There are three main components of data management that can be applied to healthcare facilities. These are known as data collection, data sharing, and data analytics. The reality of data collection and sharing is that these practices have yet to have any meaningful impact on the quality and cost of healthcare services.
Healthcare analytics, on the other hand, is showing immense promise when it comes to making system-wide improvements. One framework for thinking about data analytics is known as the health analytics adoption model. It has been developed by healthcare industry veterans to provide a successful and sustainable analytics strategy, but not all industry professionals are aware of this model and how it works.
The Levels of the Analytics Adoption Model
There are eight different levels to the healthcare analytics adoption model. These begin with personalized medicine and prescriptive analysis, which pertains primarily to personalized medicine and managing health Level seven uses predictive analytics to manage clinical risk intervention, while level six uses suggestive analytics to create a data-driven approach to population health management.
Level five of this analytics adoption model pertains to clinical effectiveness and accountable care, helping to promote waste and care variability reduction. Level four implements automated external reporting to encourage efficient and industry-standardized production of regulatory reports, while level three provides a similar framework for developing automated internal reporting.
Level two of the analytics adoption model relates to developing standardized vocabulary and patient registries for better data governance and consistency with local standards. Level one implements the creation of an enterprise data warehouse. Facilities at level zero are producing data that portrays an inefficient, fragmented view of their operations that does not offer usable data.
Products and Applications that Help
There is a wide range of products and services that fit the various levels of this framework, making it easier for facilities to progress in their data analytics journeys. It’s important to choose applications that are both optimized for facilities’ current levels of data analytics and scalable enough to continue meeting their analytics needs in the future. The first step toward better data governance is for healthcare facilities to perform self-assessments to generate reports regarding their current data collection, sharing, and analytics practices, though, so get started today.