Healthcare data is increasing in volume and complexity, while organizations’ internal demands for digital transformation, lowering costs, and improving quality have increased exponentially.
Healthcare organizations (looking to optimize the value of their data and their desire for agency over their own data assets) realize that this volume and complexity has outpaced the ability of their existing technology to meet even the most basic demands of data: acquisition, storage, integration, validation, and analysis.
The good news is that these basic tenants are somewhat being met by a number of vendors (many with little or no practical healthcare data experience) touting modern data architecture’s that promote self-enabling data initiatives.
The bad news is that these vendors are not truly enabling data-driven organization to be self-sufficient. They tend to fail to address the main barriers to success, where organizations need to manually map and transform data into custom data sets for every use case, while being cognizant of the ever changing and complicated: standards, regulations, versions, and format, along with the multitude of ontologies and terminologies that are often unique to each hospital and invariably tend to vary across and within the health system itself.
Refactor Health’s platform addresses the true limiting factors to enable an AI-assisted learning ecosystem for data discovery and advanced analytics at scale. Refactor Health offers an industry battle-tested, low cost, and scalable solution that reduces manual data preparation (expensive and error-prone) to provide early success and drive support for future projects, and vastly reduces the exorbitant project costs while solving for the lack of internal skilled and/or scarce manpower.
Refactor Health: The first augmented data discovery platform for healthcare o Real-time, standardized, and integrated healthcare data, beyond the EHR AI-driven data mapping, cleansing, and validation — the entire data preparation lifecycle o Blockchain-based governance and data validation (regulatory-grade) Open standards for deployment of AI and analytic tools o Organizational agency of valuable data assets o On-premise, cloud, or hybrid installation
