The foundation of all of 3M’s healthcare analytics is a claims-based dataset designed to deliver metrics you can put into action. It begins with our meticulous data intake and cleansing process. Then, data is risk-adjusted, grouped and calculated to provide information on cost, quality, access and value.
Although cost is a common topic in health care, it is not commonly understood. As one man’s trash is another man’s treasure, similarly, in health care, one man’s cost is another man’s gain. Costs to a payer become revenue for a care provider, as do the co-payments and other shared costs paid by consumers. The perception of “cost” depends entirely on who is paying. Not only that, the definition of cost depends on who is measuring. And everyone measures it differently.
One reader of my latest blog on segmenting health care consumers asked me if I knew of any tools to calculate a person’s chance of developing a particular disease. That question got me thinking again about the topic of risk in health and disease. I pulled a copy of John Last’s Dictionary of Epidemiology from my office bookshelf for a proper epidemiologist’s definition of risk.
As the healthcare industry strives to converge all the data sources required to manage population health, the mass of data needed to do it well, and to both clinically and analytically inform, will require something of a science project. Let’s call it gravity. Dave McCrory first proposed the idea of “Data Gravity” back in 2010. The analogy is rather simple.
The 3M Intelligent Data Asset is a claims-based dataset that provides the foundation for value-based care programs. It takes raw claims data and turns it into information that can help optimize both cost and quality.