forward. Mobile technology—including wearable and environmental sensors—may also help to collect key data that can one day be used in a so-called “learning health care system,” a seamless system that would systematically capture, analyze, and share findings from every clinical interaction and research milestone.

“Much work remains to be done in building the infrastructure for personalized medicine,” the report’s authors write, “but the resources we invest in completing the task now will enable us to realize the health and economic benefits of matching the right treatment or prevention to each and every patient.”

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