How AI and Digital Innovation Are Reshaping Clinical Trials in HealthTech

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How AI and Digital Innovation Are Reshaping Clinical Trials in HealthTech

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Across many parts of Africa, clinical research still depends heavily on paper-based workflows. For pharmaceutical companies managing global trial portfolios, this can create operational challenges around data quality, monitoring, and regulatory readiness, particularly when studies contribute to submissions in the US and Europe.Adriaan Kruger, founder and CEO of nuvoteQ has spent twelve years building the infrastructure to close that gap. His conversation with Trisha Pillay on Digital Pulse is one of the more candid accounts available of what digital adoption in clinical research actually looks like when the constraints are real.Why Paper Persists and What It Actually CostsThe persistence of paper in LMIC clinical research settings is not a technology problem. The technology exists."Our industry is so hesitant and so resistant to change," he says. "They are so risk-averse — which we totally get in this highly regulated world. These researchers are so worried about potentially using a system and then the data gets lost, or the system is down. So they go back to their tried and tested way of doing things, which is on paper."The financial dimension compounds the risk-aversion. Funding does not flow to research sites in sufficient volumes to support digital infrastructure investment. The result is double data entry, a risk mitigation practice where patient data recorded on paper is manually typed into a centralised system twice by different operators, then compared for discrepancies. In developed markets, this practice has largely been eliminated. Across much of Africa, it remains the norm.The downstream cost is significant as data science teams spend extensive time converting inconsistent paper records, Excel files, and incorrectly formatted entries into globally standardised formats before a single submission can be filed.Real-Time Visibility Changes the Risk CalculationThe operational case for digital infrastructure is sharpest when Kruger describes what real-time data visibility actually enables at the trial level. Clinical trials running across multiple countries like South Africa, Mozambique, Ghana, Finland, and Australia simultaneously have historically operated with delayed information flows. A serious adverse event occurring overnight in South America might take days to trigger a coordinated global response. The pharmaceutical company, the investigators, and the data safety monitoring board are all operating on different information timelines."If they don't have real-time data, often there's a delayed reaction on how you deal with things," Kruger says. "Now if they have a real-time view of everything happening globally, they can make insightful decisions, let's pivot, let's change the dosage, or there was a serious adverse event, it happened last night in South America. The morning we wake up, everyone knows."For pharma executives managing trial costs, trials are expensive precisely because they run long, across many sites, with significant overhead. The ability to make earlier decisions about dosage adjustment, site performance, or trial termination is a direct cost lever. "You can make decisions on whether to move faster, change direction, or potentially stop a study so that you don't spend more money on it."AI Where It Works in Clinical ResearchKruger's position on AI in clinical research is one of the more technically grounded takes in the current conversation. nuvoteQ is not AI-averse, but the firm is precise about where AI delivers and where the industry's enthusiasm is running ahead of its readiness.The clearest proof point is a regulatory review tool built for South Africa's medicines regulator, SAHPRA, in partnership with a Seattle-based philanthropic funder. The challenge was a generic drug dossier, submissions that can run to 15,000 pages, were sitting in regulatory queues for up to nine years across Africa, delaying the availability of affordable medicines for the patients who need them most.The tech company deployed an open-source LLM locally within SAHPRA's data centre, with no external internet access, trained to analyse dossiers and produce a two-page risk summary with hyperlinked references to the source document. "That has reduced the timeline to review from where it typically takes five to six weeks. We can do it in about 45 minutes now."The model works because the data never leaves a closed environment, the task is well-defined and repetitive, and human reviewers remain in the loop for final decisions. That last point is deliberate. "You will always have a human in the loop. There will always be a healthcare professional. AI will help do some of the number crunching, but ultimately, when it comes to patient care, the ethical component is how patients are being treated. I don't think AI is going to be there for a long time."The liability question Kruger raises for clinical AI tools mirrors what is emerging elsewhere in regulated industries: the responsibility for an AI-assisted...

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