What Role can AI Play in Decisions Related to Genetic Testing

What role can AI play in decisions related to genetic testing?

Digital health

6 min read

By Elizabeth Whittington

Bringing Genetics to Everyone is a Big Data Problem Thankfully, every day it seems a new association between a gene, protein, or pathway is traced to a disease or condition. With so much data being produced and so many genetic tests coming to market, it is getting more diďŹƒcult for physicians to keep track of which tests are right for each individual patient. Care providers often work with genetic counselors, specialists who are dedicated to ensuring they understand and know the most appropriate genetic test to prescribe. Providers and genetic counselors also need to track and manage test selection based on insurance guidelines and payment schedules, or risk saddling the patient with a hefty bill as genetic testing costs can range from hundreds to thousands of dollars. But with the need for

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genetic counseling quickly outpacing the number of counselors, patients and

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their providers can either play the waiting game, the guessing game, or no game at all.1

The expanding field of genetic testing In 2018, one study put the number of genetic tests at approximately 75,000, with about 10 new tests entering the market every day.2 Today there are more than 76,000 genetic tests, as well as updates to existing tests. They have expanded from single-gene tests to panels that look at multiple genes, and they are only increasing in complexity.3 Whole-genome sequencing (WGS), and now whole-exome sequencing (WES), have also become more popular.4 In addition, there is an ever-increasing set of professional guidelines to ensure the patient is receiving the right test the first time. Anecdotal stories of patients needing a follow-up test, and then being stuck because insurance will not pay for a second test, is happening more frequently.

The challenges are exponential in certain areas of healthcare, especially across primary care and oncology. The guidelines’ nuances around who meets the criteria for testing is a complex matrix and even large gene panels don’t fully address the issue given lab to lab variations in genes included. The result is a ton of detail. A complex matrix of patient data, data on genetic testing, and guideline data. In the tech world, this is known as a “Big Data” problem. Too much information to be easily managed. The impact on the provider in the office or the health system at large is the burden of keeping track of not only who to test but how to test them.

With genetic testing becoming less costly and more accessible, one would assume clinician involvement in genetic testing would also increase. But while almost 80 percent of 488 New York-based physicians surveyed between 2014 and 2016 said they had formal genetic training, only a third had ever ordered a genetic test, provided results to a patient, or referred for genetic counseling within the past year.5 Lack of confidence in providing care to patients with high-risk genetic conditions and interpreting test results were two of the main concerns about

genetic testing reported by the physicians in the survey.

AI can step in to support clinicians in closing the gaps With the complexity and dearth of commercial and clinical genetic tests, it shouldn’t be a surprise that clinicians find it overwhelming to match the right test to the right patient at the right time for the right cost.

To help solve the growing problem of health data exhaust, the field of medicine is increasingly turning to enhanced clinical tools, such as artificial intelligence (AI) and machine learning, which have been cited as a way to improve the adoption of genomic medicine. Today, many companies have turned to machine learning to process the huge amount of data needed to appropriately analyze and interpret clinical data in fields such as radiology, cardiology and more.

As the labor shortage in health care grows, AI-based technologies can help address future clinical needs. By 2026, it’s projected that 20 percent of unmet clinical demand can be addressed by AI.6 Innovative companies and healthcare systems are using AI to aid clinicians in determining an appropriate cancer treatment based on a patient’s individual genetics, and to assist in imaging diagnostics for certain diseases and conditions.

Digital platforms and machine learning can help clinicians, including community-based or primary care physicians, especially when it comes to genetic testing. These digital tools can remove the burden clinicians face, like having to be experts on all genetic testing guidelines, including who should be tested and for which genes. The burden can be taken off the clinicians in the clinic and put into a digital platform like GenomeSmart.

GenomeSmart uses human-assisted AI and machine learning to provide personalized genetic testing recommendations through its GenomeBrain platform.7 By analyzing personal and family medical history as well as other health data, GenomeSmart aims to help patients and clinicians by providing

an individualized report and a streamlined process to refer patients for genetic testing.

An art and a science Medicine is an art and a science. Because it’s more than just checking a box or filling out a form, machine learning takes into account the nuances of personalized medicine.

Machine learning takes published guidelines into account, but it also considers training cases that show how medicine is actually being practiced.

AI is filling a need by helping to identify people who are at risk for disease before they are diagnosed.

Over the next 10 years, we will see population health initiatives increase and we will examine larger and larger cohorts. These tools that can utilize machine learning and artificial intelligence to screen across whole populations really enable scale.

And through them, we have the ability to impact many, many more lives.

Sources 1 Todd

Bookman, W. (2019). Genetic Counselors Struggle To Keep Up With

Huge New Demand. KHN : https://khn.org/news/genetic-counselors-struggle-to-keep-up-with-huge-new-d emand 2 Phillips,

K. A., Deverka, P. A., Hooker, G. W., & Douglas, M. P. (2018). Genetic

Test Availability And Spending: Where Are We Now? Where Are We Going?

Health affairs (Project Hope), 37(5), 710–716. doi:10.1377/hlthaff.2017.1427 3 Yorczyk,

A., Robinson, L., & Ross, T. (2014). Use of panel tests in place of

single gene tests in the cancer genetics clinic. Clinical Genetics, 88(3), 278-282.

doi: 10.1111/cge.12488 4 Schmidt,

JL, Maas, R, Altmeyer, SR. (2019). Genetic counseling for consumer‐

driven whole exome and whole genome sequencing: A commentary on early experiences. J Genet Couns. 28: 449– 455. doi.org/10.1002/jgc4.1109 5 Hauser,

D., Obeng, A. O., Fei, K., Ramos, M. A., & Horowitz, C. R. (2018). Views

Of Primary Care Providers On Testing Patients For Genetic Risks For Common Chronic Diseases. Health affairs (Project Hope), 37(5), 793–800. doi:10.1377/hlthaff.2017.1548 6 Accenture.

(2017). Artificial Intelligence: Healthcare’s New Nervous System

[white paper]: https://www.accenture.com/t20171215t032059z__w__/us-en/_acnmedia/pdf-49/ accenture-health-artificial-intelligence.pdf.

7 GenomeSmart.

(2019). GenomeBrain Platform:

https://www.genomesmart.com/genomebrain-platform

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