Far too many laboratory tests are accurate, reproducible, and supported by strong research, yet never reach the patients who could benefit from them.
Why? Because demonstrating that a test works is only the beginning. Making it useful in the real world requires solving a different set of problems. I’m a molecular biologist who has spent my 25-year career bringing laboratory tests to clinical use.
The one lesson that has stayed with me is that adoption is a critical endpoint of innovation. Potential barriers to adoption must be considered from the outset to avoid developing a test that can never be adopted due to cost or complexity.
Three must-haves for a test
1. It has to work
Yes, it’s obvious, but the rigor needed is often overlooked. One promising result isn’t enough. It takes multiple runs with real-world specimens, different operators, and everyday laboratory conditions.
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The goal is to understand how the test performs across the populations and settings where it may be used. That means asking:
- Which samples and sample types were included in the verification study?
- How were samples collected and handled?
- What existing method was used for comparison?
- How consistently can different laboratories reproduce the results?
These questions can be uncomfortable, and they can lead to an even more uncomfortable one: If an innovation can only be implemented in a handful of well-resourced institutions, how much of its potential benefit will reach patients? These questions also take time and resources to answer. They are worth the investment.
2. It needs to be able to drop in
I’ve seen technically impressive methods struggle because they compete with established, already-reliable approaches. If a new test requires specialized equipment, additional training, complicated sample preparation, or major changes to laboratory operations (and it often does), adoption becomes considerably harder.
Laboratories operate under real pressures. Staff have limited time. Equipment must be maintained. Results must be delivered consistently. Developers often treat these constraints as an afterthought. They belong at the start.
3. It needs to be affordable
My work in liquid biopsy reinforced another lesson: A test’s clinical value means little to a patient who cannot access it. In my experience, affordability is the biggest reason good tests never reach patients or later get pulled from the market.
Advanced diagnostics carry costs well beyond reagents: equipment, staffing, quality control, repeat testing, interpretation, and overhead. Volume matters, too. The cost of a run is spread across its samples, so a run of 5 costs the lab 10 times more per test than a run of 50.
That makes instrument choice an economic decision. Vendors often tout a low cost per test on high-throughput sequencers like the NovaSeq. But those figures assume maximum batch sizes a small lab may never reach, and they rarely reflect the all-in cost. A low-volume lab may pay less per test on a smaller MiSeq.
Reimbursement matters just as much. Without enough evidence to support coverage, patients may face hundreds or thousands of dollars in out-of-pocket costs, no matter how useful the test. That leaves physicians weighing the best information against their patients’ finances.
Developers should weigh affordability before development begins, when drafting the target product profile. If a test’s intended use resembles one already covered, it may be able to use an existing CPT billing code, though a code alone does not guarantee coverage or adequate payment. For a truly novel test, developers should choose instruments based on batch sizes real labs can achieve.
What this means for physicians
Published research doesn’t make a test right for every patient. Physicians shouldn’t have to become sequencing engineers to evaluate one. Four questions help:
1. What clinical decision will this result change? Before ordering a test, identify how a positive, negative, or inconclusive result might influence diagnosis, treatment, or management. More information isn’t better if it doesn’t lead to a meaningful decision.
2. Does the evidence apply to my patient? Look beyond headline accuracy figures. Consider the populations, specimen types, and clinical settings studied. Performance in one population or research environment may not translate to another.
3. What are the test’s limitations? Ask about detection thresholds, potential false results, specimen requirements, and circumstances that could make findings unreliable. Understanding what a test cannot tell you is as important as understanding what it can.
Take ultra-sensitive HIV viral load testing. In a patient whose HIV is well controlled, detecting 3 viral copies rather than reporting a result below 20 copies may add analytical detail without changing care. Sensitivity is a capability. Clinical utility depends on what it enables us to do for the patient.
4. Will the result arrive in time to matter? Consider the full path from collection and transport to analysis, reporting, and follow-up. A result that arrives after the treatment decision delivers no clinical value.
Developers should ask these same questions much earlier, with physicians and laboratory professionals involved before major design decisions are locked in.
What innovators need to do differently
If we want more promising science to reach patients, we must consider practical clinical utility and patient access early. That means involving lab professionals and clinicians early. It also means designing for real-world labs, not ideal research conditions.
It also means candor. Test developers, and the medical advisors and key opinion leaders they rely on, should say plainly when a technology has scientific value but isn’t ready to guide care. Single-cell sequencing and spatial biology are good examples. They answer important research questions but don’t yet have widespread clinical use. Physicians can learn a lot about a test’s clinical readiness by looking at who advises the company behind it.
Finally, it means investing in medical education, because physicians who understand a test order it for the right patients and interpret it correctly. In oncology, a test may look at germline mutations, which signal inherited cancer risk. Or it may look at somatic mutations, which can guide therapy or detect minimal residual disease. Knowing the difference changes what a patient is told.
None of this diminishes scientific discovery. But good science only makes a test possible. The job isn’t finished until that test can support a better decision for the patient waiting on the result.
Jamie Platt is CEO of Pictor, a targeted proteomics platform company located in Carlsbad, California. Pictor’s technology is for research use only.
Jamie brings over 20 years of transformative leadership in genomics and molecular diagnostics. She has successfully led the development, validation, and commercialization of more than 40 complex molecular tests across global markets. Jamie previously served as chief operating officer at PGDx and Inivata, where she played a pivotal role in launching cutting-edge biopsy products and achieving two merger and acquisition (M&A) exits. She is also the founder of BRIDGenomics, a consulting firm supporting diagnostics innovation.
A strategic operator and scientist, Jamie integrates Six Sigma methodologies with deep technical expertise to drive product innovation and operational excellence. She holds multiple patents, is a recognized industry speaker, and serves on the boards of DxTerity and bioAffinity Technologies (Nasdaq: BIAF; BIAFW). Jamie earned her PhD in molecular and cellular biology from Oregon State University and completed her postdoctoral studies at the University of California, Berkeley. She is passionate about delivering impactful, science-driven solutions to the clinical diagnostics market.
