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A Measurement Science Framework to Optimize CDS for Opioid Use Disorder Treatment in the ED
Mark Samuel Iscoe1,2, Carolina Diniz Hooper1, Deborah R Levy2,3
1Department of Emergency Medicine, Yale University School of Medicine, New Haven, Connecticut, United States.
Developing clinical decision support (CDS) measures using a co-design approach is feasible for improving opioid use disorder treatment. This framework can guide the evaluation and enhancement of CDS tools in emergency departments.
Area of Science:
- Health Informatics
- Clinical Decision Support Systems
- Opioid Use Disorder Treatment
Background:
- The EMergency department-initiated BuprenorphinE for opioid use Disorder (EMBED) trial found no effect of a clinical decision support (CDS) tool on buprenorphine initiation rates.
- The Agency for Healthcare Research and Quality (AHRQ) developed a CDS Performance Measure Inventory to guide CDS development and evaluation.
- Tailoring AHRQ measures through partner co-design is crucial for evaluating and improving CDS tools like EMBED.
Purpose of the Study:
- To adapt AHRQ inventory measures for evaluating the performance of the EMBED CDS tool.
- To utilize a partner co-design approach to drive improvements in CDS for opioid use disorder treatment.
- To establish a measurement science framework for CDS development and evaluation.
Main Methods:
- Selected and adapted relevant AHRQ inventory measures using a consensus-driven, partner co-design approach.
- Conducted three iterative, multidisciplinary working group sessions with diverse stakeholders.
- Evaluated the developed measures in three emergency departments within a single health system.
Main Results:
- A 25-member partner working group narrowed 13 candidate metrics to 6 priority categories during conceptualization.
- Validated measures included: eligible encounters with CDS engagement (5.0%), teamwork on buprenorphine initiation (39.9%), and proportion of eligible users engaging with EMBED (58.3%).
- Preliminary values for EMBED usage: time spent (29.0 seconds), proportion of orders via EMBED (6.5%), and task completion (13.8%).
Conclusions:
- A measurement science framework, informed by partner co-design, proved feasible for developing measures to guide CDS improvement.
- This co-design approach can be adapted to evaluate other CDS applications.
- Future research should focus on refining and implementing these measures to optimize CDS effectiveness.
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