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Exploring Different Approaches in Measuring EHR-based Adherence to Best Practice - A Case Study with Order Sets and
Nathan C Hulse1,2, Jaehoon Lee1,2, José Benuzillo1
1Intermountain Healthcare, Salt Lake City, UT.
AMIA ... Annual Symposium Proceedings. AMIA Symposium
|April 21, 2020
Summary
Measuring adherence to electronic health record order sets is crucial for standardizing care. Granular, order-level analysis reveals more significant clinical outcome differences than higher-level adherence metrics.
Area of Science:
- Health Informatics
- Clinical Quality Improvement
- Healthcare Management
Background:
- Electronic health record (EHR) systems are increasingly adopted, necessitating standardized clinical practices.
- Best-practice order sets are developed to standardize care and improve patient outcomes within EHRs.
- Measuring adherence to these order sets is vital but lacks well-defined methodologies in medical literature.
Purpose of the Study:
- To describe initial methods for measuring compliance with best-practice order sets.
- To compare clinical outcomes based on different adherence measurement models.
- To evaluate the impact of granular versus high-level adherence metrics.
Main Methods:
- Content utilization analysis of order sets.
- Development of a calculated adherence model.
- Identification of relevant clinical key performance indicators (KPIs).
- Comparison of clinical outcomes for coronary artery bypass graft (CABG) and heart failure (HF) patients.
Main Results:
- Higher-level adherence surrogates showed uncertainty in outcome patterns.
- Granular, order-level analysis revealed more significant differences in clinical outcomes.
- Variations in outcomes were more pronounced when examining specific order details.
Conclusions:
- Accurate measurement of adherence to clinical guidelines via EHR order sets requires detailed analysis.
- Order-level adherence metrics provide a more sensitive indicator of clinical outcome variations than broader measures.
- Further research into granular adherence measurement can refine quality improvement initiatives.
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