Introduction

Modern diagnostic tests have evolved. They’re no longer stripes on a swab or colors in a test tube. Today, many of the most powerful diagnostics generate enormous amounts of biological data and rely on sophisticated algorithms to interpret it. And this is necessary, given what they’re expected to analyze. A metabolomic test might look at dozens of small markers to find minute differences in disease state. A proteomic test may measure thousands of proteins at once, searching across a highly complex dataset for subtle patterns associated with disease. Transcriptomic tests are expected to detect markers that might be measured in parts per trillion. Frankly, there is no practical way to interpret datasets of this complexity using traditional analysis alone. Modern multiparametric diagnostics therefore depend on sophisticated algorithms, increasingly powered by machine learning, to recognize complex biological patterns and translate them into clinically meaningful results.

To be clear, this is a welcome development. With this sort of information, we can manage disease more effectively and more personally, delivering treatment optimized for that individual and their unique needs. These powerful new tools are reshaping how we detect and treat disease at every stage.

Building the diagnostic is only the beginning

To realize the benefits of these technologies, we need to get them into labs and into the hands of physicians and patients. That’s where deployment comes in. Deployment takes a diagnostic from development into routine clinical use and keeps it running safely and reliably over time. It includes validation, production use, iteration and refinement, audits, and eventual retirement. And deployment is a completely different environment to development.

When diagnostic enters clinical use, the environment changes. You’re no longer working primarily with research datasets; you are handling personal health information subject to strict regulatory requirements. You must determine where and how the test will run, whether that’s within your own infrastructure, within a laboratory’s environment, or in the cloud, as well as how that model will scale as test volumes grow. You need audit logs, traceability, version control, defined user roles and permissions, and processes for regulatory compliance. And if the test is deployed across multiple laboratories or jurisdictions, each of those challenges becomes more complex. And these are only some of the challenges that emerge when a diagnostic moves from development into clinical use. During development, many of them can remain largely invisible. But once a test begins operating in the real world (across patients, laboratories, users, and jurisdictions) they become unavoidable.

That’s because developing a diagnostic and deploying one are fundamentally different problems. Development is primarily about proving that the science works. Deployment is about ensuring that the test can be operated safely, consistently, securely, and at scale. That means managing patient data, controlling software and algorithm versions, maintaining traceability, defining user permissions, validating changes, supporting multiple laboratories, and satisfying regulatory requirements.

For many diagnostic developers, this creates an entirely new set of technical, regulatory, and operational demands, often requiring expertise and infrastructure they never expected to need. Solving each problem independently can be expensive and time-consuming, particularly for smaller companies and academic spinouts. A scientifically excellent diagnostic can therefore reach the end of development only to encounter another major hurdle: getting it into routine clinical use. In diagnostics, completing the science isn’t the finish line. In many ways, it’s the start line.

We learned this the hard way

At Qualisure, we learned this firsthand. We started with an exciting prognostic test for thyroid cancer. Thyroid GuidePx® was clinically validated and ready to provide physicians with information that could help guide treatment decisions. But developing the diagnostic was only part of the journey. Bringing it into clinical use introduced an entirely new set of challenges: laboratory integration, data movement, validation, security, version control, reporting, regulatory requirements, and ultimately, scaling beyond a single laboratory.

Are you prepared to meet each of these challenges head on, and pay in time, attention, personnel, and money to solve them? And do you even know how they’ll play out in your specific situation?

The deployment problem is solvable

Throughout this series of upcoming blog posts, we’ll dive into the main areas where developers struggle to deploy. We’ll pull back the curtain and reveal blind spots, helping you prepare for the road ahead. We’ll share our story and what the future holds. Most importantly, we’ll share how we solved these problems ourselves—and why we ultimately decided there had to be a better way to deploy sophisticated diagnostics.

So join us as we explore these topics in the weeks ahead. We’re glad to have you along.