The healthcare industry is rapidly adopting artificial intelligence (AI) applications like robot-assisted surgeries and medical imaging analysis to improve patient outcomes and reduce costs. However, stringent data privacy regulations limit access to the sensitive patient data required to develop these AI systems. This is where synthetic healthcare data comes in.
Synthetic data is artificially generated to emulate real data. It offers the statistical properties and machine learning model performance of real data without containing any actual sensitive information.
In this blog post, we‘ll explore the major benefits of using synthetic data in healthcare and look at relevant real-world case studies.
Overcoming Healthcare Data Privacy Challenges
Innovating in healthcare using technologies like machine learning requires access to large, high-quality datasets. However, health data is subject to strict privacy rules.
In the US, the Health Insurance Portability and Accountability Act (HIPAA) heavily regulates protected health information (PHI). HIPAA violations can lead to fines up to $1.5 million per incident. The European Union‘s General Data Protection Regulation (GDPR) also limits sharing of patient data.
According to HIPAA Journal, over 40 million healthcare records were improperly disclosed in the past year in the US alone. A survey by Tenable found that 50% of healthcare delivery organizations (HDOs) had at least one data breach in the past year.
Data breaches are increasingly common in healthcare. Source
Breaches and manual data annotation are expensive. IBM estimates the average cost of a data breach is $4.35 million for healthcare organizations. Meanwhile, medical image annotation can cost $100+ per hour according to Labelbox.
Sharing real patient data for research purposes while sufficiently protecting identities is extremely difficult. Synthetic data offers a solution.
How Synthetic Data Benefits Healthcare
Synthetic healthcare data provides the volume and quality needed for AI without violating patient privacy. Some key benefits include:
Improves Machine Learning Model Accuracy
High-quality training data leads to more accurate AI model predictions. Synthetic data expands datasets without privacy risks while reducing overfitting.
For example, a Nature study found that machine learning models predicting brain tumors from MRI scans performed significantly better when trained on a combination of real and synthetic images compared to just real images. Synthetic data improves model robustness.
Adding synthetic MRIs boosted model accuracy. Source
Synthetic data could benefit healthcare applications like surgical robotics, diagnostic imaging, and patient risk analytics. Humana and Optum are major providers using synthetic data to improve their AI systems.
Enables Prediction of Rare Diseases
There is typically little data available for rare diseases. Synthetic data can simulate diverse patient conditions to power clinical trials and predictive models for uncommon illnesses. This is invaluable for developing treatments for diseases affecting small populations.
For instance, a recent study used synthetic EMRs to enable machine learning diagnosis of digital ulcers, a rare systemic sclerosis complication. The synthetic training data led to high predictive accuracy even with limited real examples.
Facilitates Collaboration
Researchers can share synthetic datasets without compromising patient identities. This enables open collaboration between institutions to accelerate projects.
The Medical University of South Carolina and the VA use synthetic data to test natural language processing algorithms for medical coding and mortality predictions without needing to share sensitive real records.
Provides Reproducible Research
Reproducing findings is key to scientific progress. Sharing synthetic data allows researchers to validate results while preserving patient confidentiality.
For instance, Harvard shared over 46,000 synthetic cerebral DSA scans generated by AI to enable reproducible 3D blood vessel reconstruction research.
Enables Debugging AI Systems
Synthetic data can surface corner cases that break AI systems by generating challenging simulated inputs. This aids debugging and improves reliability before deployment.
Alternatives to Synthetic Data
Synthetic data is not a silver bullet. Models built on real-world data or a combination of real and synthetic data can sometimes outperform synthetic-only approaches.
However, real data requires expensive manual annotation. Synthetic data provides a faster, more cost-effective alternative to collecting and labeling large healthcare datasets.
Simple data anonymization also has drawbacks compared to synthetic data. It can remove important patterns and correlations that AI systems need to function optimally. Synthetic data preserves these statistical characteristics.
Case Studies
Here are a few examples of synthetic data in healthcare applications:
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M-Sense: This mobile app helps users understand and manage migraine symptoms. M-Sense provides synthetic user data based on real migraine patients to researchers to enable further scientific study.
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ONC Synthetic Data Project: The Office of the National Coordinator for Health IT is developing an open source synthetic data engine to accelerate research on topics like opioid addiction and pediatric care.
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VA Lighthouse API: The US Department of Veterans Affairs provides researchers access to synthetic veteran health data to study factors impacting care while protecting patient privacy.
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Rare Disease Clinical Trials: Researchers use synthetic patient records to enable machine learning diagnosis of digital ulcers caused by systemic sclerosis.
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NLP Research: The VA and MUSC are collaborating on NLP projects using synthetic data to avoid regulatory hurdles of sharing real records.
Conclusion
Synthetic data unlocks the potential of AI in healthcare. By generating artificial datasets that mimic the properties of real-world health data, synthetic data enhances predictive modeling and collaboration while avoiding regulatory pitfalls.
As the case studies demonstrate, leading medical institutions are already utilizing synthetic data to securely accelerate research and analytics. This trend will only continue as synthetic data generation techniques improve.
To learn more about synthetic data and vendors providing synthetic data services, explore these additional resources:
