The healthcare sector is rapidly shifting from experimenting with synthetic data to large-scale operational use. Approximately 46 percent of healthcare entities and 56 percent of life sciences organizations report using or actively considering synthetic data.
Clinical trials represent one of the most promising use cases. Synthetic data can drastically reduce the number of participants needed for control groups, cutting costs and shrinking enrollment timelines. It also helps to relieve some of the ethical concerns associated with clinical trials.
However, healthcare organizations face significant obstacles when using synthetic data in practice. Organizations need a strong technology partner to help them to prepare real-world data sets and ensure strong security and governance.
The healthcare sector relies on clinical trials as the primary tool for generating evidence-based medicine. They are essential across the entire spectrum of medical care, including testing medical devices, evaluating the safety and success of new procedures, and improving diagnostics.
However, traditional clinical trials face significant operational, financial and ethical challenges that often lead to high failure rates and prolonged timelines. Approximately 80 percent of clinical trials exceed their planned enrollment period. Finding suitable patients is difficult, and many patients drop out due to the high burden of participation and medical risks.
Clinical trials also present ethical and scientific concerns. Using placebos in trials for life-threatening conditions raises ethical dilemmas. High participation burdens often exclude marginalized groups, undermining the generalizability of the results. Trials that fail to recruit enough patients cannot produce statistically meaningful results.
Synthetic data cannot replace clinical trials today, but it is rapidly transforming them. Today, it supports more than 40 percent of AI-powered clinical trials and diagnostic tools. No drug or high-risk medical device has been approved using synthetic data exclusively, but it has become a powerful supplement.
Researchers are using high-fidelity synthetic data to reduce the size of placebo control groups. Virtual control groups can shrink control arms by up to 35 percent. Reducing the control group size can cut enrollment time by up to five months in a trial involving 1,000 patients. It also relieves ethical concerns in studies involving life-threatening conditions.
Synthetic data also helps to solve the small-population problem that has historically made rare disease research difficult. It is used to generate synthetic patient cohorts that mimic rare genetic markers, allowing for statistically meaningful studies when real patient numbers are too low.
However, synthetic data struggles to capture complex, rare and time-dependent clinical relationships. It tends to mimic the average rather than edge cases, such as rare diseases or unique patient comorbidities. Synthetic data may also create “artifactual relationships” (hallucinations), leading to invalid clinical associations.
Synthetic data is only as good as the underlying real-world data, which is often fragmented, noisy or inconsistent. If the source data has biases or gaps, the synthetic data will likely inherit and amplify them. Although designed to be private, synthetic datasets — particularly those generated from small samples — can leak rare or unique characteristics of real individuals.
Currently, no universally accepted standards exist for measuring the quality, utility or privacy risks of synthetic health data. Findings from synthetic studies still need to be validated using real-world data, meaning complete replacement is not feasible.
A technology provider specializing in data governance can help healthcare organizations adopt synthetic data for clinical trials. A qualified provider acts as the bridge between raw medical data and regulator-ready synthetic datasets by ensuring the processes used are transparent, secure and statistically sound.
Technologent’s data specialists have developed an end-to-end framework to help organizations utilize their data more effectively. It helps organizations move from simple data management to advanced practices such as synthetic data creation. Along the path, Technologent helps to implement the tools and processes needed for analytics, security and governance. Let us help you maximize the quality of your source data for generating synthetic datasets.