Synthetic Data for Healthcare: Benefits & Case Studies in 2024

Synthetic data has the power to transform healthcare by enabling innovation in artificial intelligence while still protecting patient privacy. As a data analyst with experience applying AI in the medical field, I often get asked – what exactly is synthetic healthcare data, and why does it matter?

In this comprehensive guide, I‘ll explain what synthetic data is, why healthcare urgently needs it, and how it can drive breakthroughs in areas ranging from rare disease research to personalized medicine. I‘ll also dive into real-world examples and compare techniques to generate synthetic information. Let‘s explore!

What is Synthetic Data and Why Does Healthcare Need It?

Synthetic data is artificial information generated by statistical models and AI algorithms to mimic real-world data, without including any actual sensitive patient information. Think of it like a synthetic oil – it has the same properties and performance as the real thing, but is created artificially.

Healthcare organizations have massive troves of patient data that could enable transformative AI applications. But legal safeguards like HIPAA strictly regulate the sharing of medical data to protect patient privacy.

As just one example, a 2021 report found over 40 million healthcare records were improperly disclosed in the US between 2020-2021 alone. High-profile breaches like these demonstrate why data privacy is so critical in healthcare.

At the same time, strict privacy regulations can stymy progress on healthcare AI and prevent collaboration. This landscape makes advancing healthcare technology incredibly challenging.

Synthetic health data provides a path to balance innovation with privacy. By generating artificial records, researchers and companies can develop and share anonymized data to power healthcare AI.

Key Techniques to Generate Synthetic Data

There are a few primary techniques used today to create synthetic health data:

Generative adversarial networks (GANs) – GANs use two neural networks that compete against each other to generate increasingly realistic synthetic data. GANs can produce high-quality synthetic images, time-series data, and more.

Variational autoencoders (VAEs) – VAEs are a form of neural network that learns to encode data into a latent space and then decode it again. The synthesized outputs can serve as artificial data.

Simulations – Sophisticated simulations of biological systems and disease progression can produce synthetic patient trajectories over time.

Differential privacy – Algorithms are added to analyses of real data to inject controlled noise. This allows statistics to be shared while protecting patient identities.

Each approach has pros and cons. For example, GANs can struggle with rare data patterns but produce highly realistic outputs. Simulations require extensive expert insights to design. And differentially private data retains biases and flaws from the original dataset.

Understanding these key differences allows data scientists to select the optimal synthetic data generation technique for their specific healthcare use case.

The Benefits of Using Synthetic Data in Healthcare

Now that we‘ve covered the basics of what synthetic medical data is and how it‘s made, let‘s explore some of the key benefits driving adoption:

Improves Machine Learning Model Accuracy

High-quality training data is the fuel that powers accurate AI models. Synthetic health records can be used to significantly increase the size of training datasets for machine learning algorithms, without needing more real-world medical data. This amplification effect improves model performance and generalizability.

In one study, researchers found that combining real ICU data with progressive amounts of synthetic data led to increasing improvements in AI model performance. The synthetic data helped the algorithm generalize and reduced overfitting.

Enables Research on Rare Diseases

It‘s incredibly challenging to build prediction models for rare diseases when there are only a handful of real patient cases globally. Synthetic data can be used to generate hundreds or thousands of artificial patient records to reach the sample sizes needed for proper clinical trials and research studies.

For example, researchers used synthetic data to expand limited datasets of patients with primary adrenal insufficiency and test AI models for monitoring glucocorticoid replacement therapy. The synthetic data was used to simulate additional patients and validating the models [1].

Allows Collaboration While Protecting Privacy

Pharmaceutical researchers could collaborate with university hospitals if there was a way to safely analyze data across institutions. Synthetic healthcare information enables this cross-organizational analysis and collaboration while still protecting patient confidentiality.

Partners can develop core models on their internal real data, and then share synthetic versions of their datasets with others. This powers meta-analyses and pooling of insights across massive synthesized populations.

Provides Reproducibility for Medical Research

Being able to reproduce the results of an experiment or analysis is a key tenet of effective scientific research. But sharing real medical records between institutions to validate findings is often not possible due to regulations on protected health information (PHI).

Synthetic data provides a path to resolve this by enabling researchers to publish shareable artificial datasets along with their analyses. Other scientists can then rerun the same experiments on the published synthetic data to validate the results.

Streamlines Regulatory Approvals

The FDA and other regulatory bodies have strict requirements around the use of real-world data in clinical trials and regulatory submissions. Deidentifying data to make it anonymous is challenging without stripping away critical information.

Synthetic data offers a way to meet regulatory requirements for removing identifying patient information. Researchers can generate artificial trial datasets that retain necessary statistical properties while fully protecting patient privacy.

Comparing Approaches: Real Data vs. Synthetic vs. Hybrid

Now that we‘ve covered the potential of synthetic healthcare data, how does it compare to other approaches? Here‘s a high-level comparison:

• Real data provides maximum accuracy but very limited availability.
• Synthetic data is widely accessible but less accurate than real-world data.
• Hybrid approaches combine the strengths – using real data where possible plus synthetic for additional scale.

So while synthetic health information enables many new possibilities, it is not a magic bullet. In many cases, algorithms built on real datasets or a blend of real and synthetic data will outperform synthetic-only approaches.

Synthetic Data Use Cases Across Healthcare

Synthetic medical data is quickly moving from theoretical concept to real-world implementation across healthcare. Here are just a few examples of synthetic data in action:

Medical Imaging & Diagnostics

• Researchers in China published an open-source synthetic CT scan dataset to enable development of AI Diagnosis models while protecting patient privacy.
• MIT scientists created an AI model called GANPlan that generates synthetic cancer patient CT scans. This enables researchers to test personalized radiation therapy planning at scale.

Drug Discovery Research

• Startup Insilico Medicine uses generative networks called Chemistry42 to design novel molecular structures as drug candidates. This allows synthesizing promising compounds before lab trials.
• Researchers published a paper demonstrating the use of synthetic data to enable collaboration in early stage pharma R&D without sharing proprietary datasets.

Population Health Studies

• Public health experts created the Synthetic Massachusetts model to enable open population health analysis without real patient data. Researchers have used this to study opioid addiction treatment across simulated communities.
• A COVID-19 simulation model called SimCity19 uses synthetic populations, environments, behaviors and interventions to study pandemic response. This helps safeguard privacy while informing public health strategy.

Regulatory Applications

• The Medical Device Epidemiology Network initiative (MDEpiNet) developed the SyntheaTM data synthesizer to create artificial patient records for medical product safety surveillance.
• Insilico Medicine worked with the FDA to safely generate regulatory submissions featuring synthetic data for novel drug candidates.

Key Takeaways on Synthetic Healthcare Data

The rapid growth of synthetic medical data demonstrates its vast potential to propel healthcare innovation while protecting sensitive patient information:

• Synthetic data techniques allow generating artificial records that mimic real-world statistical properties without including PHI.
• Key benefits include improving AI model accuracy, enabling rare disease research, supporting collaboration, and facilitating reproducibility.
• Leading techniques include GANs, VAEs, simulations, and differential privacy, each with pros and cons.
• Synthetic data powers real-world advances in areas like medical imaging, clinical trials, population studies, and pharma R&D.
• Hybrid approaches combining real and synthetic data provide a robust overall strategy.

While not a magic solution, synthetic health information can provide invaluable benefits ranging from life-saving clinical insights to reproducible medical research. I hope this guide provided a comprehensive overview of this transformative technology – please reach out if you want to discuss more!