1. What is Synthetic Data?
Synthetic data is artificially generated data that mimics real data's statistical properties and patterns. It is generated through advanced generative AI models like GANs, LLMs, or DGMs, which generate real-like synthetic data by learning from real data. But why generate synthetic healthcare data that works like real data? Because, Real healthcare data contains sensitive patient information, making it impossible to fully use or share without compromising patient data privacy. RWD is often biased, imbalanced, and incomplete, affecting the accuracy of results. Real data is also expensive and time-intensive to collect, clean, and anonymize, affecting turnaround times for tasks that could be done in days instead to months. Synthetic healthcare data, unlike real-world data, contains no Personally Identifiable Information (PII). This means you can safely use and share synthetic medical data, internally and externally, without worrying about data breaches or breaking privacy laws. But that’s not all—it can improve the quality of your training datasets. For example, it can eliminate prejudices related to race or gender, correct imbalances such as an anomalous increase in income that skews the average in population studies and ensure equal representation, like balancing the ratio of men and women in datasets used for training hiring algorithms.The trade-off between data privacy and utility is a significant challenge for enterprises, research institutions, and healthcare providers, all of which need to protect sensitive information, like customer or patient data. Synthetic data, especially when supported by differential privacy guarantees is privacy-compliant, as it cannot be traced back to real individuals, while still offering high utility. This makes it a cost-effective and faster alternative for applications in machine learning, AI, data analysis, and beyond.This of course is an oversimplification of what synthetic data has to offer. To dive deeper into its benefits and explore real-world use cases, check out our comprehensive guide on synthetic data.
2. Differential Privacy in Synthetic Data Generation:
Differential privacy was developed to address the failures of traditional de-identification methods which contain re-identification risks. Differential privacy protects individual data by adding controlled randomness, often using distributions like the Laplace distribution. The level of privacy is governed by the epsilon (ε) parameter, with smaller values providing stronger privacy. It ensures that analyses yield nearly the same results whether or not an individual’s data is included, maintaining privacy while allowing statistical validity.There are two types: central differential privacy (CDP), where noise is added post-collection, and local differential privacy (LDP), where noise is added at the individual level before collection. Its composition property manages cumulative privacy loss across multiple analyses, and its future-proof design ensures privacy even with external data. By "blurring" data, differential privacy allows accurate population insights while safeguarding individual information, offering robust privacy guarantees for synthetic medical data.
3. Challenges in Medical Data Analysis:
Data has always been a catalyst and a bottleneck for innovation in the healthcare industry. With little to no margin of error since human lives are at stake, medical professionals, researchers, and enterprises struggle to access data for two main reasons. It is very expensive to collect or license such data. As per estimates collecting data from a single patient in a clinical trial can cost upward of $20,000 while licensing real-world data can cost $100,000 into the millions of dollars. And it is very hard to work it. Second, Real data requires constant privacy reviews and audits, and, anonymization and de-anonymization at different levels.
a. Privacy concerns in real medical datasets:
Medical datasets inherently contain sensitive patient information, making their use in research and AI development difficult with concerns about confidentiality and misuse. Without proper data protection measures, patient data could be exposed, leading to violations of trust, reputational harm to institutions, and potential legal consequences. These concerns create barriers to accessing the vast amounts of data necessary for advancing healthcare technologies.
b. Legal and ethical barriers:
Stringent data protection regulations like the Health Insurance Portability and Accountability Act (HIPAA) in the U.S., the General Data Protection Regulation (GDPR) in Europe, and the Personal Data Protection Commission in Singapore are designed to protect personal information. While necessary for protecting patient rights, these laws impose restrictions on data sharing and limit the ability of researchers to access and use real-world medical data freely delaying critical research efforts.
c. Limitations in accessing diverse and large-scale datasets:
Acquiring datasets that have diverse populations and conditions is an ongoing challenge in medical research. Institutional silos, patient confidentiality concerns, and the fragmented nature of healthcare data systems restrict access to large-scale, representative datasets. This lack of diversity affects the development of AI models capable of addressing the needs of underrepresented populations.
d. Edge Cases and Rare Scenarios:
Real-world datasets often lack sufficient examples of rare diseases or unusual clinical presentations, creating gaps in AI model training. This scarcity of data can lead to underperforming models in scenarios involving rare conditions or edge cases. Addressing these gaps is crucial for improving diagnostic accuracy and treatment outcomes for less common medical conditions.
