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Solutions for filling gaps in accountable care measure sets
Tom Valuck1, Donna Dugan, Robert W Dubois
11120 North Charles St, Baltimore, MD, 21201.
Objectives:
A primary objective of accountable care is to support providers in reforming care to improve outcomes and lower costs. Gaps in accountable care measure sets may cause missed opportunities for improvement and missed signals of problems in care. Measures to balance financial incentives may be particularly important for high-cost conditions or specialty treatments. This study explored gaps in measure sets for specific conditions and offers strategies for more comprehensive measurement that do not necessarily require more measures.
Study Design:
A descriptive analysis of measure gaps in accountable care programs and proposed solutions for filling the gaps.
Methods:
We analyzed gaps in 2 accountable care organization measure sets for 20 high-priority clinical conditions by comparing the measures in those sets with clinical guidelines and assessing the use of outcome measures. Where we identified gaps, we looked for existing measures to address the gaps. Gaps not addressed by existing measures were considered areas for measure development or measurement strategy refinement.
Results:
We found measure gaps across all 20 conditions, including those conditions that are commonly addressed in current measure sets. In addition, we found many gaps that could not be filled by existing measures. Results across all 20 conditions informed recommendations for measure set improvement.
Conclusions:
Addressing all gaps in accountable care measure sets with more of the same types of measures and approaches to measurement would require an impractical number of measures and would miss the opportunity to use better measures and innovative approaches. Strategies for effectively filling measure gaps include using preferred measure types such as cross-cutting, outcome, and patient-reported measures. Program implementers should also apply new approaches to measurement, including layered and modular models.
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