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From simply inaccurate to complex and inaccurate: complexity in standards-based quality measures
David A Dorr1, Aaron M Cohen, Marsha Pierre-Jacques Williams
1DMICE, OHSU, Portland, OR, USA.
AMIA ... Annual Symposium Proceedings. AMIA Symposium
|December 24, 2011
Summary
Health care quality measures are becoming more complex, hindering implementation. Increased complexity, measured by concept identifiers and taxonomies, correlates with implementation challenges and perceived difficulty.
Area of Science:
- Health Informatics
- Health Services Research
Background:
- Quality measurement in healthcare has historically faced challenges impacting outcomes.
- Incentive programs like Meaningful Use aim to improve measures but may increase complexity.
Purpose of the Study:
- To evaluate the complexity and perceived difficulty of measures selected for the Meaningful Use program.
- To assess the relationship between measure complexity and implementation success.
Main Methods:
- Quantitative analysis of unique concept identifiers, taxonomies, and aggregated concepts within 45 Meaningful Use measures.
- Survey of informatics professionals to gauge the perceived difficulty of implementing these measures.
Main Results:
- The 45 measures contained 20,316 unique concept identifiers, 35 taxonomies, and 317 aggregated concepts.
- Half of surveyed informatics professionals found the measures at least moderately difficult.
- A higher number of concept identifiers was associated with fewer implementations (r=-.37).
- Perceived difficulty correlated with a greater number of taxonomies (r=.24).
Conclusions:
- Increasing complexity in healthcare quality measures, particularly with more taxonomies and concept identifiers, poses significant implementation challenges.
- Future measures, while aiming for clinical relevance, may face substantial accuracy impacts due to complexity and reliance on unstructured data.
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