4. Advantages of Using Synthetic Healthcare Data:
Synthetic healthcare data provides a lot more expandability, flexibility, and security when working in healthcare, especially in research and AI development. Research into generating synthetic medical data for healthcare identified 45 different motivations for generating synthetic data, resulting in five main categories: data privacy and security, data scarcity, data quality, AI development, and direct medical and clinical applications.
a. Enhanced Privacy Protection:
Synthetic data eliminates direct identifiers by generating artificial datasets that mimic the statistical properties of real data. This approach ensures that sensitive patient information remains secure while enabling researchers to perform analyses without compromising privacy. By reducing reliance on real-world data, synthetic data offers a robust solution to longstanding privacy concerns.
b. Increased Data Accessibility:
Synthetic data bypasses privacy constraints, allowing researchers and institutions to share datasets more freely. This increased accessibility fosters collaboration across geographical and institutional boundaries, empowering researchers to work with datasets that were previously unavailable or heavily restricted.
c. Scalability and Customization:
Unlike real data, synthetic data can be generated at scale and customized to simulate specific conditions, demographics, or scenarios. This flexibility allows researchers to tailor datasets to their exact needs, supporting more comprehensive and targeted studies without the logistical challenges of acquiring real-world data.
d. Mitigation of Bias:
Real-world medical data often reflects systemic biases, such as the under-representation of certain groups. Synthetic data generation can actively address these biases by creating balanced datasets that ensure fairness in AI model development. This capability is critical for building equitable healthcare technologies that perform reliably across diverse populations.
e. Cost-Effectiveness:
The collection, storage, and management of real-world medical data are resource-intensive processes. Synthetic data offers a more cost-effective alternative by enabling researchers to generate datasets on demand, reducing the financial and logistical burdens of traditional data acquisition methods.
f. Enhanced Machine Learning Model Training:
Synthetic data allows for robust model training by providing diverse, well-labeled datasets that simulate real-world complexities. This helps AI systems learn more effectively and generalize better across various conditions, improving their accuracy and reliability in real-world applications.
g. Faster Experimentation and Research Cycles:
With synthetic data, researchers can bypass lengthy data collection and approval processes, accelerating experimentation and innovation. This expedited timeline is particularly beneficial for time-sensitive studies, enabling faster iterations and quicker validation of hypotheses.
h. Facilitates Open Science and Data Sharing:
Synthetic EHR data removes privacy risks, encouraging open sharing of datasets among researchers and institutions. This openness fosters a collaborative research environment, promotes transparency, and accelerates the pace of scientific discovery in medical fields.
i. Support for Rare Disease Research:
Rare diseases often lack sufficient data for meaningful analysis or AI model training. Synthetic patient data generation fills this gap by simulating realistic datasets for these conditions, empowering researchers to study rare diseases more effectively and develop tailored interventions for affected patients.
5. Use Cases of Synthetic Data in Medical Research and Life Sciences:
a. Clinical Trial Simulation:
Synthetic patient data can simulate patient populations, treatment responses, and disease progression, enabling researchers to test clinical trial designs before implementation. This reduces costs, optimizes resource allocation, and improves the likelihood of trial success by identifying potential challenges in advance.
b. Disease Risk Prediction Models:
By generating diverse datasets that include underrepresented populations and rare conditions, synthetic data enhances the development of disease risk prediction models. These models can better identify individuals at risk for specific diseases, leading to earlier intervention and improved patient outcomes.
c. Drug Development and Testing:
In drug discovery, synthetic data accelerates the testing of drug efficacy and safety by simulating patient responses to new treatments. This approach reduces reliance on real-world data, shortens development timelines, and lowers the costs associated with early-stage research.
d. Epidemiological Studies:
Synthetic data enables researchers to model disease outbreaks, transmission patterns, and population health trends without accessing sensitive real-world datasets. These simulations support public health initiatives, inform policy decisions, and enhance preparedness for future pandemics.
