Designing the next generation of at-home diagnostics

IPT speaks to Ali T at LotusDx about the challenges for accessible diagnostics, from developing accurate and user-friendly at-home tests to building trust and connecting testing with effective healthcare pathways

Ali T (AT): The answer depends heavily on geography and on the purpose of the test. Individual testing, whether prompted by routine screening or a specific personal circumstance, is likely to increase over the next few years, provided tests are accurate and easy to use. Population-level screening is moving in the opposite direction in countries with established screening programmes, across western Europe, for example, largely due to antimicrobial resistance. Treating more people means more antibiotic use and every additional course adds pressure to a resistance problem already tightening treatment options across several of these infections. Low- and middle-income countries face a different constraint entirely, which is why the opposite trend is likely to happen. Most still rely on syndromic management, since the lab infrastructure required for widespread testing isn’t in place. Because a large share of STIs are asymptomatic, disease burden remains high relative to countries with that infrastructure. This has also resulted in antibiotic overuse, though as a consequence of limited diagnostic availability rather than overtreatment through screening. Therefore, an expansion of STI diagnostics is necessary to reduce unnecessary antibiotic use and disease burden.

The barriers to testing, however, are broadly similar regardless of infrastructure. In countries with an established testing culture and those without, avoidance is most often driven by a lack of awareness and stigma. In both environments, private STI and HIV self-tests have seen significant uptake. In England, for example, almost 40% of chlamydia testing is now conducted through self-sampling kits rather than in clinic, driven by the removal of the need to arrange a clinic appointment, reduced fear of stigma and the comfort of testing from home. Similarly, in high-HIV-burden African countries, which don’t have the same decentralised laboratory infrastructure as the UK, self-testing kits have produced a two- to three-fold increase in uptake compared to clinic-based testing. In both cases, adoption has been reinforced by awareness campaigns from healthcare providers, communities, charities and other institutions educating the public on STIs and sexual health more broadly, reducing stigma and increasing willingness to test in the absence of symptoms.

AT: The home environment differs significantly from the laboratory in that the user is expected to perform the test alone, with no training or oversight, using a device that has to be usable by anyone regardless of age, physical ability, or education level. That means the test needs to be not just accurate, but user-friendly, while minimising how much variation in a person’s operation of the device can affect the final result. On top of that, if the aim is regular, repeat testing, then it needs to be affordable, whether that’s for an individual buying it outright or a healthcare provider distributing it at scale. There’s no ten- or twenty-year return-on-investment case that justifies the cost in many scenarios, particularly for a single-use test. In short, it needs to be low-cost, accurate, robust and fast, things that are frequently in tension with one another.

The clearest examples of widely adopted home-based testing are pregnancy and ovulation tests, glucose meters and HIV self-tests. One thing they share is that their biomarkers are sensitive and specific enough that the device doesn’t need complex sample processing, amplification, or data analysis to deliver a reliable result. However, that hasn’t held for many other indications. Separately, consumer behaviour has shifted: before the pandemic, people were less confident self-testing and leaned more heavily on healthcare professionals, whereas now self-testing is broadly normalised. The technical hurdles remain regardless of that shift, though, with manufacturers needing to find biomarkers with sensitivity and specificity high enough to avoid complex processing altogether, or building devices that can process more demanding samples faster and more cheaply than their laboratory equivalents.

AT: That’s a tricky one. Ideally, you want to base your device on technology that’s scalable and easily manufacturable, but that’s difficult at the early, proof of concept stage. At the outset, there are too many unknowns still shaping the device’s physical structure and function and until its mechanism of action is settled and optimised for the best result, formal design for manufacture work is premature. What typically happens is you select a technology stack, validate it in the lab on a small batch of samples, then once you move to a more diverse sample set and start testing for interference or cross-reactivity, underlying issues with the approach surface. Resolving those issues means tweaking the device, process, or underlying technology, sometimes significantly, to the point where any manufacturing or scaling plans already committed to need to be reassessed. That’s why design-for-manufacturing as a full, systematic process only really makes sense once the concept is validated and the design has stabilised engaging it earlier just means redoing the work as the architecture shifts under you.

The one thing worth checking even at the feasibility stage, though, is whether you’re relying on a niche or exotic component that could bottleneck manufacturing if its supplier disappeared. That’s a coarse, low-cost check you can run early and it gives you a good read on the supplier landscape before committing to anything. If a device can be built from well-established off-the-shelf components, reagents, or materials already produced at volume by multiple manufacturers for other applications and can be validated as fit for use in a medical device, it becomes inherently more scalable without requiring a bespoke, high-investment manufacturing process.

