Better big data. (2nd November 2015)

• Record Type: Journal Article
• Authors: Al Kazzi, Elie S; Hutfless, Susan
• Is Part Of: Expert review of pharmacoeconomics & outcomes research. Volume 15:Number 6(2015)
• Page Start: 873
• Page End: 876
• Publication Date: 2015-11-02
• Subjects: Pharmacology -- Economic aspects -- Periodicals; 338.47615105
• DOI: 10.1586/14737167.2015.1083864
• Languages: English
• ISSNs: 1473-7167

Abstract

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.