Real-world data can help researchers make important decisions about clinical trials. However, that data must be organised and accessible. The volume of data being collected in the healthcare industry is increasing rapidly. If researchers and healthcare organisations can access quality data sets, they could unlock new insights to drive improved health outcomes.
This is especially useful in clinical trials, where pharma companies and researchers actively work toward new treatments. Real-world data (RWD) and real-world evidence (RWE) can help scientists to make important decisions for clinical trials.
Making effective use of RWD also goes beyond clinical trials, as it can help payers, regulatory bodies, and policymakers. However, to access large patient data sets, organisations often need to rely on the cloud for its scalability and flexibility. To use RWD and RWE, it’s important for healthcare stakeholders to understand what they are, how they relate to clinical trials, and how the cloud can support data initiatives to improve patient outcomes.
Real-world data can give researchers a comprehensive and up-to-date understanding of a disease, its treatment, and its impact on patients. This data can then be used to make informed decisions about which patients should be included in a study, which treatments to evaluate, and which outcomes should be monitored. It can also provide insight into how specific treatments are used in clinical practice and impact patient outcomes. Such data can be invaluable for clinical trial planning and design, ensuring that the results from a trial will be relevant to the real world.
Cloud-enhancing data sets for real-world clinical trials
The cloud provides a secure and scalable platform for data storage and analytics. It also offers an expansive, low-cost infrastructure for organisations to store, analyse, and share large datasets. It provides data security, scalability, reliability, and access to powerful analytics tools that can be used to gain insights and make better decisions. Additionally, the cloud can be used to power AI and machine learning applications to improve accuracy and speed in data analysis.
Real-world clinical trial data sets contain a wealth of information about the patients enrolled in the study, the treatments they receive, and their outcomes. Analysing and interpreting these data sets to drive greater clarity, structure, and context is now the need of this hour. Data sets often contain large amounts of data that are difficult to understand, and the relationships between variables are only sometimes transparent. Cloud-enhancing data sets can help bridge this gap.
Many organisations rely on the cloud to bring real-world data together. These real-world data platforms leverage cloud computing technologies such as cloud storage, flexible computing, geographic reach, and AI with machine learning (ML) to bring varied real-world data sources together, which is especially important when it comes to clinical trials. In addition to public databases, researchers or healthcare organisations can create in-house databases or aggregate data from several public databases and apply an artificial intelligence (AI) algorithm to enhance real-world data analysis with fewer mistakes compared with a human. Cloud computing and ML models can help healthcare organisations break down data silos and digest information to be accurate, relevant, and actionable. This allows organisations to focus on patient care while cloud technology automatically normalises, indexes, structures, and analyses the data for them. These data sets can be organised and contextualised to facilitate a better understanding of the data. Rich visualisations and machine learning algorithms can better simplify these data sets. Cloud computing also allows faster and more efficient data processing and analysis. As cloud computing continues to grow, the potential of cloud-enhancing data sets for real-world clinical trials is also increasing. These data sets can provide valuable insights into patient outcomes and treatments, helping improve patient care and facilitate better decision-making.
Looking at the future
Today, we are seeing a wave of healthcare organisations moving to the cloud, enabling researchers to aggregate and harmonise research and development data with information from across the value chain while benefitting from compute and storage options that are more cost-effective than on-premises infrastructure. Cloud-based hyper-scale computing and ML enable organisations to collaborate across data sets, create and leverage global infrastructures to maintain data integrity, and more easily perform ML-based analysis to accelerate discoveries and de-risk candidates faster.
The future of real-world data in healthcare is very promising. Real-world data (RWD) is derived from sources outside the traditional clinical trial setting, such as patient electronic health records, insurance claims, and patient-reported outcomes. It can be used to improve patient care, inform health policies, and demonstrate medical products’ safety and effectiveness. RWD can also provide insights into healthcare costs, utilisation, adherence, and patient outcomes. The data will continue to inform healthcare decisions and improve the quality of care. Additionally, using artificial intelligence (AI) and machine learning (ML) will become increasingly important in analysing RWD, allowing for more accurate and timely insights.