Precision Health: What the CEO of a DNA Diagnostics Company Wants Investors and Patients to Know
Written By: Ryan Morrison
Based on a Navigating Wealth conversation with Sam Raha.
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Precision medicine has been described as the future of healthcare for so long that it is easy to miss the moment it arrived. Sam Raha has spent 25 years at the intersection of genetics, diagnostics, and the business of turning scientific discoveries into products that reach patients, most recently as President and CEO of Myriad Genetics, the company that pioneered BRCA testing 34 years ago and now serves 1.5 million patients annually. His view of where precision health stands today is specific enough to be useful both to investors evaluating healthcare as a sector and to individuals thinking about what proactive genetic testing can and cannot do for them.
Precision health uses individual biological data (primarily genetic and genomic information) to match patients with treatments that have a high likelihood of working for their specific biology, rather than relying on generalized population averages. It is already embedded in the treatment of tens of millions of patients annually through companion diagnostic tests that help oncologists select the right drug for the right patient. The opportunity ahead is integration with wearables, AI-enabled analysis, and expansion beyond cancer into other disease categories.
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What Precision Health Is and Why It Has Finally Arrived
For most of medical history, treatment decisions were based on population-level averages. If you were sick, a doctor would prescribe what worked for most people with your condition, regardless of your individual biology. Precision health changes that by using specific information about you (genetic, genomic, proteomic, or some combination) to match treatment to patient at a much finer level of resolution.
The early adoption happened in oncology, and not by accident. Cancer drugs benefit from precision targeting more than almost any other therapeutic category, because the difference between a drug that works for a specific tumor biology and one that does not can be enormous. Pharmaceutical companies also discovered that clinical trials using biomarker-selected patients were more tractable with the FDA: easier to design, faster to reach statistical significance, more likely to result in approval. Precision medicine, which pharma once viewed as a threat (a drug that works for everyone is the ideal commercial proposition), became an accelerant to market.
Today, tens of millions of patients annually are being prescribed drugs that were selected, in part, based on a biomarker test. Sam Raha is direct: precision medicine is not still arriving. It has arrived. The question is how much further it will go, how fast, and in which therapeutic categories.
The broader trend reinforces the case. The pandemic produced a generation of consumers who are genuinely interested in monitoring and managing their own health. People who had never heard of spike proteins became fluent in the language of immune response in a matter of months. Wearables that track sleep, heart rate variability, and blood oxygen levels have normalized the idea of continuous personal health data. The cultural infrastructure for individualized health management is now in place in a way it was not a decade ago.
Watch the Full Conversation
This article draws on a Navigating Wealth conversation with Sam Raha, where we discuss what precision health is, how companion diagnostics work, how AI is being embedded into diagnostic testing, and what individuals should know before pursuing proactive genetic testing. Watch the full episode for the broader discussion.
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Sam Raha is President and CEO of Myriad Genetics, a leading precision medicine and molecular diagnostics company. He is a molecular biologist by training who spent his early career at Illumina before moving into senior commercial and leadership roles at Agilent Technologies. He joined Myriad in December 2023 with a mandate to redefine the company's position across the full cancer care continuum while expanding into prenatal health and mental health diagnostics.
How Companion Diagnostics Work — and Why They Matter for Investors
A companion diagnostic is a test whose use is written directly into a drug's FDA approval. It is not an optional add-on; it is a required step in the treatment protocol. An oncologist prescribing a drug that has a companion diagnostic on its label must first determine that the patient's biology meets the relevant criteria. The diagnostic company earns a fee every time that determination is made.
The business model that produces is durable in a way that pure pharmaceutical revenue is not. A pharmaceutical company's revenue from a drug is limited by its patent life. When the patent expires and generics enter the market, the revenue collapses, sometimes over a period of months. A companion diagnostics company earns from every test ordered for the life of the drug's commercial adoption, without the same patent cliff exposure. The protection comes from the drug's patent, the companion diagnostic's own intellectual property around methodology and bioinformatics, and from the scale advantage of processing millions of samples with industry-leading turnaround times.
The Myriad example is concrete. Myriad developed the MyChoice HRD (homologous recombination deficiency) test in partnership with AstraZeneca. HRD measures a specific pattern of DNA repair dysfunction that predicts which patients are likely to respond to a class of cancer drugs called PARP inhibitors. MyChoice is the companion diagnostic listed on the FDA label for AstraZeneca's Lynparza. Every oncologist who prescribes Lynparza and wants to confirm whether a patient is likely to respond will order a MyChoice test. Myriad earns from that test; the revenue compounds as Lynparza's adoption grows; and the relationship does not expire when AstraZeneca's patent does, because Myriad's test has its own regulatory standing and commercial position.
