As the world of healthcare becomes more digitalized, Electronic Health Records (EHRs) have emerged as an invaluable source of data for clinical decision-making and healthcare research. EHRs form the digital backbone of today’s healthcare industry. Traditional EHRs serve as virtual repositories of patient data, collected over time from multiple healthcare providers. These records enhance care coordination, provide clinical decision support, and enable patients to actively participate in their care. Yet, despite their advantages, traditional EHRs present challenges in their usage and analysis.
The sheer volume of traditional EHRs, which can encompass millions of patient records, each with thousands of data points presents a significant challenge in data analysis. Along with the complexity and size of these records, there’s an issue of ‘noise’ and incompleteness, meaning extraneous or missing data which can hinder the identification of patterns and trends. This vast, complex, and often inconsistent nature of EHRs makes traditional statistical analysis cumbersome and often ineffective.
Given these challenges, the health industry is exploring innovative solutions – one of which is Synthetic Electronic Health Records.
Synthetic EHRs are a novel approach to health data analysis, leveraging machine learning to emulate the statistical patterns found in real EHR data. Picture them as doppelgängers of real EHRs, mirroring their patterns, but not their individual, privacy-sensitive data points. They provide a complete and accurate dataset that can help in pattern detection and trend analysis in ways that traditional EHRs can’t.
In the original research paper, Variational Graph Autoencoders (VGAEs), a type of neural network (a computational model that’s capable of learning patterns and making decisions based on the data they process, much like how a human brain would.), were used to generate synthetic EHRs. VGAEs effectively capture complex dependencies between different medical events in EHRs, allowing the generation of synthetic EHRs that are realistic, privacy-preserving, and scalable. To illustrate, from a dataset of 100 real EHRs, the model could generate 10,000 synthetic EHRs, enhancing the breadth of the dataset while maintaining confidentiality.
Contrasting traditional and synthetic EHRs
Challenges and recommendations for synthetic EHRs
Quality and volume of original data: The quality of synthetic EHRs highly depends on the quality and volume of the original data. The more data, the better the model can learn and generate improved patient trajectories.
- To improve the quality of synthetic EHRs, models should be trained on larger and more diverse datasets. This can help the model better learn and generate improved patient trajectories.
Binary classifier limitation: The current model assumes that encounter nodes are connected to only one medication node. Imagine, during a doctor’s visit (or what’s referred to as an ‘encounter’), a patient may be prescribed more than one medication. However, the current model simplifies this and assumes that during each visit, a patient is prescribed only a single medication. This is a limitation since in real-world scenarios, one encounter may involve multiple medications.
- To account for the possibility of one encounter involving multiple medications, the binary classifier could be replaced with a module that can account for multiplicity.
Potential bias: Synthetic EHRs can potentially be biased if they are generated from a non-representative sample of real EHRs. This can lead to inaccurate representations and demographic misrepresentation.
- To avoid demographic misrepresentation, it’s important that the original data is representative of the population. Using an extensive and inclusive dataset can help ensure that synthetic EHRs aren’t biased.
Computational expense: The process of generating synthetic EHRs is computationally expensive and might be a limiting factor for organizations with limited resources.
- To mitigate the computational expense associated with generating synthetic EHRs, organizations could adopt cloud computing. This approach would help reduce the cost of generating synthetic EHRs and make the process more feasible for a wider range of organizations.
In conclusion, the advent of synthetic EHRs could open up a world of potential for healthcare research and delivery. By providing a privacy-preserving way to generate detailed and realistic patient data, synthetic EHRs could revolutionize the way we train machine learning models and analyze healthcare trends.
However, to truly leverage this potential, we must also focus on scaling these technologies, ensuring inclusivity, and maintaining data accuracy. When properly executed, synthetic EHRs could play a pivotal role in fostering global health equity. By providing healthcare researchers and professionals with more accurate and comprehensive data, we could better understand and address healthcare disparities around the world, ultimately leading to more equitable health outcomes for all. This is just the beginning – the future of healthcare could be synthetic, and data-driven. This holds immense promise for global health equity.
As we stand at the brink of a new era in healthcare, we must ask ourselves: How can we leverage these synthetic Electronic Health Records to maximize global health equity?
Source – https://www.nature.com/articles/s41746-023-00822-x
