To drive innovation and boost data solutions in healthcare and pharmaceuticals industries, access to unbiased, statistically significant data is crucial. However, utilizing sensitive patient data poses privacy, breach, and compliance risks. Synthetic data offers a solution by providing secure alternatives that support research and analysis while safeguarding patient privacy and meeting regulatory requirements.
The challenges associated with healthcare and
pharmaceutical data are multi-faceted and require
comprehensive solutions. Addressing privacy and
security concerns, fragmentation and
interoperability issues, and empowering the use of
accurate and quality data are needed.
Synthetic data holds vast potential in healthcare
and pharmaceutical industries, impacting areas such
as diagnostics, disease management, wearables for
early detection, drug research, clinical decisions,
staffing, hospital occupancy, healthcare costs, and
end-of-life care. It serves as a powerful resource
for advancing medical research, breakthroughs, and
patient care.
According to McKinsey's survey published in 2023, a
lack of high-quality, integrated healthcare data
platforms is the main challenge cited by medtech and
pharma leaders as the reason behind the lagging
digital performance. As much as 45 % of these
companies' tech investments go to applied artificial
intelligence, industrialized machine learning and
cloud computing - none of which can be realized
without meaningful data access.
Synthetic data has become an ideal solution as it
can enable accessibility to privacy-compliant data.
Access to quality data can help to enhance the
quality of patient care through machine learning
modeling and artifical intelligence, decreases
expenses, and fosters opportunities for
collaboration and partnerships.
Current use cases associated with healthcare and pharmaceutical data, explore the applicability and potential utility of synthetic data in overcoming these challenges.
Machine Learning models are critical in improving healthcare and pharma operations, as they are often used for diagnosis and treatment. Synthetic data provides a reliable and unbiased data source for training Machine Learning models, enabling healthcare and pharma companies to make more accurate predictions and decisions based on original clinical data. With access to secure high-quality data, healthcare and pharma companies leverage AI models such as drug-target interaction prediction and biomarker discovery, to improve patient outcomes and reduce costs.
To remain competitive, healthcare and pharma companies must constantly innovate, develop new products, and run clinical trials. Synthetic data generation create large, representative datasets that accurately represent the patient population and information, facilitating that healthcare and pharma companies run faster and more accurate clinical trial research by simulating clinical trial scenarios, including patient recruitment, treatment response, and adverse events. This helps to improve the efficiency and accuracy of clinical trials by optimizing study design, protocol development, and decision-making processes, ultimately reducing costs and time to market.
Managing data governance and privacy practices slow down access to healthcare data, delaying important research, innovation and analysis efforts. Synthetic data provides a fast, easy way to create large secure and anonymized datasets that are statistically similar to original patient data and comply with strict privacy regulations. It allows healthcare and pharma companies to accelerate their research, innovation and analysis while protecting sensitive patient information.
Data silos and regulatory compliance barriers limit collaboration and prevent healthcare professionals from gaining a complete understanding of patients' needs and experiences. Synthetic data provides healthcare and pharma companies with the datasets they need to safely share sensitive patient data across the healthcare ecosystem without barriers and compliance limitations.
Enhances predictive modeling and data analytics by providing comprehensive and representative datasets, facilitating insights into disease progression, treatment outcomes, and population health trends.
Accelerate the identification of potential drug candidates by providing a vast dataset for testing, reducing the time and cost associated with traditional methods. Expedites the drug discovery process by predicting drug efficacy, toxicity, and off-target effects, leading to the identification of novel therapeutic interventions with lower development costs.
Synthetic data can model patient populations with diverse genetic backgrounds, lifestyle factors, and medical histories, enabling the development of personalized treatment strategies, leading to more effective and targeted therapies.
Synthetic data is compliant for regulatory submissions and compliance testing, ensuring data privacy and security. It mitigates risks associated with handling sensitive patient data, while still allowing for robust testing and validation of algorithms and systems in a secure environment.
It also addresses data scarcity and bias issues by generating diverse and representative datasets, improving the performance and generalizability of AI models in biotech and pharma applications. This helps to be compliant with ethical AI guidelines.
Check out some of our explanatory articles or
cross-industry
use cases to know more
We use third-party web analytics tools to analyze website usage and measure the success of advertising campaigns. Further details can be found in our privacy policy.
We use cookies to analyze and improve our service to you. We trat maintaining you privacy seriously. By clicking "Accept All", you consent to our use of cookies.
Read our cookie policy.Sticly necessary
These are required for essential site functionality or our legal compliance
Performance & analytics
These track site usage anonymously and contain no personal information
Marketing & advertising
These help us track our marketing activity and improve the service we offer to you
