An Evaluation of Clinical Decision Support and Use of Machine Learning to Reduce Alert Fatigue

Abstract

Therapeutic duplication alert is one of the Clinical Decision Support Systems (CDSS) that was implemented to help physicians and other healthcare providers in making clinical judgements about the patients’ management of therapy and decreasing medication errors. However, there were high override rates of these alerts by physicians as they were deemed to be of non-clinical significance. The quantity of the alerts fired by the system was high leading to “alert fatigue”. Thus, the hospital administrators reached an agreement to deactivate it. To assess the validity of this decision, the aim of the study was to analyze the impact of therapeutic duplication alert deactivation on medication errors’ rate. This study retrospectively screened a total of 593 electronic Medication Administration Records (eMAR) of hospitalized patients with 297 e-MARs in the pre-therapeutic duplication alert deactivation period and 296 e-MARs in the post-therapeutic duplication alert deactivation period in a tertiary care hospital in Saudi Arabia. The number and type of duplicate medication errors in each period was documented to determine whether there was a significant difference between the two periods. The results detected 51 out of 297 e-MARs with medication errors in the pre-therapeutic duplication alert deactivation period versus 47 out of 296 in the post alert deactivation therapeutic duplication. Chi square test showed that there was no significant difference in the incidence of medication errors detected among the two periods with a p-value of 0.672. Therefore, we concluded that there was no significant difference on the medication error after the therapeutic duplication alert deactivation. An integration of machine learning into the clinical decision support design was recommended to filter the duplicated and unimportant alerts and reduce the alert fatigue of physicians.