The gross margin that results from this model, roughly 70% at Myriad, reflects the leverage available once the fixed cost of test development and FDA approval has been absorbed. A test that serves 1.5 million patients annually at 70% gross margins generates significant free cash flow relative to the ongoing cost of running the business.
For investors evaluating healthcare, the companion diagnostics segment represents a specific structural advantage within the broader precision medicine theme: recurring revenue tied to drug adoption, no patent cliff, high gross margins, and a growing addressable market as more drugs come to market with companion diagnostic requirements.
The Investment Case for Precision Medicine
The investment case for precision medicine is built on a feedback loop that becomes self-reinforcing as adoption grows.
Better patient-drug matching produces better clinical outcomes. Better clinical outcomes reduce the total cost to the healthcare system of failed or ineffective treatment courses. Lower total cost makes broader insurance coverage and reimbursement easier to justify for payors. Broader reimbursement increases utilization. Increased utilization generates more data about what works, enabling more refined biomarker development. And more refined biomarker development creates the next generation of companion diagnostics and targeted therapies.
The pharma industry's relationship with precision medicine illustrates the shift in incentive. Twenty years ago, pharmaceutical executives viewed biomarker-based selection as a commercial threat: a drug that works for 30% of patients rather than 100% of patients means a smaller addressable market. What emerged over the following decades is a different calculus. A drug that demonstrates strong efficacy in a biomarker-selected population moves through clinical trials faster, faces a more favorable FDA review, can command a premium price relative to its efficacy evidence, and accumulates real-world data that supports label expansion into adjacent populations. The precision approach is now seen as a commercial accelerant, not a constraint.
For diagnostics companies specifically, the structural advantage is that they participate in the upside of every successful targeted therapy without taking the same development risk. A pharma company bears the full cost of drug development, which runs into the hundreds of millions or billions of dollars. A companion diagnostics company co-develops the test during the drug development process, often funded in part by the pharmaceutical partner, and then earns from the commercial rollout without the drug development risk.
For context on how high-net-worth investors are currently sizing healthcare and biotech allocations relative to other alternatives, the 2026 asset allocation report covers current allocation patterns across 230+ respondents. How precision medicine compares structurally to other alternative sectors like private credit, in terms of revenue durability, reimbursement risk, and entry timing, is a useful parallel for investors building out an alternatives allocation.
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How AI Is Changing Genetic Diagnostics: Three Categories
Sam Raha organizes AI's role in precision medicine diagnostics into three categories, each at a different stage of maturity.
Operational AI is already fully deployed. The most concrete example at Myriad is insurance prior authorization. Insurance companies began using AI bots to manage denial and reimbursement requests several years ago, automatically generating new questions and objections to slow or block payment. Myriad has built its own AI capability to engage with those bots directly, reducing the prior authorization cycle from approximately six months to a matter of weeks. In a business where cash flow depends on timely reimbursement from insurance companies, that compression has material financial impact. Lab batching and workflow optimization are also in this category: AI helps schedule and sequence sample processing to reduce turnaround time and improve throughput.
Customer-facing AI is the digital layer. For diagnostic testing, this means helping patients understand what their test results mean before, during, and after the process, and giving healthcare providers the same support when they need to communicate complex genetic information in a clinical setting. The question "what does this mutation mean for me" does not always have a simple answer, and AI-assisted navigation of the response reduces the dependence on humans for every interaction while improving the quality and consistency of the information delivered.
Product-integrated AI is where the field is moving fastest and where the most significant clinical impact will emerge. Myriad is launching an AI-enabled prostate cancer test in Q2 2026 that combines two inputs: a digital pathology component where an algorithm analyzes cell morphology at a resolution and scale that no human pathologist could match, and a molecular genomics component that characterizes the underlying biology of the cancer. The combined signal gives oncologists a more confident answer on borderline cases (patients where the traditional question was "treat now or wait"), moving them from clinical uncertainty to a clear recommendation.
The regulatory picture is more permissive than many outsiders expect. The FDA and equivalent bodies globally are actively engaging with AI in diagnostics, seeking to develop frameworks rather than waiting to resist the technology. The constraint on full adoption is not scientific approval; it is reimbursement. Insurance companies tend to lag on covering new test categories, including AI-enabled ones, by a year or more after FDA clearance.