e. Health Policy and Resource Planning:
Governments and healthcare organizations use synthetic data to evaluate the potential impact of health policies and allocate resources effectively. By modeling various scenarios, decision-makers can optimize healthcare delivery, reduce costs, and improve patient outcomes at a systemic level.
f. Rare Disease Research:
Real-world datasets often lack sufficient cases of rare diseases for meaningful analysis. Synthetic data bridges this gap by creating realistic simulations of rare disease cases, empowering researchers to study these conditions and develop targeted therapies and diagnostic tools.
g. Medical Device and AI Validation:
Testing medical devices and AI algorithms on synthetic data ensures rigorous validation while maintaining patient privacy. This approach supports regulatory compliance and enhances confidence in the reliability of these technologies before their deployment in real-world settings.
h. Training and Education:
Synthetic datasets provide safe and realistic environments for training medical professionals, AI developers, and researchers. These datasets simulate complex medical scenarios, enabling hands-on learning without ethical concerns or risks to patient safety.
i. Clinical Trial Design Optimization:
Synthetic data can be used to optimize clinical trial designs by modeling different trial structures, patient recruitment strategies, and treatment regimens. This enables researchers to predict trial outcomes and minimize the risk of failure before real-world implementation.
j. Health Insurance Analytics:
Synthetic data aids health insurers in developing predictive models for risk assessment, fraud detection, and cost optimization. It allows for comprehensive analysis without exposing sensitive patient information, ensuring compliance with data privacy regulations.
6. Examples of Synthetic Health Datasets taken from the National Library of Medicine
View complete research here.
7. Summarizing Synthetic Medical Data for Medical Research and Insights:
Synthetic data generation is revolutionizing the healthcare industry by addressing data scarcity, privacy concerns, and biases in medical datasets. It lowers the cost and time needed for clinical trials, especially for rare diseases, and enhances the predictive accuracy of AI models in personalized medicine. Synthetic data ensures equitable treatment recommendations by representing diverse patient populations and provides researchers with high-quality datasets without compromising patient privacy. Increasing data volume and excluding Personally Identifiable Information (PII), allows for safe experimentation and accelerates drug discovery through simulated clinical trials. Furthermore, it mitigates biases in areas like age, race, and gender, enabling more generalizable and fair AI applications in healthcare used for research and analysis.
8. Synthetic Medical Tabular Data with Betterdata’s Synthetic Data Engine:
Our SOTA models are trained to generate high-quality and representative synthetic data that replicates real data's statistical properties and underlying patterns, ensuring that the generated data meets high standards of fidelity, privacy, and usability.
Our models prioritize creating synthetic medical data that performs similarly to real data in ML tasks:
a. Model Testing: Training separate ML models on synthetic data and real data. Then, evaluating them both on unseen test real data.
b. Metrics: Testing ML model performance on accuracy, precision, recall, and ROC AUC scores.
In our tests, synthetic data achieved a mean ML performance score of 0.9731, demonstrating its effectiveness for model training and validation. This means synthetic data works as well as real data for ML.
The fidelity score measures how well the synthetic data replicates the statistical properties and patterns of the original (real) data. High fidelity in synthetic data ensures that the data retains the same distribution as the original, without directly copying any specific data points.
Correlation graphs represent the relationships between features in a dataset. They are particularly useful for identifying patterns, trends, and the strength of relationships between variables. Our report shows a high correlation between real and synthetic data meaning that the underlying relationships are well preserved in synthetic data.
Cosine similarity is a distance-based metric used to measure the similarity between two datasets, i.e., the synthetic dataset with the real dataset in this case. It shows how closely synthetic data records match real data records. A score of 0 for one synthetic patient record means that the same patient record also exists in the real dataset. This is considered a privacy breach and by default, Betterdata’s engine sets this threshold to greater than zero. Furthermore, a score of 1 for any synthetic record means that it does not resemble any real data record at all and is statistically not representative. Depending on the business requirements, such synthetic records may or may not be useful, but from a privacy perspective, it must be ensured that no synthetic record has a distance of 0.
