Multiple factors potentially influence pain intensity or frequency, and consequently the need for an opioid prescription. This study aims to identify factors associated with being discharged with an outpatient opioid prescription. We constructed a database containing clinical, non-clinical, and organizational variables from the EHR that are potentially relevant for ordering an opioid at discharge. Descriptive statistics of these variables and univariate association analysis reveal that all of the examined variables to be statistically significantly associated with opioid prescription at discharge. Further, we fitted a random forest model to examine the information content in the examined variables regarding whether a patient will be discharged with an opioid. The model resulted in a mean AUC of 0.84, suggesting the factors examined in this study in combination contain significant information regarding prescription of an opioid at discharge.
|Original language||English (US)|
|Number of pages||6|
|Journal||AMIA ... Annual Symposium proceedings. AMIA Symposium|
|State||Published - Jan 1 2018|