There is no escaping the fact that we are in the midst of a healthcare data explosion. While the exact amount of healthcare data being generated is unknown, it is estimated that the healthcare industry generates 30% of the world’s data volume, and currently, there are approximately 2000 Exabytes of healthcare data. By 2025, the expected compound annual growth rate for healthcare data will be 36% (Frost and Sullivan), and healthcare will become the fastest-growing source of data worldwide (IDC). Not only are there more data sources, but there are also more types of data and machine learning (ML) and artificial intelligence (AI) are required to make healthcare data Findable, Accessible, Interoperable, and Reusable (FAIR) to answer new and different types of questions.
Life sciences and Healthcare organizations have enormous potential to develop new therapies and improve patient outcomes by harnessing data; however, to date, the ability to leverage all this data has been fraught. Problems like low quality, unreliable data sources, difficulty integrating the data, and technology not built to store and examine huge amounts of non-relational information are slowing progress.
We are at an inflection point where the promise of big data, Artificial Intelligence (AI), and Machine Learning (ML) are already enabling new discoveries. The opportunities are enormous.
So, how do you take advantage of these opportunities right now?
Cloud technologies that enable large amounts of data to be accessed and analyzed cost-effectively, efficiently, and securely are essential tools in today’s data-driven environment. According to the Healthcare Information and Management Systems Society analytics survey report, over 83% of pharma companies already leverage cloud services. Data technology platforms, like Kythera Labs’ Wayfinder, make effective use of healthcare Real World Data (RWD) possible by managing and transforming RWD data into insights. Wayfinder includes end-to-end data management, ingestion, cleansing, and processing of various data formats, as well as delivering data science tools and critical functions like de-identification of Private Health Information (PHI/PII). With the speed of AI adoption by Healthcare and Life Sciences, having the right technology, like a lakehouse architecture and data governance tools are needed to make finding answers easier, faster, and more cost-effective.
Siloed data and difficulty in integrating different data formats are pervasive problems in the Life Sciences and Healthcare industries. Business units and therapeutic areas within the same organization often have their own data used only for their specific purpose. These silos restrict the use of data across the enterprise and result in data, technology, and cost redundancies.
Additionally, widely varied data formats complicate data integration and limit the power of RWD. A common data model breaks down these silos, brings together data from different sources, simplifies data complexity, and organizes enormous datasets to make integration and utilization possible. Kythera Labs has developed a dynamic common data model that not only standardizes the data it also works like a feedback loop that self-informs and improves the data by interacting with hundreds of data points. Our model enables users to get more value from the diverse range of data sources and types, and our Wayfinder platform facilitates data collaboration and reuse of data assets across an organization.
High-quality data is a requirement for Life Sciences and Healthcare organizations. Research and development, identification of patients for clinical research recruitment, and new product development all rely on high-fidelity data. As the use cases for ML and AI multiply, high-fidelity data is the backbone and foundation of having confidence in the answers derived. According to Databricks’s own data of over 9,000 global companies, 411% more ML models are now in production than at the same point in 2022. Their data show that there is concern around these initiatives and that data problems are nearly 3x more likely to jeopardize an organization’s AI/ML goals than everything else combined.
Kythera Labs’ data science enhances data quality through a medallion architecture. Our architecture and processing technology create specific, progressive improvements to large volumes of healthcare RWD as it moves through the Bronze, Silver, and Gold layers. We restructure and reformat data to produce data assets with consistent and unified structures, and we remove incorrect information, impute correct information, and infer the existence of missing healthcare events to increase confidence that our data is correct and complete. Life Sciences and Healthcare Provider organizations can take advantage of our improvements in the Bronze, Silver, and Gold layers, including data cleaning, standardizing, de-identifying, uplifting, curating, and joining functionality to integrate data and analyze robust, holistic, and unique datasets at scale.
Life Sciences organizations and the US Healthcare system increasingly rely on high-quality RWD and will require technology platforms that can process big data with different formats from many sources. As organizations break down data silos, having the tools to integrate and share data are also essential. Want to learn more about how Kythera Labs’ dynamic common data model and Wayfinder platform help clients get to work faster? Get in touch or connect with me at LinkedIn.
Enhancing the accuracy and utility of pricing transparency data with claims data and a powerful data analysis platform.
Machine learning and AI can make the healthcare data explosion Findable, Accessible, Interoperable, and Reusable (FAIR).