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Phrase Demo: Introducing, Analyzing, and Improving Clinical Decision Support
Follow along with our AMIA Annual Symposium presentation as we show how Phrase Health technology uses clinical analytics to improve the patient experience and clinician efficacy and efficiency in cases of kids admitted to the ER with severe symptoms of Asthma.

Phrase Demo: Introducing, Analyzing, and Improving Clinical Decision Support
When an asthmatic comes into the emergency room with severe symptoms, clinicians jump into action. What treatment they reach for matters and data suggests there may be better ways to improve the young patient’s outcome. Using intermittent doses of an albuterol inhaler appear to be just as effective (and faster and easier for the patient) than using a nebulizer with continuous albuterol.
Real world data matters more than research does, so follow along as we use the Phrase Health platform to inform both intervention (electronic clinical decision support) and analysis of resulting outcomes to improve the patient experience and clinician efficacy and efficiency in cases of kids admitted to the ER with severe asthma. These video clips are from our recent AMIA Annual Symposium presentation. If you'd like to watch the entire presentation, find it here.
Choosing the Cohort
Here, we define the cohort of the analysis, in this case emergency room clinicians.
Choosing the Outcome and Process Measures
Based on the model of a key driver diagram, here we choose outcome and process measures. This lets us analyze the adoption of our intervention as well as the association with downstream quality measures.
Adding Clinical Decision Support
Here we add the actual intervention itself. In this case, an order set that makes it easy for clinicians to opt for the treatment we’re hoping they’ll use.
Analyzing the Results
Here we analyze the results and determine next steps for our quality improvement project.
Follow along with our AMIA Annual Symposium presentation as we show how Phrase Health technology uses clinical analytics to improve the patient experience and clinician efficacy and efficiency in cases of kids admitted to the ER with severe symptoms of Asthma.
Written by
Phrase Health
Nov 2, 2021
Written by
Phrase Health
Nov 2, 2021
When an asthmatic comes into the emergency room with severe symptoms, clinicians jump into action. What treatment they reach for matters and data suggests there may be better ways to improve the young patient’s outcome. Using intermittent doses of an albuterol inhaler appear to be just as effective (and faster and easier for the patient) than using a nebulizer with continuous albuterol.
Real world data matters more than research does, so follow along as we use the Phrase Health platform to inform both intervention (electronic clinical decision support) and analysis of resulting outcomes to improve the patient experience and clinician efficacy and efficiency in cases of kids admitted to the ER with severe asthma. These video clips are from our recent AMIA Annual Symposium presentation. If you'd like to watch the entire presentation, find it here.
Choosing the Cohort
Here, we define the cohort of the analysis, in this case emergency room clinicians.
Choosing the Outcome and Process Measures
Based on the model of a key driver diagram, here we choose outcome and process measures. This lets us analyze the adoption of our intervention as well as the association with downstream quality measures.
Adding Clinical Decision Support
Here we add the actual intervention itself. In this case, an order set that makes it easy for clinicians to opt for the treatment we’re hoping they’ll use.
Analyzing the Results
Here we analyze the results and determine next steps for our quality improvement project.