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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.

Synthetic Data for Healthcare and Pharma

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.

Synthetic Data for Healthcare & Farma

Use Cases

Current use cases associated with healthcare and pharmaceutical data, explore the applicability and potential utility of synthetic data in overcoming these challenges.

Accelerate ML efforts to improve medical decision-making

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.

Scale pharma product development by simulating clinical trial scenarios while ensuring quality and compliance

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.

Gain faster access to clinically-relevant data without compromising patient privacy

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.

Connect the healthcare ecosystem and avoid data silos that limit collaboration and insight

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.

Enriching your existing dataset with external complementary information to enhance the outcomes

Enhances predictive modeling and data analytics by providing comprehensive and representative datasets, facilitating insights into disease progression, treatment outcomes, and population health trends.

Simulate biological processes and molecular interactions, aiding in drug discovery and development

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.

Boosting personalized medicine

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.

Regulatory compliance

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.

Addressing data scarcity while reducing bias

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.

Related articles

Check out some of our explanatory articles or cross-industry
use cases to know more

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