AT: Often the advances we’ve seen are the result of several independent developments converging at the right time, rather than any single technology driving the field forward on its own. The discovery of more sensitive biomarkers has expanded the number of diseases we can detect, while advances in materials science and low-cost, powerful electronics have made it possible to package that detection into something small and cheap enough to manufacture at scale. Automated and passive sample processing has reduced how much a user needs to get right by hand and improvements in data processing mean a result can be interpreted, quantified and even shared automatically rather than left to a person’s own interpretation of it. It’s the combination of these and more that make accurate, rapid home testing achievable in a way it simply wasn’t a decade or two earlier. We’ll see more innovative combinations of these over time and newer technologies will spawn from those combinations in turn.

I can’t say for certain where the next wave will come from, but for users, the core challenge stays the same: several lab-grade techniques need to be combined into a small footprint without losing sensitivity, so people can test confidently and correctly without training. We’ve already seen real progress on the collection side, dried blood spot devices and wearable upper-arm collection patches being two examples, which shows innovation needs to happen across the whole workflow, not just at the point of detection. During the pandemic, people got comfortable handling reagents and mixing samples themselves, which worked, but it introduced ambiguity: a negative result and a mistake in a process step could look identical to the user. Removing that step entirely, moving towards true sample-to-answer devices, would cut that variability and make home testing both easier to use and more robust. That is just one area where improvements will have significant impact on at-home testing.

AT: For healthcare professionals, clinical evidence is one of the most important ways of demonstrating an at-home test’s efficacy. Manufacturers need to run validation studies across a demographically diverse population to show performance holds across different ethnicities and comorbidities. They also need confidence, through human factors testing, that users with varying health literacy and dexterity can perform the test correctly and get a reproducible result, since there’s no way to observe compliance once testing happens unsupervised in someone’s home. On top of that, real-world pilot data is needed to show how the full test-to-treatment pathway performs, not just the test in isolation. If a patient completes the test but has no clear route to follow-up treatment, that’s an added barrier placed on the patient and it undermines the test’s clinical utility within that disease’s care pathway. For patients, confidence is closely tied to acquisition

channel. When a kit comes through an NHS service, a clinic, pharmacy, or another healthcare provider, patients tend to assume that provider has already carried out the necessary due diligence on their behalf. Plenty of tests, though, are bought direct, through a manufacturer’s or distributor’s own site, or via generalist retailers like Amazon. In those cases, people rely on reviews or other social proof that people like them have used the test successfully. That’s where trust is most fragile: when someone tests privately and gets a result that conflicts with what a doctor or lab later confirms, confidence erodes fast and if that pattern repeats at scale, it’s often what precedes a product being withdrawn from market by the regulator, something we’ve seen happen repeatedly with at-home tests in recent years. Once that happens, rebuilding trust is exceptionally difficult.

AT: Partnerships are invaluable in developing new medical technologies, largely because no single organisation holds all the expertise a medical device needs to succeed. A device has to clear several very different hurdles including development, validation, regulatory clearance, market access, adoption and more. Academia, industry, healthcare providers, payers and patient or community organisations each hold a piece of that puzzle and partnering to share knowledge and resources makes the process faster and more inclusive.

This kind of collaboration is also becoming more deeply embedded than it used to be. Where academia-industry partnerships once mostly ran through simple licensing, IP handed over with the university stepping back, it’s now far more common to see risk-sharing throughout development, with both sides working on translational research together to reach clinical use faster and with stronger evidence.

We’ve drawn on support from the NHS, sexual health charities, academic groups, pharmacies and accelerator programmes. At every stage, that’s added to our understanding of distribution and real-world use, how a result translates into a patient accessing treatment and how to design around existing pathways. Ultimately, without these partnerships, we would have to independently rediscover everything the wider ecosystem already knows. This makes the process not only slower, but also more expensive and difficult.

AT: A diagnostic is only the first step in a pathway and its value depends on a treatment existing that can act on the result. Without that, earlier detection has limited clinical utility no matter how advanced the technology behind it. The biggest breakthroughs of the next decade, will be tied to treatments that benefit from earlier detection. The advances themselves are likely to come from technologies that enable earlier disease detection, tighter integration with existing healthcare systems and clinical workflows, the development of risk prediction methods and the expansion of computer-aided diagnosis automating assessments that have traditionally relied on expert human interpretation, just a few areas among others.

Outside of technology and treatment, communities have repeatedly proven to be a decisive factor in the success of testing and early intervention programmes. No matter how advanced the technology, a device with no uptake has limited impact. A significant part of the challenge in medicine is psychological, so expanding access to testing beyond hospitals and clinics, placing it within everyday community settings and pairing that with awareness campaigns, gives people a low-pressure environment in which to raise health concerns and get tested without the weight of feeling unwell or being cast as a patient. So further expansion of community-based healthcare will increase the likelihood of regular testing and represents a significant step towards keeping people healthier for longer.