Should You Get Genetic Testing? What to Know Before You Decide
The question of whether to pursue genetic testing is worth approaching with more specificity than the general "knowledge is power" default suggests.
For genetic testing where there is a clear, actionable result, the case for testing is strong. BRCA1 and BRCA2 mutations associated with elevated breast and ovarian cancer risk have established management protocols: enhanced surveillance, preventive surgery, or risk-reducing medication depending on individual circumstances and preferences. Hereditary colon cancer syndromes, hereditary cardiac conditions, and certain neurological risk factors fall into the same category. A positive finding creates a clear path to doing something with the information.
For genetic findings where the connection between mutation and outcome is probabilistic rather than deterministic, and where no clear management protocol exists, the benefit of testing is more nuanced. Most traits are influenced by many genes simultaneously, combined with environmental factors, lifestyle, and epigenetic effects. The result is not "you will get this disease" but "your risk is elevated by this amount relative to the general population." Whether that information is useful depends on whether it changes what you do with it.
Sam Raha's recommendation for most individuals who want to start somewhere: the MyRisk hereditary cancer panel is a useful baseline test. It is not only for women. Men carry and pass on BRCA mutations just as women do, and hereditary cancer risk is relevant for both sexes across multiple cancer types. Beyond cancer, pharmacogenomics (testing for genetic variants that affect how your body metabolizes specific drugs) is increasingly actionable, particularly for individuals managing chronic conditions or taking multiple medications.
Sriram's own experience is instructive about the limits of the current system. He underwent standard blood testing that flagged him as high on a particular marker using American reference ranges. A subsequent PCP correctly identified this as a normal variant for South Asian individuals: the reference range he was evaluated against did not reflect his population's biology. The test was not wrong. The frame of interpretation was. Understanding how the 80/20 applies to health optimization (which tests and interventions move the needle versus which create noise) is the practical question that precision medicine is beginning to answer at the individual level.
The Insurance Risk Nobody Tells You About
The Genetic Information Nondiscrimination Act (GINA), passed in 2008, prohibits health insurers and employers from discriminating against individuals based on genetic information. It is a meaningful protection for the most immediate concerns most people have about genetic testing.
What GINA explicitly does not cover matters just as much. Life insurance, long-term care insurance, and disability insurance are excluded from GINA's protections. These are the insurance categories where individuals in the Long Angle community are most likely to have significant active coverage or open underwriting processes. A hereditary cancer mutation, a cardiac risk variant, or a neurological risk factor that shows up on a comprehensive genetic panel could, in principle, affect your ability to obtain or renew coverage in these categories at standard rates.
This is not a theoretical concern. It comes up in conversations among Long Angle members, and Sriram articulated it directly in this conversation: some people who are otherwise inclined toward proactive, high-resolution testing are deferring it specifically because they have open underwriting processes in progress or anticipate needing to purchase life or long-term care coverage in the near future.
The practical guidance: if you are in the middle of a life insurance underwriting process or expect to purchase significant coverage in the next one to three years, speaking with an independent insurance advisor before running comprehensive genetic panels is worth the time. The test will still be available after your coverage is in place. The sequence matters.
The systemic problem is broader. The US insurance system creates a structural disincentive for preventive testing that, in many cases, would reduce long-term healthcare costs. A patient who discovers a hereditary cancer risk and takes preventive action is likely to cost the healthcare system less over their lifetime. But the insurance company that covers their life policy may have no stake in that outcome. Australia has recently advanced legislation restricting insurers from using genetic information in underwriting decisions, a potential model for addressing this misalignment at the policy level.
Where do Long Angle members discuss the intersection of healthcare investment and personal health decision-making?
Members share diligence on healthcare and biotech investment opportunities, compare notes on proactive testing decisions, and discuss the insurance considerations that rarely come up in a standard financial planning conversation. The environment is solicitation-free.
The Ethnic Bias Problem in Standard Reference Ranges
The reference ranges that appear on standard blood test results were built from historical patient cohorts. Those cohorts, for reasons that reflect the history of clinical research in the United States, are not representative of the full ethnic and genetic diversity of the patient population. The result is that reference ranges designed for the average American population systematically misclassify individuals from underrepresented groups.
Sriram's experience with this is direct and personal. A standard blood panel flagged him as elevated on a particular marker. One physician treated it as a concern worth investigating further. A second physician, with familiarity with South Asian physiology, correctly identified it as a variant that is normal for individuals of South Asian ancestry: the marker the first physician was concerned about does not have the same clinical meaning in his population. The reference range was not wrong for the population it was built on. It was applied to someone for whom it was not designed.
