According to McCurdy, another benefit of using cloud technology is the ability to analyze data and break down silos, making it easier to use AI and ML to address interoperability challenges.
Common challenges include the local storage of large amounts of data from research equipment such as microscopes and spectrometers, which creates a barrier for archiving, processing and sharing data securely, says McCurdy. In addition to that data, there’s been an influx of data from sensors, mobile devices and medical devices in recent years. Overcoming these challenges has the potential to advance precision medicine and improve patient outcomes.
“Most healthcare data is frequently incomplete and inconsistent. It’s often unstructured and stored in disparate formats such as clinical notes, lab reports, insurance claims, medical images, recorded conversations and time-series data. This makes it incredibly challenging for organizations to process, extract information and analyze at scale,” he says. “In the next decade, making sense of all this data will provide the biggest opportunity to transform care. However, this transformation will primarily depend on data flowing where it needs to at the right time, all while being processed in a way that is secure and protects patients’ private information.”
Cloud computing and ML models can help healthcare organizations break down data silos and digest information to be accurate, relevant and actionable. This allows organizations to focus on patient care while the cloud technology automatically normalizes, indexes, structures and analyzes the data for them, says McCurdy.
“Today, we are seeing a wave of health care organizations moving to the cloud, which is enabling researchers to aggregate and harmonize 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,” he says. “Cloud-based hyperscale computing and ML enable organizations 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.”
How Does Real-World Evidence Speed Up Clinical Trials?
According to the U.S. Food and Drug Administration, medical product developers use real-world data and evidence to design clinical trials and observational studies with the goal of creating new treatment approaches. RWE can also be used to monitor post-market safety and adverse events.
RWD can add value throughout the clinical trial process by increasing efficiencies and reducing time to market for potentially lifesaving treatments.
“Those customers are seeing gains across all phases of the clinical trial process, including reduced drug development timelines, simplified regulatory complexity and more holistic patient views for better insights,” says McCurdy. “In drug discovery, the analysis of RWD uncovers vital information into drug performance and safety, so it provides an important feedback loop into the drug development R&D teams. The same is true in post-approval, where RWD is used to ensure industry compliance and identify any adverse events.”
Eagle says clinical trials are a sample of data from the real world that control for many differences to assess the impact of one variable. RWD can’t replace clinical trials, but it can speed up researchers’ ability to ask the right questions in a trial. They can turn to the real-world evidence to determine what is happening and take the steps to determine why.
This potential research approach can only enable these determinations when the data sets have the right kind of information.
“My biggest tip for building and launching into real-world evidence is to find the database that covers the parameters or variables that are to be analyzed rather than building a database and then realizing later that the data sets are missing important data,” says Eagle.
What Is the Future of Real-World Data in Healthcare?
With investments in AI and ML, the healthcare and life sciences industries are now witnessing the democratization of genomics, and “multi-omics” is becoming the new norm for better understanding the body, how it reacts and how to best treat it versus treating a disease or population, says McCurdy.
He adds that by using cloud technologies and natural language processing, healthcare organizations can pair ML with data interoperability to help uncover new ways to enhance patient care, improve outcomes and save lives while also driving operational efficiency to help lower the overall cost of care.
As the field of data analytics and AI evolves in healthcare, Eagle says the ability to access real-time or nearly real-time data and analyses will increase, making the industry more agile.