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Healthcare payers, providers, and other value-based care organizations face challenging questions as they work to improve treatment performance and cost. How healthy are patient cohorts and panels in a particular market? What treatments are the most effective for the most prevalent conditions in a population? How widespread are preventative screenings and vaccinations?
Answering these questions requires access to the most significant tool in a healthcare decision maker’s arsenal: claims data. But that data can be incredibly difficult to utilize for effective decision-making thanks to two issues: its volume, and its quality.
A joint Dell EMC and IDC study recently estimated the total volume of global healthcare data to exceed 2,314 exabytes. That’s more than 23 quadrillion terabytes and should give some sense of the scale and challenge that healthcare data analysts face: it’s not easy to make data-driven healthcare decisions when you’re drowning in exabytes. Further, so much of that data is of poor quality.
Numerous studies posted by the National Institute of Health highlight the data quality challenges healthcare analysts face, and yet pressures to make decisions on this data are only increasing thanks to the rise of value-based care. Spurred by patients, governments, and investors, payers seeking data-driven methods to reduce healthcare costs are constantly seeking ways to improve healthcare data quality in order to make the most informed care decisions.
There are three recommended criteria you should use to evaluate healthcare data quality: breadth, depth, and trustworthiness. We’ll examine these elements below, and share how they can be used to make your data more actionable.