East Indian and East Asian individuals, for example, have a documented elevated cardiovascular risk relative to populations of European ancestry that is not fully captured by the standard lipid panel thresholds. Applying European-derived reference ranges to these patients can both underestimate risk (missing a genuine signal) and overestimate it (flagging normal variation as pathological). Precision medicine, at its most ambitious, would replace population-average reference ranges with individually calibrated norms based on genetic background, family history, and biomarker trajectory over time.
This is not an abstract future capability. It is a concrete present-day problem that individual patients can partially address by advocating for themselves. If a test result seems inconsistent with your overall health, your family history, or your own sense of how your body works, asking your physician whether the reference range being applied is appropriate for your ethnic background is a reasonable question. Seeking a second opinion from a physician with relevant population-specific expertise is worth the time for any finding that would lead to significant follow-on action.
Frequently Asked Questions
What is precision health and how is it different from regular medicine?
Precision health uses individual biological data (typically genetic, genomic, or proteomic) to tailor diagnosis and treatment to a specific patient rather than applying generalized population averages. Standard medicine asks what works for most people with a given condition. Precision health asks what is most likely to work for this patient, given their specific biology. The approach is most developed in oncology but is expanding into cardiology, psychiatry, prenatal health, and general wellness.
What is a companion diagnostic test?
A companion diagnostic is a test whose use is written into a drug's FDA approval label. Before prescribing a targeted therapy that has a companion diagnostic requirement, an oncologist must determine whether the patient's biology meets the relevant biomarker criteria. The test identifies which patients are likely to benefit from the drug and which are not. Companion diagnostics are developed in partnership with pharmaceutical companies and represent a distinct revenue stream for diagnostics companies that does not depend on the drug's patent expiration.
Should I get genetic testing for cancer?
For hereditary cancer syndromes with established management protocols (BRCA1/2, Lynch syndrome, hereditary cardiac conditions) testing is generally recommended for individuals with relevant family history, and increasingly for broader populations as costs decline. The MyRisk hereditary cancer panel is a comprehensive starting point for both men and women. Before testing, speak with a genetic counselor to understand what the results could mean and what the actionable follow-on steps look like for various outcomes.
What does GINA protect and what does it not cover?
The Genetic Information Nondiscrimination Act (2008) protects against discrimination by health insurers and employers based on genetic information. It does not cover life insurance, long-term care insurance, or disability insurance. Individuals with open underwriting processes in any of these categories should speak with an independent insurance advisor before running comprehensive genetic panels.
How is AI being used in precision medicine today?
AI is being used in three ways in precision medicine diagnostics: operational (lab efficiency, insurance prior authorization automation), customer-facing (digital results interpretation for patients and providers), and product-integrated (AI as a component of the diagnostic test itself). Myriad Genetics is launching an AI-enabled prostate cancer test in Q2 2026 that combines digital cell morphology analysis with molecular genomics data to give oncologists more confident guidance on borderline treatment decisions.
Are standard blood test reference ranges accurate for people of all ethnicities?
No. Reference ranges in standard blood tests were built from historical cohorts that are not representative of the full ethnic diversity of patients. Individuals from South Asian, East Asian, and other underrepresented populations may be flagged as high or low on markers that are within normal range for their specific population. If a result seems inconsistent with your overall health or family history, asking your physician whether the reference range is appropriate for your ethnic background is a reasonable first step.
Final Thoughts
Precision medicine is not a futurist concept. It is already embedded in how tens of millions of patients are treated annually, primarily in oncology. The infrastructure for broader adoption (falling sequencing costs, wearable data integration, AI-enabled analysis, and a cultural shift toward proactive health management) is in place. What remains is the expansion: into more therapeutic categories, more biomarker types, and more precise individual-level analysis.
For investors, the companion diagnostics segment represents a structural advantage within that trend: recurring revenue, no patent cliff, high gross margins, and a compounding market. For patients, the opportunity is real, but so is the need to navigate it carefully: understanding what tests are actionable, what protections exist, and what reference ranges were designed for populations that may not include you.
Long Angle members discuss healthcare investment opportunities and personal health decisions in the same solicitation-free environment.
Members share what diligence revealed on biotech and diagnostics investments, compare notes on proactive testing approaches, and navigate the insurance considerations that rarely come up with a standard financial advisor.
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