Big data to assess CMS quality measures
GNS officials say the firm will deploy its REFS, or Reverse Engineering and Forward Simulation, its big data analytics and machine learning platform, to help determine the impact that quality measures have on the quality of care delivered to patients.
CMS quality measures are collected and reported with the goal of improving patient care. Since the agency has begun to link these measures to value-based incentive payments for providers, it is crucial to gauge their impact.
CMS currently produces a triennial impact report with an assessment of the measures, but GNS says it will now substantially advance the process by using REFS, in tandem with cloud-based supercomputing, to analyze the causal relationships between measures and outcomes.
GNS will link data generated by provider reports of quality measures with real world patient outcome data, officials say. Using a high-throughput, data-driven computational approach, REFS will perform trillions of calculations to identify causal and predictive relationships between measures and outcomes, explore links between measures, define important patient subpopulations and identify gaps where new measures are needed to determine the quality of patient care.
"This collaboration is a perfect example of how REFS can use real world outcomes data to determine how to provide high quality healthcare across conditions, settings and populations," said Carol McCall, chief strategy officer at GNS, in a statement.
"Evaluating these quality measures is a significant challenge, given the size and complexity of the data," she added. "However, this is a very important challenge. REFS can support CMS by addressing this complexity, in order to create quality standards that have a meaningful impact on patient care.
[See also: Quality measures 'need refinement']