Better big data

Abstract

By 2018, Medicare payments will be tied to quality of care. The Centers for Medicare and Medicaid Services currently use quality-based metric for some reimbursements through their different programs. Existing and future quality metrics will rely on risk adjustment to avoid unfairly punishing those who see the sickest, highest-risk patients. Despite the limitations of the data used for risk adjustment, there are potential solutions to improve the accuracy of these codes by calibrating data by merging databases and compiling information collected for multiple reporting programs to improve accuracy. In addition, healthcare staff should be informed about the importance of risk adjustment for quality of care assessment and reimbursement. As the number of encounters tied to value-based reimbursements increases in inpatient and outpatient care, coupled with accurate data collection and utilization, the methods used for risk adjustment could be expanded to better account for differences in the care delivered in diverse settings.

Financial & competing interests disclosure

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

No writing assistance was utilized in the production of this manuscript.