A 3-Part Strategy for Overcoming Data Overload in Precision Medicine

A recent article by Todd Winey, Precision Medicine in the Lab: The Intersection of Informatics and Pathology, does a great job of describing the challenges pathologists face in delivering precise laboratory decisions when data that can influence their decision—such as lifestyle and demographic data—is silo-ed. However, one of the biggest roadblocks to realizing precision medicine’s full potential is less about data silos than data overload.

The concept of data overload is at the heart of many challenges physicians and other healthcare professionals face in the emerging world of precision medicine, where new genetic testing products are released at a rate of 10 per day and physicians have nearly 70,000 products from which to choose. This staggering variety often presents a problem, as most physicians do not have formal training in genetics, and genetic testing products often are released before evidence-based guidelines that help to inform their use are available. This makes it difficult for physicians not only to pinpoint which tests to order, but also to interpret the test results and understand the minute differences between results.

Even when physicians do have confidence in the type of test selected, the interpretation of the test result may vary from lab to lab. This puts physicians in the difficult position of having to determine which labs – and which data – to trust. Often, physicians must separate the “glitz” of the products being marketed to them from their predictive value.

A Collaborative, Multidisciplinary Solution

In this new era of genomic science, issues with data overload pose significant challenges for providers long before a test has been implemented. It’s not realistic to expect physicians to predict the impact of a genetic test or product when they do not have the level of knowledge required to do so. What is needed is a framework physicians can access at the point of care to inform their decisions around precision medicine.

Ensuring access to the right information at the right time requires a framework of critical resources that can be drawn from at the point of care. This framework should comprise three components:

• The molecular expertise to interpret the changes that are identified
• The clinical expertise to characterize the relevance of those changes
• The multidisciplinary experience to determine the treatment options that should be explored

Consider Exondys 51, a relatively new gene therapy for those diagnosed with Duchenne Muscular Dystrophy (DMD). DMD is marked by an absence of dystrophin, a protein that keeps muscle cells intact. The condition affects one in 3,500 to 5,000 boys, with symptoms usually appearing between the ages of 3 and 5.

Exondys 51 costs about $300,000 a year for young boys, and its price—based on the weight of the child—can be much higher for teenagers. The drug works by increasing production of a form of dystrophin, but it only works in 13 percent of boys who suffer from DMD: those with a confirmed genetic mutation that is amenable to exon 51 skipping.

This is where understanding the molecular action of a patient’s genetic mutation is key: If a patient does not have the right genetic mutation to support treatment with Exondys 51, that patient will not benefit from this expensive drug. Laboratories, generally used to categorizing test results as “positive” or “negative” are not used to interpreting the molecular action of a treatment for a specific patient, so they do not typically flag this information for the provider, which could result in massive amounts of unnecessary costs to the patient.

Now, consider how this scenario could be improved within the structure of the three key pieces noted above: Laboratories, providers and genetic counselors must work together when considering a DMD diagnosis. Once diagnosed, a genetics expert should be consulted on the matter, reviewing lab results for the inclusion of the genetic mutation that would make a patient receptive to Exondys 51 treatment.

By developing a framework of decision-making support that combines molecular, clinical and multidisciplinary expertise, providers will be better positioned to determine the right treatment options based on a patient’s unique biology, hereditary risk, environmental risk, social determinants of health and medical needs, and could end up increasing care value while drastically decreasing costs for some patients. This framework is critical, particularly in fields such as oncology, where the demand for precision medicine is rapidly growing.

Developing the Right Framework

Establishing a structure for precision medicine decision making is key to tearing down clinical data silos and reducing the potential for errors in decision making and burnout that can result from data overload. Take the time to set up the right framework for precision medicine decision making in your organization. With the right processes in place, healthcare providers will be better able to achieve precision medicine’s full potential.