Overview
Genetic testing is rapidly moving into mainstream care, creating new opportunities for earlier diagnosis, personalized treatment, and improved outcomes while introducing significant clinical and operational challenges for health plans. In this webinar, experts from Avalon Healthcare Solutions and AllMed explored the forces driving genetic testing growth, the challenge of evaluating rapidly evolving evidence, and how health plans can build scalable approaches to manage precision medicine.
The discussion featured Sarah Bretz, Senior Product Manager at Avalon Healthcare Solutions; Jill Reznikoff, Vice President, Clinical Operations, Quality and Client Delivery at Avalon Healthcare Solutions; and Beth Helgerson, MD, Utilization Management Physician at AllMed, and was moderated by Eric Diamond, MD, Medical Director at AllMed.
Avalon’s 2026 Lab Trend Report found genetic testing spend increased 35 percent while utilization rose 22 percent, reflecting both greater use and the growing sophistication and cost of testing.
Genetic Testing: Innovation Is Driving Rapid Growth
Approximately 10 new genetic tests enter the U.S. market every day, with testing expanding beyond rare diseases into oncology, reproductive health, cardiovascular disease, and neurology. Genetics is also playing a larger role throughout the patient journey—from identifying inherited risk and diagnosing disease to selecting targeted therapies and monitoring treatment response. At the same time, providers are increasingly moving from single-gene assays to comprehensive sequencing panels, liquid biopsies, and advanced genomic profiling, contributing to spending growth that is outpacing utilization.
Clinical Value Depends on the Right Patient and the Right Test
More testing does not always mean better care. Genetic tests can provide significant clinical value for the appropriate patient while offering little actionable information for another. The panel emphasized that coverage decisions should consider three fundamental questions: Does the test accurately identify what it claims to measure? Will the result change clinical management or improve outcomes? And where does the test fit within the patient’s care pathway? Evaluating clinical validity, clinical utility, and value together can help health plans support meaningful innovation while ensuring members receive the right test at the right time.
Evidence-Based Policy Must Keep Pace With Innovation
With new tests entering the market faster than traditional policy cycles can accommodate, evaluating each innovation individually is becoming increasingly difficult. The panel highlighted the need for an evidence-based infrastructure that continuously monitors emerging technologies, evaluates clinical validity, utility, and cost, and rapidly translates that evidence into medical policy and operational workflows. Strong medical policies can help health plans adopt innovations when evidence supports them while avoiding premature adoption of tests that have not demonstrated meaningful clinical benefit.
Coding and Test Identification Remain Major Challenges
Genetic testing presents an additional operational challenge because thousands of tests can be billed under the same nonspecific CPT codes. Without knowing exactly which test was performed, which genes were analyzed, or which technology was used, health plans can struggle to apply the appropriate medical policy and determine reasonable reimbursement. More precise test identification can enable greater automation, reduce unnecessary manual reviews, and provide faster and more predictable decisions for providers.
Utilization Management: Better Decisions, Not Simply Fewer Tests
The panel emphasized that effective utilization management should not be viewed solely through a cost-containment lens. Evidence-based management can address both overutilization—avoiding tests that provide little actionable information—and underutilization by helping ensure patients receive clinically important testing that might otherwise be missed. Avalon shared an oncology example in which more efficient testing pathways and automation reduced the time from testing to guideline-concordant treatment from approximately 30–40 days to about seven days. The ultimate goal is better clinical decision-making, not simply fewer tests.
Preparing for the Future of Precision Medicine
As precision medicine becomes more mainstream, health plans will need capabilities that can scale with increasing diagnostic complexity. The panel identified four priorities: continuous surveillance of emerging evidence, robust assessment of clinical validity and utility alongside economic impact, technology that accurately identifies tests and automates evidence-based policies, and integrated laboratory data and analytics. Together, these capabilities can help health plans move beyond reactive utilization management toward earlier diagnosis, more personalized treatment pathways, and more consistent application of evidence-based care.
Conclusion
Genetic testing holds significant promises for improving patient care, but its rapid growth is challenging traditional approaches to coverage, medical policy, and utilization management. As innovation accelerates, health plans will need scalable, evidence-based capabilities that allow them to evaluate new tests consistently, identify the right test for the right patient, and translate evolving science into practical clinical and operational decisions. The opportunity is not simply to manage the growth of genetic testing, but to ensure advances in precision medicine reach patients quickly, appropriately, and consistently.
