As the healthcare sector and life sciences further harness the power of data-driven insights, concerns about privacy and data security continue to emerge. Synthetic data, a transformative solution, has arrived as mediator of ethical standards and patient privacy, while ensuring data accuracy and diversity. Keep reading to learn more about synthetic data and how it is working to revolutionise the healthcare sector and life sciences industries.
Preserving patient privacy with synthetic data
Within the healthcare realm, synthetic data offers a valuable opportunity to safeguard ethical standards and patient privacy. Leveraging privacy-related metrics and data generation techniques, synthetic data complements original patient data, preserving confidentiality. In other words, synthetic data replication maintains data features while ensuring privacy in the management of large patient datasets.
Privacy concerns are a major challenge in data sharing and data handling, and biases further hamper the usability of real data. Synthetic data presents a cost-efficient solution to these challenges. ADC leverages both real-world and synthetic data to improve clinical research, employing virtual control groups for informed decision-making. This approach proves valuable in cases lacking control therapy or evidence of patient survival.
The process involves feeding data into a synthetic data engine, essentially a state-of-the-art deep learning model skilled at capturing intricate relationships and correlations among variables, ensuring data integrity extends well beyond individual data points.
Mitigating data breach risks
Data breaches are a threat that should not be handled lightly, particularly when dealing with sensitive information. Synthetic data, however, generates entirely new data points rather than duplicating existing data, a crucial defense mechanism. Rigorous metrics further ensure that each generated data point maintains a safe distance from its original counterpart.
Moreover, measures are in place to guarantee the realism and fidelity of synthetic data while avoiding any semblance to individual patient information. For instance, the BlueGen platform provides a comprehensive report containing five privacy-related metrics, safeguarding against any unwanted similarities to the original dataset. Read more about how a group of ADC Consultants tested the BlueGen platform.
The evolving landscape of synthetic data
The future of synthetic data in healthcare and life sciences appears promising. Expectations of widespread adoption are not confined to healthcare alone but extend to sectors like financial and public services.
Gartner estimates that by 2025, the use of synthetic data and transfer learning will reduce the volume of real data needed for machine learning by 70%. Furthermore, synthetic data will reduce personal customer data collection, avoiding 70% of privacy violation sanctions.
Synthetic data presents a safer alternative to working with production data, such as when safeguarding sensitive clinical trial information. This newfound accessibility enables strategic planning for clinical trials and data analysis, among other critical applications. While adoption is on the rise, realising its full potential may take time. Nevertheless, the market is expanding rapidly, revealing numerous valuable use cases.
Real-world applications: synthetic data in action
Two use cases underscore the efficacy of synthetic data in healthcare and life sciences:
- Accelerating clinical trials: By enabling early data generation for clinical trials akin to previous ones, it eliminates waiting periods for data collection, empowering data visualisation experts and data scientists to create preliminary reports. This approach not only accelerates clinical trials but also reduces costs, expediting product availability. Implementation options include platforms like BlueGen and open-source Python packages, as shown in the Novo Nordisk Digital Patients case study. The latter is ideal for generating data following specific distributions, expediting the writing process without exposing real data.
- Leveraging collaborative data: Synthetic data empowers healthcare institutions to collaborate across regions and countries, creating a robust and unbiased patient data pool. This collaborative approach yields more accurate research findings, strengthening conclusions and fostering the creation of global healthcare databases. These databases inform policies, benefit pharmaceutical companies, enhance healthcare cooperation, and drive innovative treatments.
Overcoming reservations to recognise potential impact
Reluctance to embrace synthetic data often arises from a lack of awareness. Universities and academic hospitals, for example, grapple with data sharing barriers, hindering effective collaboration. The solution lies in recognising the transformative potential of synthetic data technology in expediting processes. Initiating small, low-cost experiments to explore possibilities is a great entryway and can help overcome reservations.
In contrast, pharmaceutical companies, equipped with greater resources, are better positioned to experiment with synthetic data. They can leverage their data science or data engineering teams to collaborate with companies like ADC, conducting tests to assess utility, process, and associated risks. Embracing such experimentation can lead to breakthroughs in data sharing and analysis within these institutions.