How providers can use genetic data to improve population health

Genetic data to improve population health

SUNNYVALE, Calif. (Apr. 24, 2019) – It’s hard to ignore the growing buzz around genetic testing.

From TV ads hawking over-the-counter tests to the arrest of the Golden State killer through DNA matching with an open-source genetics database to popular breed-identification tests for dogs, public interest in genomics and genetic testing has never been higher.

Despite all the attention around genetic data, many provider organizations lag when it comes to leveraging genetic data to improve patient care, sometimes feeling that they lack the expertise to understand and explain to patients the implications of existing genetic tests.

This lack of utilization of genetic data is a missed opportunity for providers. By combining genetic data with lifestyle factors and known medical conditions, clinicians can identify personalized plans for lowering and managing patients’ risks of developing chronic diseases.

The key is to convert genetic data to actionable personalized information that doctors can use to tailor medications, lifestyle changes, therapies and treatment plans for individual patients.

Indeed, some providers have already begun to offer genetic testing as a means of improving patient care. Last year, Geisinger Health announced that it would offer DNA sequencing to 1,000 patients, with the goal of eventually providing the service to its entire 3 million patient population. Similarly, Illinois-based NorthShore University Health System recently announced plans to offer genetic testing to 10,000 primary care patients to assess their risk of developing conditions such as breast cancer, colorectal cancer and heart disease.

Some of these initiatives are designed to identify new genetic markers which are associated with a chronic condition, and some are measuring the association effect of an existing genetic marker to a new phenotype. There is already very useful data about the association between known genetic markers and progression of several chronic conditions as well as human responses to specific drugs and foods, which we could apply in our healthcare system to improve outcomes.

The U.S. health system needs more widespread incorporation of genetic data to create personalized treatment and health management plans to fully realize the value of this information. Nothing is more personalized or predictive than our own genetic data

The state of genetic testing today
In general, providers order genetic tests for patients who are assessed as at-risk for certain genetic conditions, such as diabetes, heart disease and cancer. To reduce the potential for misinterpretation and misinformation, it’s best that doctors order all genetic tests, and trained professionals such as genetics counselors or doctors should explain the results to patients

Most genetic tests analyze single nucleotide polymorphisms (SNPs), which are the most common type of genetic variation among people. While particular SNPs may not cause disorders, some SNPs can be predictive of certain diseases, enabling genetic tests to reveal an individual’s predisposition toward developing that disease, so very targeted and modifiable interventions can be subsequently applied to reduce the risk of developing the disease.

Some of the common examples of genetic tests in use today include tests for pregnant women to detect fetal genetic abnormalities, drug response assessments and breast cancer risk assessments. Genetic testing for breast cancer, for example, involves analysis of the BRCA genes to look for specific mutations, which may indicate an increased cancer risk.

In a recent research study published at PlosOne in 2017 by Zarkoob et al. the effect of combining the environmental data with the genetic data was measured in a Swedish population. The study demonstrated who would get the most value from undergoing a genetic test in a population if diabetes risk were being assessed. With advanced analytics capabilities, providers would be able to identify what sub-population within a large population would get the most benefit from undergoing a genetic test. This would in turn generate the best clinical and financial outcomes for the health providers and payers.

Combining genetic and lifestyle data
Providers can derive even greater value from genetic data by combining it with other types of data, such as lifestyle data, lab data, medical data and behavioral data gleaned from survey questions about personal habits.

For example, consider a scenario in which a patient is overweight, with a high triglyceride level and has a family history of diabetes. The patient’s physician orders a genetic test, which reveals that the patient has also genetic predisposition for diabetes which means that patient genetically also has an increased likelihood of developing diabetes. By combining genetic and lifestyle data, advanced data analytics can measure how much that patient could reduce the likelihood of developing diabetes by reducing his or her triglycerides by 10 units or by losing 5 pounds

This is a clear example of how personalized medicine powered by genetics can help patients at an individual level. It is much more efficient for a provider to tell the patient that by reducing your weight by 5 pounds in the next 4 weeks, you will reduce your likelihood of getting diabetes by 10 percent, compared with just telling the patient to reduce weight.


Advanced analytics coupling genetic and environmental data can deliver much more personalized, actionable information to the provider. Another example on what an advanced health analytics system might reveal to a provider is that by screening a few SNPs from a pharmacogenetics (drug response) panel, a physician can select the most appropriate cholesterol-reducing drug for someone with a high cholesterol level. Having the ability to prescribe the most effective and targeted drug for a patient based on the most personalized data about the patient will generate a big win/win scenario for the physician, patient and the payer. In this case, the patient’s chances of developing cardiovascular disease would be lowered by the right drugs based on her genetic data.

What’s holding back genetic testing?
Due in part to the technology’s novelty, genetic testing today is not as widespread as it is likely to be in the future – but novelty isn’t the only reason. Like many things in healthcare, costs and reimbursement factor heavily into the situation. The American Medical Association points a finger at public and private payers.

“Many patients do not have access to precision medicine because most public and private health insurers do not offer coverage for genetic or genomic services unless certain clinical criteria and evidentiary standards are met,” the AMA said when it adopted a new policy on genetic testing in 2017. “As a result, access to this next generation of clinical testing services is often limited.” Again, more efforts by healthcare providers should be targeted towards reducing the risk of a disease before an individual develops the disease, versus having the main focus merely on people who have already developed one or multiple chronic conditions.

Payers, for their part, cite questions about the clinical efficacy of many genetic tests, lack of regulatory oversight as well as a need for more education – particularly involving direct-to-consumer companies that may not adequately explain results. “Genetic counseling is key to interpreting the results of a test that imparts a probability of disease,” according to trade group America’s Health Insurance Plans.

Both sides raise fair points in citing obstacles that are preventing wider adoption of genetic testing, but the industry will find ways to overcome those barriers soon enough. As those barriers fall, we’ll move increasingly closer to better management of population health through smarter use of genetic data in combination with environmental data.

For the full article, please go to the LinkedIn Article.

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